Evaluating Drug-Induced Cardiovascular Disease: A ... - ACCP

Evaluating Drug-Induced Cardiovascular Disease: A Pharmacoepidemiologic Perspective By Brian J. Quilliam, Ph.D., RPh; and Marilyn M. Barbour, Pharm.D., FCCP, BCPS Reviewed by Cynthia Jackevicius, Pharm.D., MSc, FCSHP, BCPS (AQ Cardiology); and Judy W.M. Cheng, Pharm.D., MPH, FCCP, BCPS (AQ Cardiology)

Learning Objectives

attributed to cardiovascular drugs. Although many cardiac agents can produce adverse cardiovascular events, through extension of their beneficial effect or otherwise, noncardiac drugs can also cause unintended cardiovascular outcomes. For several years, considerable attention has been given to cyclooxygenase II inhibitors, thiazolidinediones (TZDs), stimulant drugs, and other drugs because of their ability to cause previously unrecognized cardiovascular outcomes. Despite the preponderance of information regarding adverse cardiovascular events in the medical literature, the timeline from initial case reports to definitive evidence of drug-induced cardiovascular events can be long. For example, initial case reports linking phenylpropanolamine to stroke appeared in the 1980s, yet definitive studies were not conducted until almost 20 years later. As the scientific process unfolds, patients may unnecessarily continue to be at risk of adverse cardiovascular events as the medical community awaits definitive guidance from the U.S. Food and Drug Administration (FDA), national guidelines, or the drug manufacturer. A thorough understanding of the strengths and limitations of the evidence available at each stage of the investigation can allow pharmacists to more appropriately weigh the risk-to-benefit ratio associated with an individual’s exposure to a particular drug and guide therapeutic decisions accordingly. A general understanding of the pharmacoepidemiologic methods used to identify adverse events and a framework for applying the findings emanating from these methods to everyday clinical practice are essential.

1. Distinguish among available pharmacovigilance methodologies used in adverse event surveillance. 2. Evaluate the strengths of pharmacovigilance methodology to assess possible adverse events from cardiovascular drugs. 3. Estimate adverse event detection limits in pharmacoepidemiologic studies of cardiovascular drugs through application of knowledge regarding study design, sample size, and participant enrollment. 4. Demonstrate an in-depth practical understanding of the merits and shortcomings of common pharmacoepidemiologic study designs used to assess adverse events identified through pharmacovigilance. 5. Assess a recent example of drug-induced cardiovascular disease in light of the study designs used in their discovery and form an educated opinion, considering all available evidence.

Introduction Epidemiology of Adverse Events Adverse drug reactions are common and remain a significant source of morbidity and mortality. These events contribute significantly to health care costs, costing more than $175 billion in the United States annually. A recent comprehensive meta-analysis estimated that 5.3% of hospital admissions were the result of an adverse drug reaction, with 45.7% of adult hospitalizations for an adverse event

Baseline Review Resources The goal of PSAP is to provide only the most recent (past 3–5 years) information or topics. Chapters do not provide an overall review. Suggested resources for background information on this topic include: • Aschengrau A, Seage GR, eds. Essentials of Epidemiology in Public Health, 2nd ed. New York: Jones and Bartlett Publishers, 2008:chaps 6–9. • Harmark L, vanGrootheest AC. Pharmacovigilance: methods, recent developments and future perspectives. Eur J Clin Pharmacol 2008;64:743–52.

PSAP-VII • Cardiology

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Evaluating Drug-Induced Cardiovascular Disease

be detected in a trial of at least 30,000 participants. This general rule highlights that a typical trial of 3000–5000 participants can detect an effect occurring in 1 in 1000 to 1 in 1650 participants. However, trials are typically powered to detect the primary end point, not secondary safety end points such as less common adverse drug reactions. As a result, inadequate participant enrollment, leading to decreased statistical power, may be of particular concern for secondary end points. Even with adequate enrollment, the studied population may not contain enough participants from subpopulations at risk of the adverse event. For example, the possible association between atypical antipsychotics and lipid abnormalities was not identified until after marketing, partly because of the limited inclusion of patients at high risk of cardiovascular disease independent of drug exposure. This recent example highlights the need for additional postmarketing tools to identify relatively rare adverse events or those occurring in subpopulations. Pharmacovigilance focuses on identifying, elucidating, and preventing negative effects associated with drugs both pre- and postmarketing.

Abbreviations in This Chapter AERS LST MI RCT RRR SRS TZD VAERS

Adverse Event Reporting System Large, simple trial Myocardial infarction Randomized clinical trial Relative reporting ratio Spontaneous reporting system Thiazolidinedione Vaccine Adverse Event Reporting System

Necessity of Postmarketing Evaluation of Safety Partly because of unforeseen negative effects from newly approved drugs, there is mounting concern that the marketing practices of a drug manufacturer may result in rapid uptake, often before the safety profile in the general public has been established. For example, postmarketing identification of an association between rofecoxib and cardiovascular events highlighted a major public health concern. The serious, unrecognized adverse events of myocardial infarction (MI) and stroke, although relatively rare, resulted in a large absolute number of events because of the widespread use of this drug (more than 80 million people worldwide) despite rigorous premarketing testing for safety and efficacy. Premarketing clinical trials are often conducted in homogeneous populations with a limited number of participants. Furthermore, they can take place over short periods, depending on the required outcomes for FDA label approval. For example, the first multicenter premarketing trial of pioglitazone enrolled 408 patients and observed them for a maximum of 26 weeks on either pioglitazone monotherapy or placebo. The average age of participants was 54, 78% were white, almost 60% were men, and only 13% had taken two or more antidiabetic drugs before randomization. Patients with impaired liver function or kidney disease were excluded. Although homogeneity in clinical trials helps control for errors, it often precludes the evaluation of beneficial and adverse events in real-world populations. For these and other reasons, rare adverse events or events that occur in subsets of the population may not be detected during premarketing clinical trials. Thus, the true safety profile of a drug agent is often only partially known at the time of marketing. As exemplified by the rofecoxib example, even rare but serious adverse events can have devastating consequences when a large number of people, including populations in which the drug was not studied, take the agent. Identifying adverse events, particularly those that are rare or that occur in certain segments of the population, may be difficult before marketing a drug. Probability theory suggests that an adverse event occurring in 1/n participants requires 3n participants to detect that effect with 90% probability. Thus, an event occurring in 1 in 10,000 could


Pharmacoepidemiology Clinical trials are considered the gold standard for identifying beneficial effects of drugs. The utility of trials to study adverse events of drugs is limited, particularly because of the high costs of conducting clinical trials and ethical concerns about randomizing patients to a drug suspected to be harmful. Pharmacoepidemiology, a unique discipline with roots in both epidemiology and pharmacology, permits the study of drug effects, both beneficial and adverse, in human populations. Although the discipline itself is not new, the field of pharmacoepidemiology is rapidly evolving, and its methods are often used when assessing adverse effects attributed to drugs. This chapter focuses on postmarketing surveillance and highlights basic pharmacovigilance and pharmacoepidemiologic methods used to identify adverse events associated with drugs. The chapter includes a recent practical example relevant to pharmacists, the possible role of TZDs in MI.

Pharmacovigilance History of Pharmacovigilance In the United States, regulatory requirements for drug safety have largely been shaped in response to catastrophic events. Of note in this evolution were the deaths of more than 100 children in the 1930s after taking sulfanilamide that contained ethylene glycol. In the 1950s, chloramphenicol-induced aplastic anemia prompted the American Medical Association to create an adverse event reporting system. In the 1960s , the teratogenic effects of thalidomide, both in the United States and abroad, resulted in the passage of the Kefauver-Harris Amendment, requiring that both safety and efficacy data be submitted to the FDA before 226


Spontaneous Reporting Systems In the United States, the FDA collects information on suspected adverse drug events through an SRS, the MedWatch system. Reports collected from MedWatch, together with mandatory adverse event reporting required of drug manufacturers, are compiled into two large databases: (1) the Adverse Event Reporting System (AERS); and (2) the Vaccine Adverse Event Reporting System (VAERS). Other countries and the World Health Organization (WHO) have similar reporting systems, and most drug manufacturers have an SRS that they maintain internally. All of these sources of information can be used in the context of pharmacovigilance for data mining to identify drug and adverse event combinations occurring more frequently than expected.

new drug approval. Because of growing concern about the safety of marketed medical products, the FDA followed the American Medical Association’s lead and initiated a spontaneous reporting system (SRS) to identify adverse events potentially attributable to drugs. In these early years, pharmacovigilance mainly focused on postmarketing surveillance, with premarketing safety data originating from phase I–III clinical trials. More recent examples of unforeseen serious adverse cardiovascular events, including those resulting from rofecoxib and rosiglitazone, reinforce the need for more sensitive detection methods during both the pre- and postmarketing of a drug. During the past decade, the FDA, private companies, and academic researchers have strengthened pharmacovigilance with the discovery of new methodologies and the expanded application of existing methodologies to the early identification of possible drug-related adverse events. There has been a large investment by the federal government and the private sector to develop and refine methods for the early identification of potential adverse events. Because many of these methods were developed by individual entities and are tailored for specific needs, many different methods and systems are used in the United States. Regardless of system, pharmacovigilance, through the application of techniques such as data mining and signal detection, results in earlier and more sensitive detection of possible safety concerns occurring below the thresholds of standard premarketing trials.

AERS/VAERS Databases The FDA’s AERS database contains almost 3 million spontaneous reports, whereas the VAERS database contains around 200,000 spontaneous reports. Both databases compile reports from the MedWatch program, the SRS (the FDA’s predecessor before MedWatch was implemented), published case reports in the literature, and required report submission by drug manufacturers. Pharmaceutical companies are required to submit all spontaneous reports they receive that are either serious (i.e., life threatening/fatal, result in hospitalization or a prolonged length of stay, or result in a disability or congenital malformation) or unexpected (i.e., not listed in the package insert) within 15 days of receipt. This reporting requirement continues for the life of the product. In addition, drug manufacturers are required to submit all other reports (other than serious/unexpected ones) quarterly for the first 3 years a drug is marketed and annually thereafter. The AERS and VAERS databases are useful for adverse event surveillance, mostly because of their size and ability to capture drug and event combinations across all manufacturers and health care settings. Important limitations of these databases include a lack of standardized coding for drug names; and the lag time for reports submitted by a paper-based claim form to be entered in the database. Despite these limitations, the AERS and VAERS databases are the largest collection of spontaneous reports in the United States, which lends to their utility in data mining.

Pharmacovigilance Methodology Data Mining Early identification of probable adverse events is essential so that an appropriate risk management plan can be devised and implemented. Data mining is a promising approach for more rapidly identifying drug and adverse event combinations that require additional evaluation. In the context of pharmacovigilance, data mining is the process of applying individually designed algorithms to a data source to identify the potential adverse events associated with a drug. Data mining uses statistically driven techniques to identify drug–adverse event associations that appear more often than expected within the database, thus generating hypotheses that warrant further investigation. These associations can then undergo evaluation in their clinical context, and decisions can be made about the need for further assessment of these events. The mining process consolidates large amounts of data into manageable packets of information; this can lead to more rapid discovery of previously unrecognized adverse events and more efficient use of resources. The availability of large databases, several of which are maintained in the United States and internationally, is necesary for applying data mining techniques in pharmacovigilance.


WHO Safety Database The WHO maintains an international database containing in excess of 4.6 million spontaneous reports from 94 participating countries including the United States. The Uppsala Monitoring Centre in Sweden maintains this database. Within each member country, a designated National Center for Pharmacovigilance is responsible for submitting case reports to the Uppsala Monitoring Centre. However, unlike the U.S. system, the WHO Safety Database uses two standardized coding systems: one for coding reported adverse events, and one for coding suspected drugs. These 227


standardized coding systems ease the application of data mining techniques and allow comprehensive searches of the database. Countries submitting case reports to the Uppsala Monitoring Centre follow the WHO’s standardized format, making submissions uniform across countries and permitting international analyses. In addition, the size and the international coverage of the database make it attractive for use in pharmacovigilance. One major limitation of the database is the differences in reporting by the various participating countries. Because each country has its own requirements for spontaneous reporting (e.g., the 15-day reporting requirement in the United States), the degree of reporting varies by country. This differences in reporting should be considered when performing comprehensive international analyses as well as between-country comparisons.

report is critical, this process is time-consuming and may result in critical delays in the identification of serious adverse events. Even the most experienced clinician can only review a limited number of reports, and the interrater reliability of reviewers may be suboptimal. To remedy this, quantitative statistical methods have been developed that can bring spontaneous reports that exceed a predefined statistical threshold to the attention of clinicians. These quantitative methods assist clinicians by prescreening a large volume of spontaneous reports, allowing clinicians to focus their qualitative reviews on the reports occurring more often than expected. Signal Detection In general, signal detection is the identification of new adverse events or previously recognized events that are occurring more often than expected in phase I, II, and III clinical trials as well as during postmarketing surveillance. In the past decade, numerous quantitative methods have been developed to identify signals that can be prioritized for further review by clinical staff. These methods have been developed by government agencies, academic institutions, and drug manufacturers, and are often tailored to the developer’s needs. The most common type of quantitative signal detection uses disproportionality analysis; however, other types exist. Although many signal detection algorithms are based on the same theoretical concept (i.e., disproportionality analysis), several different statistical approaches are used in making this methodology operational, including the application of non-Bayesian and Bayesian statistical approaches. For example, the FDA and WHO monitoring programs use Bayesian approaches. Both Bayesian and non-Bayesian disproportionality analyses identify drug and event combinations that occur more often than expected, ignoring the clinical context in which the association is occurring. In non-Bayesian disproportionality analysis, the frequency of a reported drug and adverse event combination is assessed (i.e., observed occurrence of that combination), then divided by what would be expected for that combination under the assumption of independence. The observed count (n) is the number of reports linking that drug and adverse event contained in the database. The expected count is derived by multiplying three quantities: (1) the proportion of reports in the database mentioning the event (PE); (2) the proportion of events mentioning that drug in the database (PM); and (3) the total number of reports in the database (Ntotal). A relative reporting ratio (RRR) is then estimated by the following formula:

Drug Manufacturer Databases Most pharmaceutical companies maintain an internal spontaneous reporting database that can be used for data mining of adverse events. The spontaneous reports maintained in these databases are restricted to the products of that company and can be numerous, depending on the size of the company, its geographic representation, and the number of products marketed. International drug companies often have the advantage of databases populated with spontaneous reports from many countries; some may have reports from a drug that was available outside the United States before FDA label approval. In addition, some of these databases were established long before AERS/VAERS, and therefore contain older reports not found in the FDA databases. The limitations of these in-house databases are their relatively small size, their confinement to product areas of the company (e.g., cardiovascular drugs), and the lack of information on reports from drugs in the same class manufactured by others. A major concern of these databases is that their relatively small size may lead to masking or lack of detection of adverse effects with low incidence. Many of these databases can be used for preliminary analyses that can then be expanded within the larger FDA or WHO databases. Today, timely collection and inclusion of spontaneous reports into large databases is commonplace. Regardless of the database used, all spontaneous reporting databases lack information on the denominator or the actual number of people exposed to a particular agent. These databases are a rich source of information that can be screened for drug and adverse event combinations. Each year, SRSs receive hundreds of thousands of reports of adverse events; some require more in-depth assessment than others. Before the use of data mining in pharmacovigilance, the responsibility for prioritizing areas for further review was relegated to clinicians, who evaluated these reports in their clinical context, producing a qualitative assessment. Although the clinical context of a spontaneous Evaluating Drug-Induced Cardiovascular Disease

RRR =

n Ntotal × PM × PE

The RRR is then evaluated, and combinations meeting a particular predefined statistical threshold are flagged for 228


further evaluation. This predefined statistical threshold is set by the end user of the signal detection methodology, guided by the particular signal detection method used and the goals of the analysis. For example, take a hypothetical spontaneous reporting database of moderate size (containing 542,331 reports); 72 reports are submitted suggesting a link between an antihypertensive drug and liver failure. This particular agent was mentioned in 1032 reports, and liver failure was mentioned in 3438 reports within the database. Using a non-Bayesian approach to calculate an RRR: RRR =

or those caused by biases, including confounding by indication. Confounding by indication occurs when the reason for the person taking the drug is related to the outcome, thus biasing the association between that drug and the suspected adverse outcome. For example, TZDs were originally reserved for third-line therapy in the management of diabetes, a marker for disease severity. Having more advanced diabetes is also associated with worsening heart failure, making it difficult to disentangle the true effects of TZDs on the incidence of heart failure from the contribution of advanced diabetes. For these reasons, it is imperative that quantitative methods be used in conjunction with qualitative methods to ensure an efficient and timely identification of areas that may require further investigation. In some cases, quantitative methods may lack statistical power to identify drug and event combinations that are important but that occurred in only a few spontaneous reports submitted to the database. Even rare events (e.g., occurring in 1 in 100,000 individuals) can be important, particularly when a large number of people in the general population are taking the drug involved. Underreporting to SRSs is probable because of underrecognition of adverse events in the clinical setting as well as clinical inertia. These points should also be considered in the qualitative evaluation of quantitatively identified signals. However, it is unlikely that these reports would be identified and flagged by a clinical reviewer. Despite the wealth of information contained in SRSs, increases in reporting can be artificially stimulated by media publicity of a particular adverse event or the legal system’s encouragement of patients to file reports of a suspected adverse event as a means of gathering documentation that can later be used in legal proceedings.

72 542,331 × (1032 / 542,331) × (3438 / 542,331)

This results in an RRR of 11.0; this would then be evaluated in comparison with the a priori threshold used by the company for signal detection. Bayesian approaches are also widely used within pharmacovigilance, particularly when the RRR is based on a limited number of events, potentially leading to undue influences of the RRR by only a few reports. Bayesian approaches involve the application of prior information, typically based on existing data in the SRS. This prior information is then used to create a new statistical distribution, referred to as the posterior distribution, which can be used to calculate the RRR. Unlike the non-Bayesian approach, Bayesian statistics involve the application of complex statistical distributions in the estimation of the prior distribution, often derived through mathematic modeling. The incorporation of the prior distribution into the estimation of the posterior distribution accounts for the level of certainty based on existing available information. Bayesian inference is a widely growing field, incorporated into many health care studies. The FDA uses the Multi-item Gamma Poisson Shrinker, and the WHO international monitoring program uses the Bayesian Confidence Propagation Neural Network. Although these methods vary, the end result is the same: Bayesian disproportionality signal detection methods can flag drug and adverse event combinations for consideration. As noted, all quantitative signal detection methods are guided by statistical thresholds and do not consider the clinical framework in which the association occurred. Disproportionality analysis assumes that a causal relationship exists, ignoring altogether the other factors that may be contributing to or responsible for this relationship. For example, in the initial evaluations of TZDs as possible causes of heart failure, an important contributing factor was the elevated baseline risk of heart failure in patients with diabetes. Quantitative signal detection methods could not evaluate the possible contributing role of diabetes to the reported adverse outcome. Another important caveat of quantitative methods is that they can identify signals that are already well known PSAP-VII • Cardiology

Role of the Pharmacist in Spontaneous Reporting Where appropriate, the filing of spontaneous reports is a critical necessity for all clinicians. Furthermore, pharmacists should encourage their colleagues to report suspected adverse events. Although identification of adverse reactions in clinical practice is commonplace, the decision on when a spontaneous report should be filed is less clear and can be influenced by clinical inertia, time constraints, and the pharmacist’s own clinical judgment regarding the possible causal relationship between the drug and the outcome. Case reports to SRSs should be submitted when a drug or drugs are suspected of causing a previously unknown adverse event. A large number of case reports are submitted shortly after a drug is approved for marketing, with a trickle-down effect as the agent remains on the market longer, potentially caused by clinicians’ increased comfort with the drug after several years’ experience. The aforementioned instance of phenylpropanolamine-induced stroke is an important example of the necessity of long-term pharmacovigilance by clinicians. Despite years of experience with this drug, its causal role in stroke was unrecognized for almost 30 years. A clinical hunch can be an important avenue for 229


the exposed and unexposed groups. These imbalances can often be minimized through careful selection of an unexposed group or can be adjusted for analytically by statistical techniques including regression modeling. To their benefit, cohort studies typically provide a clearer visualization of the temporal effect between a drug and an adverse event because participants with the outcome of interest (i.e., a specific condition such as MI) are excluded at baseline by design. Furthermore, they allow the simultaneous assessment of multiple end points, possibly several adverse events. A major drawback of cohort studies, however, is their inefficiency in assessing rare outcomes, as may be the case with previously unrecognized adverse events. However, current administrative data sets permit the follow-up of a large number of people with ease, making cohort study designs more popular for studying adverse events.

stimulating discussion in the medical community, leading to hypothesis generation and, if warranted, more formal testing. Pharmacy as a profession has an important role in the identification of new adverse events. In summary, despite the limitations of SRSs, they continue to be a rich and important source of information on probable drug and adverse event combinations that warrant further investigation. With the volume of spontaneous reports, the application of quantitative and qualitative signal detection methods to data generated by SRSs is often complementary. Although the pharmacovigilance methods discussed are critical early steps in the detection of new adverse effects, hypothesized drug and adverse event combinations deemed important often require further evaluation through pharmacoepidemiologic studies.

Pharmacoepidemiology Possible causal associations between the drug and the event are more formally evaluated within pharmacoepidemiologic study designs. The most common designs used to assess adverse events identified during postmarketing surveillance are the cohort, case-control, and randomized large, simple trials (LSTs). Each of these study designs has both benefits and limitations, and it is important to recognize these when reviewing the results of a study quantifying the association between a drug and a hypothesized adverse event.

Case-control Studies Case-control studies use an observational study design that samples a group of individuals with a particular outcome (e.g., an adverse cardiovascular event) and then samples a similar group of individuals who did not have the outcome from the same population that gave rise to the cases. After sample identification, exposure history is assessed for the drug or drugs of interest, considering the hypothesized exposure window (i.e., acute, chronic, or intermittent exposure). In addition, other potentially contributing characteristics of the cases and controls (confounders) are either minimized through study design or measured and recorded so they can later be considered in the analysis. Case-control studies are useful for evaluating adverse events for three main reasons. First, they are efficient designs when the outcome is rare (as is probable with adverse events) because they identify eligible patients based on the outcome of interest and permit the identification of multiple contributing drugs simultaneously. Second, because all events typically occurred at study inception, they do not rely on long follow-up periods and can be performed in a relatively short time. This permits a rapid analysis, which may be critical for risk management strategies, particularly when an adverse event is serious. Finally, they are often less expensive than some of their prospective counterparts. However, because these are observational studies, they can be subject to biases including confounding by indication and sometimes an unclear temporal relationship between the drug and the adverse outcome. These and other limitations can be minimized by careful case and control selection and valid and accurate measurement of drug exposure. In the past 30 years, there has been heightened recognition of the usefulness of the case-control design for quantifying the effect of a drug on a previously unforeseen adverse event. This increased recognition is partially the result of improved sampling methodologies. For example, risk-set sampling allows better identification of controls

Cohort Studies Cohort studies identify an eligible population (in particular, excluding those with the outcome of interest at baseline) and then classify participants on the basis of their drug exposure. This typically creates two (or in some cases, more) groups of individuals: a group exposed to the drug of interest and an unexposed group (either exposed to a similar agent or not exposed at all). Investigators then assess the baseline comparability between these two groups and observe them for the development of the outcome during a predetermined time interval. Two main types of cohort studies exist, prospective and retrospective. In prospective cohort studies, the study typically defines exposure groups on study inception and then observes participants in the future for the development of the outcome. In retrospective studies, exposure is defined during some historical time interval, and patients are observed from that period forward until a later historical period. Regardless of the type of cohort used, these studies are becoming more popular for detection of adverse events because of the widespread availability of large administrative data sources. Retrospective cohort studies using secondary sources of data can be conducted quickly and relatively less expensively than other prospective study designs. They are also subject to biases, including confounding by indication, and differences in other baseline characteristics between


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10,000 people). Events occurring with chronic long-term exposure or after a long latency period are also problematic, because the trial may be too short to identify either type of event. In addition, randomized clinical trials (RCTs) can be expensive and precluded by ethical considerations because participants cannot be randomized to a drug hypothesized to cause harm. Contributing to the expense of traditional RCTs are participant incentives, site fees, and costs of intense monitoring and follow-up. Since the 1980s there has been a movement toward the use of LSTs, a fundamentally different approach to the typical RCT conducted during premarketing. Large, simple trials have been used extensively in Europe and are garnering support in the United States. Large, simple trials are recommended for evaluating treatments that have important public health impacts yet have a modest treatment effect, because even modest effects can have a significant impact when a large number of individuals have the condition of interest. Large, simple trials have less restrictive eligibility criteria, and participants are observed for a shorter time; they also can identify a treatment effect smaller than typically assessed in premarketing clinical trials. However, an important and fundamental assumption underlying the LST is that the effect will be uniform in the population tested and that no single segment of the population will benefit (or be harmed) differentially by the drug under study. Large, simple trials benefit from the balance achieved by randomization, but because of their lack of administrative complexity, they are able to monitor a larger number of participants. For example, the Third International Study of Infarct Survival assessed the use of thrombolytic agents after MI, studying more than 40,000 patients worldwide, a feat potentially impossible in a typical RCT. It remains unethical to randomize participants to a drug suspected of causing harm; therefore, the LST is still not practical for directly testing a hypothesized adverse effect. The utility of LSTs lies in their increased ability to detect rare adverse effects during the course of the trial; they also provide an additional large data source for data mining and signal detection after the trial’s conclusion, particularly for easily measurable adverse events (e.g., MI, all-cause mortality). However, lack of prior knowledge may preclude monitoring for specific adverse events. Thus, LSTs may still not be ideally suited for identifying adverse outcomes not suspected at the trial’s inception. As with any randomized trial, stringent safety monitoring for known and unknown adverse events is imperative, and the large sample size of LSTs can heighten the utility of signal detection methods. Current FDA regulatory requirements do not support the use of LSTs for collection of necessary premarketing clinical data; however, their usefulness for identifying moderate effects in more real-world populations, coupled with their potential to increase detection of serious but rare adverse effects (e.g., those occurring in less than 1% of the population), may soon increase their

that are similar to the cases. In addition, application of the propensity score, a modern statistical approach, allows more efficient control of confounding. In recent years, the nested case-control study has become a popular design in pharmacoepidemiologic studies of adverse drug events. This design situates a case-control study within a larger cohort, or eligible population. In essence, this design allows better enumeration of the source population (because the larger cohort population from which the cases and controls were sampled is well defined) and can be used to control for confounding by indication. For example, the larger cohort could be restricted to a group of individuals with similar characteristics (for instance, all people might have moderate to severe disease), which would limit the ability for disease severity to confound because all study participants have more advanced disease. Regardless of observational study design used to identify the association between a drug and an adverse event, clinicians must consider the strengths and limitations of a given study when deciding how it will be incorporated into their clinical practice. In particular, clinicians should identify important differences between study groups (cases and controls in the case-control design; exposed and unexposed in cohort designs) including (1) comparability in selection factors, focusing on differences in the study practices used to select patients to be included (or excluded) from each of the study groups; (2) similarity of demographics and comorbidities that can also affect the outcome under study; (3) consistency of study procedures (i.e., assessment of exposure, outcome, and confounding factors); and (4) the degree of loss to follow-up. In addition, clinicians should consider the threats to validity resulting from methods used to identify the exposure and outcome in the study. Have these measures been validated? Do they make sense in light of your own clinical judgment? Furthermore, consider whether the researcher’s determination of exposure and outcome status allowed visualization of a clear temporal relationship between exposure and outcome. It is also important to consider the strength of the association reported in the study. Larger effects (relative to smaller ones) are unlikely to be the result of residual or unaccounted-for biases, whereas smaller effects could be negated if additional biases were considered in either the design or the analysis of the study. After a critical review of these factors, clinicians can determine whether the findings apply based on similarities or differences between the studied population and the clinician’s own patient population. Large, Simple Trials Because the primary focus of many clinical trials is on efficacy, important safety information that can be derived from these studies is often overlooked; this typically involves adverse events. As previously discussed, because of their size and duration, clinical trials are not suited for identifying rare adverse events (e.g., events occurring in 1 in


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desirability in the United States. Of importance, although these trials are administratively less complex and have lower costs associated with follow-up and data collection overall, their large size often results in significant costs.

sible risk associated with these drugs in real-world patient populations is not available from these studies. Observational studies can provide important information on the possible association of TZDs with MI in patient populations more representative of the patients with diabetes encountered in routine clinical practice, and provide data on longer durations of exposure. A review of these observational studies allows pharmacists to evaluate the evidence from a pharmacoepidemiologic perspective and determine how the possible risk could influence their practice.

Evaluating the Risk of MI Associated with TZDs: An Example of the Role of Observational Studies The potential association of TZDs with MI and cardiovascular death is a practical example lending itself to this discussion. The two currently available TZDs in the United States, rosiglitazone and pioglitazone, were approved by the FDA in 1999. Within a few years of marketing, both were in the top 20 drugs prescribed annually in the United States, with sales in the billions of dollars. In June 2007, a meta-analysis suggested that rosiglitazone was associated with an increased risk of MI and cardiovascular death. This meta-analysis of 42 randomized trials estimated an increased risk of MI and a nonsignificant increase in cardiovascular death in rosiglitazone users compared with the control group. Unlike the previous investigation of the role of TZD in causing or exacerbating heart failure, the first detection of an association with MI did not arise by signal detection from case reports but rather from a meta-analysis of published and unpublished RCTs. Although this meta-analysis presented important findings for further evaluation, there were several important limitations. Of note, this analysis was based on findings of trials that were publicly available and, as is typical with most meta-analyses, did not use patient-level data (i.e., only aggregated statistics from each of the individual trials were used) to estimate the effect. In addition, the trials included in this meta-analysis were not designed to assess MI and cardiovascular death. Finally, the trials did not have information on how MI was defined in the included studies, thereby potentially resulting in a heterogeneous definition of the outcome. After this meta-analysis, at least six other meta-analyses were published assessing the association of rosiglitazone (three studies), pioglitazone (two studies), or both (one study) with MI risk. The three rosiglitazone studies all showed increased risk of MI, whereas the two pioglitazone studies showed a decreased risk of MI. The meta-analyses of rosiglitazone did not find an increased risk of cardiovascular mortality, but the two studies of pioglitazone found a protective effect of pioglitazone on cardiovascular mortality. Although meta-analyses can provide important information on possible safety concerns of marketed drugs, they are subject to the same limitations of the included studies. In this example, the meta-analyses all included RCTs of relatively short duration and homogeneity in their included patient populations, yet information on the posEvaluating Drug-Induced Cardiovascular Disease

Observational Studies Evaluating TZDs and MI A nested case-control study conducted in Ontario, Canada, used the nationalized health insurance databases to create a cohort of patients with diabetes, at least 66 years old, and taking at least one antidiabetic drug. Individuals were eligible for inclusion if they met these criteria between April 1, 2002, and March 31, 2005. The eligible cohort was observed until they experienced an event (acute MI, heart failure, or death) or until March 31, 2006. Within this cohort, a nested case-control study was then conducted to assess the possible association between TZDs and MI. To this end, the study identified all cohort members experiencing an acute MI as cases to be included in the study. Risk-set sampling was used to identify a maximum of five controls for each case that were matched on age, sex, duration of diabetes, 5-year history of cardiovascular disease, and previous MI. The final sample consisted of 75,229 individuals, 12,578 cases, and 62,651 matched controls. Researchers then assessed antidiabetic drug exposure in the year before the index date (date of acute MI for cases and the case’s date for matched controls). The primary interest was TZD exposure, defined as current (prescription within 14 days of the index date) or past (15–365 days before the index date). Further stratification occurred on the basis of TZD as monotherapy versus combination therapy. The final analysis assessed exposure history in four categories: current TZD monotherapy, current TZD combination therapy, past treatment with TZDs, and other oral hypoglycemic combination therapy (referent group). After adjustment for between-group differences and use of a matched sampling scheme through conditional logistic regression, current TZD monotherapy was significantly associated with an increased risk of acute MI compared with the referent group. The other two groups (current TZD combination therapy and past treatment with TZDs) did not show a significant effect. Further division of the TZD monotherapy group into current rosiglitazone and pioglitazone exposure showed a statistically significant increase in risk of acute MI in the rosiglitazone group, whereas the current pioglitazone group did not show a statistically significant effect. Because this study relied on administrative data (i.e., health insurance claims data), it is possible that it missed cases of acute MI as well as instances of TZD exposure. In 232


of acute MI. The first retrospective cohort study used administrative data from a large U.S.-based health plan. The study included patients with diabetes who were at least 40 years old and had at least two prescriptions filled for either rosiglitazone (n=15,104) or pioglitazone (n=14,807) between 2003 and 2006. These enrollees were then observed for a hospitalization for acute MI for an average of 1.2 years (pioglitazone group) and 1.3 years (rosiglitazone group). After 17,256 person-years of follow-up in the pioglitazone group, there were 161 hospitalizations for acute MI; after 19,229 person-years of follow-up in the rosiglitazone group, there were 214 hospitalizations for acute MI. After adjustment for clinical differences between the groups using Cox proportional hazards modeling, pioglitazone was associated with a statistically significant decrease in MI rates compared with rosiglitazone. Because of the observational design of this study, coupled with its reliance on an administrative data source, residual confounding cannot be ruled out as a contributor to these findings. For example, claims databases do not often have information on height or weight, prohibiting body mass index (BMI) calculation. Because obesity (i.e., BMI greater than 30 kg/m2) can also have an influence on MI risk independent of TZD use, residual confounding can result. As in other studies, it is also important to consider drug acquisition versus actual consumption when using pharmacy claims databases to assess TZD exposure. The second cohort study used a Medicare database and identified a group of older (65+) Medicare beneficiaries with diabetes who had newly started treatment with either rosiglitazone or pioglitazone in 2000–2005. The study identified 28,361 patients: 14,260 (50.3%) initiated on rosiglitazone and 14,101 (49.7%) initiated on pioglitazone. These two groups were then observed for the primary outcome of all-cause mortality and the three secondary outcomes of acute MI, stroke, and hospitalization for congestive heart failure. Using Cox proportional hazards modeling, researchers estimated the effect of rosiglitazone relative to pioglitazone on each of these outcomes. After more than 14,000 person-years of follow-up in each group, the event rate for all-cause mortality was 59.7 per 1000 person-years in the pioglitazone group and 69.2 per 1000 person-years in the rosiglitazone group. The rate of MI was 24.7 per 1000 person-years in the pioglitazone group and 26.5 per 1000 person-years in the rosiglitazone group. After adjustment for baseline differences, there was a statistically significant increased all-cause mortality rate in rosiglitazone compared with pioglitazone users. A nonsignificant difference in the rate of acute MI between the two groups remained even after stratification by history of coronary artery disease. Although each study is unique, these observational studies have important commonalities. All four studies were conducted using administrative data sets and benefited from the inclusion of large sample sizes. These sample

addition, TZD exposure was based on pharmacy claims data, which are indicative of patient acquisition of the drug but not necessarily patient consumption of the TZD. Finally, the referent group in this study was all non-TZD antidiabetic drug therapy; the inherent heterogeneity of this referent group must also be considered. Another recent retrospective cohort study assessed the effect of TZDs (troglitazone, rosiglitazone, and pioglitazone), metformin, and the combination of both by performing a secondary analysis of Medicare data from April 1998 to March 1999 and from July 2000 to June 2001. The study identified 8872 Medicare beneficiaries, at least 65 years old and with diabetes, who were discharged from a hospital after an acute MI. Participants were categorized by the diabetes drugs prescribed at discharge into four groups: (1) those taking TZDs (but not metformin); (2) taking metformin (but not TZDs); (3) those taking both TZDs and metformin; and (4) those taking antidiabetic drug therapy but not TZDs or metformin (referent group). The four study cohorts were observed for a maximum of 1 year for the primary outcome of time to all-cause mortality. Participants were also observed for secondary outcomes including time to rehospitalization for acute MI, time to rehospitalization for heart failure, and time to all-cause rehospitalization. Because this was a nonrandomized study, the analyses were adjusted for demographic and clinical characteristics between the four groups using a Cox proportional hazards model. After adjusting for baseline differences, the study found a significant association, an approximately 50% reduction, between taking both metformin and a TZD (compared with the referent group) and rates of all-cause mortality. This comparison was not significant for the other two groups. Furthermore, within 1 year of discharge, 16.5% of the metformin group, 18.8% of the TZD group, 15.1% of the combination group, and 18.8% of the referent group had experienced a rehospitalization for acute MI, a nonsignificant but potentially clinically meaningful difference. Several important limitations must be considered, including the possibility of residual confounding (including confounding by indication) and the inclusion of all TZDs in one group, disallowing evaluation of a differing effect between the two TZD agents. As in other studies, the referent group was a heterogeneous group of people taking other antidiabetic drug therapy (in addition to metformin and/or TZDs). As designed, this study does not further permit evaluation of the underlying mechanism for the combined effect of metformin and TZDs because this study group was small (n=139), and direct comparisons with TZD therapy (without metformin) and metformin therapy (without a TZD) were not performed. This study and the Canadian study also focused on rehospitalization for identifying MI cases and may have missed instances of MI that resulted in death before rehospitalization. Two other observational cohort studies directly compared rosiglitazone and pioglitazone exposure on the rates


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sizes, typically not feasible in RCTs, allow the identification of adverse events occurring more rarely than could be detected in a typical trial. Three of the four studies assessed older patients; one study assessed relatively younger patients (older than 40). Although not presented, these studies all had less restrictive eligibility criteria than randomized trials of TZDs and included patients with diverse demographic, social, and clinical factors, making them more representative of real-world patients with diabetes. These study attributes allow important insight into the possible role of TZDs on unintended cardiovascular outcomes in patients more closely resembling patients in everyday clinical practice. Although each study had differing eligibility criteria and differences in design, they were all subject to common limitations that should be considered when interpreting their results. Because these studies were nonrandomized, residual confounding (e.g., confounding by indication) may be at least partially responsible for the observed associations (or, in some cases, the lack of observed associations). However, each study analytically controlled for baseline differences between the comparison groups. In addition, they all relied on administrative data sources to identify exposure to TZDs and other drugs of interest. Because drugs are often changed or discontinued over time, or patients do not adhere to them, misclassification of exposure is probable. Despite these limitations, these studies, when evaluated in the context of all available evidence, can provide useful insight into the possible association between TZDs and MI and cardiovascular mortality in less homogeneous populations.

evaluating individual studies, the pharmacist must then shift focus to the larger realm of a conclusion based on the findings across all studies. Things to consider when evaluating a possible association include: (1) the source(s) of the information (signal detection, observational design, or randomized study); (2) the consistency of the effect across all the available evidence; (3) the magnitude of the association, because larger effects (e.g., a relative risk greater than 2) are unlikely to be the result of residual biases, whereas smaller effects may be the result of uncontrolled biases; (4) the validity of each of the studies; and (5) other contributing factors that may be causing the association. In general, in the absence of a definitive conclusion, pharmacists should be reminded that using a drug in a particular patient should always necessitate an evaluation of the riskto-benefit ratio. Early warning signs for a particular agent arising from spontaneous reporting may cause clinicians to prescribe that drug more cautiously until more complete information is available, which may further limit the continued analysis of the spontaneous reporting database. Results from more definitive randomized trials and meta-analyses of these trials may assist clinicians in determining the possible causal association between the drug and the adverse outcome, yet they often do not provide estimates of the occurrence of an adverse event in a realworld population, a necessary component for accurately determining the risk-to-benefit ratio for a particular patient. Furthermore, RCTs are time-consuming to complete, and this information delay could lead to unnecessary harm in patients already taking the drug. To fill this gap, well-designed observational studies can be of assistance by providing estimates of risk in real-world patient populations. Through careful consideration and evaluation of all available sources of information, clinicians can more fully assess the risk-to-benefit ratio of a particular drug for their patients. The example of TZDs and MI shows that risk estimation for an adverse event often requires several different studies, each providing pieces of information that can be used to more fully understand the risk profile of a drug. This process may be lengthy; as it unfolds, clinicians must make decisions regarding the risks and benefits of using a drug in their patient population. For TZDs, the risk of MI in patients taking rosiglitazone appears probable, yet definitive studies showing causality are lacking. The FDA advisory committee arrived at this conclusion in June 2007, and as a result, a black box warning was added to the package insert for rosiglitazone. For pioglitazone, the evidence is suggestive of a protective cardiovascular effect, yet some of the information is conflicting. To better understand the possible protective effect of pioglitazone on MI, additional studies are necessary and should include both observational studies and an LST. As this story continues to unfold, pharmacists with an understanding of pharmacoepidemiologic methods will

Causality: Weighing All Available Evidence With the abundance of information in the medical literature, clinicians may encounter difficulty in keeping up with published studies. However, with previously unrecognized adverse events, the medical literature may be scant and slowly unfolding, often leaving clinicians to weigh the risks and benefits of using a particular drug on the basis of incomplete information. Arriving at the decision of whether a drug-related adverse event is probable can be challenging, requiring clinicians to assess the breadth and depth of available information and arrive at a conclusion that is relevant to their clinical practice. As discussed, many pre- and postmarketing methods can be used to evaluate the possible contribution of a drug to an adverse outcome; each method has its own inherent strengths and limitations. Pharmacists must often err on the side of caution and incorporate risk management strategies into their clinical practice before complete safety information is available. As previously discussed, pharmacists must evaluate the particular threats to validity within individual studies. After Evaluating Drug-Induced Cardiovascular Disease

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be better prepared to evaluate the existing evidence and proactively make a decision.

focusing on quantitative signal detection. This article is a good resource for pharmacists wanting to understand more about particular quantitative methods used worldwide (e.g., the Bayesian Confidence Propagation Neural Network). In addition, this article highlights challenges with these methods and provides insight into future directions of signal detection.

Conclusion Clinicians routinely assess the risks and benefits of using drugs in their patients. Using a pharmacoepidemiologic approach, clinicians can more fully evaluate the risk-tobenefit ratio by evaluating all sources of information available to them, including those originating from pharmacovigilance, observational studies, and randomized trials.

5.

Although this article is more than 15 years old, it is a good reference for clinicians evaluating studies that specifically assess adverse events. The article lays a foundation for evaluating studies in assessing harm and helps clinicians approach this literature thoughtfully and statistically. One important caveat is that this article was written before the increased reliance on observational studies to evaluate possible adverse events; it therefore focuses on the evidence generated from randomized studies.

Annotated Bibliography 1.

Strom BL, Kimmel SE. Textbook of Pharmacoepidemiology. West Sussex, UK: Wiley, 2006. The authors provide a broad overview of pharmacoepidemiology. This textbook is the standard for all pharmacists desiring a more in-depth understanding of pharmacoepidemiology. In this book, there are plentiful examples of different aspects of pharmacoepidemiology including study design; sources of bias; and views of pharmacoepidemiology from academia, industry, and government. The well-written book is a good resource for clinical pharmacists as they read the medical literature or conduct clinical research studies.

2.

6.

4.

7.

Thom EA, Klebanoff MA. Issues in clinical trial design: stopping a trial early and the large simple trial. Am J Obstet Gynecol 2005;193:619–25. These authors present two topics in this article relative to randomized studies: early termination of trials and LST use. Although both are interesting, the latter topic provides a brief introduction to the theoretical underpinnings of the LST, together with a comparison to the more common RCT.

van Manen RP, Fram D, DuMouchel W. Signal detection methodologies to support effective safety management. Drug Saf 2007;6:451–64. The authors of this journal article present an in-depth but understandable review of automated signal detection methods, from underlying concepts to practical application. The article focuses on quantitative signal detection methods, but the authors also highlight the complementarity of quantitative and qualitative signal detection methods. For clinicians interested in a more in-depth understanding of the different quantitative methods in use, this article provides a rigorous introduction.

8.

Hauben M. A brief primer on automated signal detection. Ann Pharmacother 2003;37:1117–23.

9.

Peto R, Collins R, Gray R. Large-scale randomized evidence: large, simple trials and overviews of trials. J Clin Epidemiol 1995;48:23–40. This historical piece (the major driving force for LSTs) provides an in-depth primer on the conceptual framework necessary for the conduct of LSTs. This article first reviews some notable trials and then provides the basis for LSTs including an in-depth discussion of the uncertainty principle, a necessary assumption when an LST is conducted.

Written by a renowned pharmacovigilance expert, this article is based on a comprehensive review of the signal detection methods used in the medical literature,


Guyatt G, Meade M, Drummond R, Cook E. Users’ Guide to the Medical Literature: A Manual for Evidence-Based Clinical Practice, 2nd ed. New York: McGraw-Hill Medical, 2008. Similar to the 1994 article above, this text provides a comprehensive framework for practicing clinicians to use when evaluating the medical literature. The text covers everything from an overview of the principles of evidence-based medicine to specific items to consider when interpreting published studies including issues in the design, threats to validity, and a review of relevant statistical issues. This easyto-follow text provides an abundance of timely, practical examples.

Almenoff J, Tonning JM, Gould AL, Szarfman A, Hauben M, Ouellet-Hellstrom R, et al. Perspectives on the use of data mining in pharmacovigilance. Drug Saf 2005;28:981–1007. This article, the result of a working group (Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools), provides a comprehensive review of data mining as applied to pharmacovigilance. Because the committee drafting this report was diverse, so too are the views presented. This article reviews basic tenets of data mining including theory, strengths, and weaknesses and further provides a more in-depth review of methodologies commonly used. Finally, this article provides a brief review of spontaneous reporting databases used in the United States and internationally.

3.

Levine M, Walter S, Lee H, Haines T, Holbrook A, Moyer V; for the Evidence-Based Medicine Working Group. User’s guide to the medical literature. IV. How to use an article about harm. JAMA 1994;271:1615–9.

Nissen SE, Wolski K. Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. N Engl J Med 2007;356:2457–71. This article presents the results of a meta-analysis published in June 2007, suggesting an increased risk of MI and

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limited because treatment patterns may differ (i.e., TZDs may be prescribed differently in the United States).

cardiovascular death from rosiglitazone. This meta-analysis included 42 randomized trials, each comparing rosiglitazone with a control group and lasting at least 24 weeks. In this analysis, the authors estimated a 43% increase in MI (odds ratio [OR] = 1.43; 95% confidence interval [CI], 1.03–1.98) and a nonsignificant 64% increase in cardiovascular death (OR = 1.64; 95% CI, 0.98–2.74) in rosiglitazone users compared with the control group. As a result, the FDA conducted a committee meeting on July 30, 2007, and concluded that there was sufficient evidence to suggest an association between rosiglitazone use in type 2 diabetes mellitus and myocardial ischemia, resulting in the addition of a black box warning. Major criticisms of this analysis focus on the lack of access to patient-level data, the omission of some trials, and that none of the trials included in the analysis were designed to assess these end points.

12. Gerrits CM, Bhattacharya M, Manthena S, Baran R, Perez A, Kupfer S. A comparison of pioglitazone and rosiglitazone for hospitalization for acute myocardial infarction in type 2 diabetes. Pharmacoepidemiol Drug Saf 2007;16:1065–71. This study used a retrospective cohort design and directly compared two large groups of patients, one taking pioglitazone (n=14,807) and the other taking rosiglitazone (n=15,104); these groups were then observed for acute MI. As in other observational studies, this study focused on an elderly Medicare population with less restrictive eligibility criteria compared with previous RCTs. After follow-up, the study found a decreased acute MI rate in pioglitazone users compared with rosiglitazone users (HR = 0.78; 95% CI, 0.63–0.96). This study is important because it is one of the few that provides information on the direct comparison of pioglitazone with rosiglitazone. However, despite statistical adjustment, baseline differences between the groups that were unmeasured or unaccounted for may have contributed to this estimated effect through residual confounding.

10. Inzucchi SE, Masoudi FA, Wang Y, Kosiborod M, Foody JM, Setaro JF, et al. Insulin-sensitizing antihyperglycemic drugs and mortality after acute myocardial infarction. Diabetes Care 2005;28:1680–9. This observational prospective cohort study used administrative data from 1998 to 2001 and included a large cohort of elderly Medicare beneficiaries (n=8872). These patients,recently discharged from the hospital after an acute MI, were prescribed a TZD, metformin, or both on discharge. They were then observed for 1 year, principally for all-cause mortality or secondarily for readmission to the hospital for acute MI, heart failure, and all causes of readmission combined. This study did not find an increased association of the primary end point but found increased all-cause and heart failure readmission rates (all cause readmission hazard ratio [HR] = 1.09; 95% CI, 1.00–1.20 and heart failure readmission HR = 1.17; 95% CI, 1.05–1.30) in TZD users compared with metformin users. This study provides important information because it assesses the risk of cardiovascular outcomes in patients already at high risk (because of a recent MI).

13. Winkelmayer WC, Setoguchi S, Levin R, Solomon DH. Comparison of cardiovascular outcomes in elderly patients with diabetes who initiated rosiglitazone vs pioglitazone therapy. Arch Intern Med 2008;168:2368–75. This observational study also used a Medicare database of older beneficiaries. The identified patients were newly initiated on treatment with rosiglitazone (n=14,260) or pioglitazone (n=14,101); they were observed for all-cause mortality (primary outcome) and acute MI, stroke, or hospitalization for congestive heart failure (secondary outcomes). After adjustment for baseline differences, there was a statistically significant increased rate of all-cause mortality (HR = 1.15; 95% CI, 1.05–1.26) and congestive heart failure (HR = 1.13; 95% CI, 1.01–1.26) in patients taking rosiglitazone compared with pioglitazone. However, the study failed to show an association on rates of MI or stroke. Of interest, this study also performed a direct comparison of pioglitazone and rosiglitazone and assessed new users.

11. Lipscombe LL, Gomes T, Levesque LE, Hux JE, Juurlink DN, Alter DA. Thiazolidinediones and cardiovascular outcomes in older patients with diabetes. JAMA 2007;298:2634–43. This study of older adults was conducted in Canada using a large health care database and a nested case-control design. This study observed a sample of 159,026 patients and identified 12,578 MI cases and a similar group of 62,651 controls. Study authors then assessed TZD exposure and found an increased risk of MI associated with current TZD monotherapy prescription use (OR = 1.40; 95% CI, 1.05–1.86) compared with individuals using other agents. Additional analyses of the TZD monotherapy group partitioned the group into current rosiglitazone and pioglitazone exposure; it showed a statistically significant increase in risk of acute MI in the rosiglitazone group (OR = 1.76; 95% CI, 1.27-2.44). The current pioglitazone group did not show a statistically significant effect (OR = 0.73; 95% CI, 0.40–1.36). This study is useful because it was conducted in a large population of real-world patients with diabetes. However, because this study was conducted in Canada, generalizability to the U.S. general population is potentially


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Self-Assessment Questions 40. A patient recently suffered an ST-segment elevation myocardial infarction (MI). Based on your clinical experience, you are concerned that one of the patient’s drugs could have contributed to the MI. Which one of the following is best in reporting this possible adverse event?

375 patients in each of the study groups. In the published study results, the authors state: “Despite our smaller enrolled sample size, post hoc power calculations suggest that the sample size of 375 patients per group resulted in acceptable power (80%) to detect the originally hypothesized difference of 12% between the groups.” Which one of the following is the most critical concern of the decreased participant enrollment in this study?

A. Conduct a thorough patient interview coupled with a review of the patient’s medical record, and complete and submit a single MedWatch form. B. Conduct a thorough patient interview coupled with a review of the patient’s medical record, and complete and submit a separate MedWatch form for each suspected drug agent. C. Contact the manufacturers of the suspected agents and ask for information on the probable role of each agent in causing the MI. D. Report the event to the hospital administrators and suggest they file a MedWatch form.

A. The study was underpowered to identify rare adverse events. B. The effectiveness of the randomization procedure was suboptimal. C. The study was underpowered to detect a difference in the primary end point of all-cause cardiovascular mortality. D. The study was underpowered to detect a difference in the secondary end points of adverse drug events.

41. Using data mining techniques in the Adverse Event Reporting System (AERS) database, the FDA identified 23 case reports of isolated systolic hypertension attributed to a newly approved diet agent. The original phase III clinical trial was conducted over 12 months, enrolling 1200 women with moderate obesity (body mass index [BMI], 30–32 kg/m2). A recent observational descriptive study estimating the prevalence of diet agent use suggests that 1.5 million U.S. women have taken the drug since it was marketed. Which one of the following is the most probable reason this adverse event was undetected in the original randomized clinical trial (RCT)?

43. Which one of the following is most important to consider when using the AERS database versus the in-house database of an individual pharmaceutical company? A. The severity of the hypothesized adverse event. B. The size of the spontaneous reporting database. C. The quality of the reports contained in the database. D. The disproportionality statistical method to be used. 44. Which of the following statements best describes an example of data mining in the context of pharmacovigilance?

A. The trial would require at least 3,000,000 women to detect this effect. B. The trial would require at least 300,000 women to detect this effect. C. The trial would require at least 30,000 women to detect this effect. D. The trial would require at least 15,000 women to detect this effect.

A. Use of a health care database to assess the association between a drug and negative outcomes. B. Application of a Bayesian statistical approach to the FDA’s AERS database to flag possible signals. C. Analysis of an RCT to assess prespecified adverse events. D. Clinician review of spontaneous reports identified in the FDA’s AERS database.

42. A recent phase III RCT of an antihypertensive agent (compared with placebo) was designed to assess a 12% group difference in the primary trial end point, blood pressure control, with 90% power. To achieve this power, the trial required the enrollment of 423 patients per group. Furthermore, this trial was designed to detect secondary outcomes, namely relatively common adverse drug reactions that differed by at least 5% between the two groups with 80% power. Despite the researchers’ best efforts, they were only able to enroll PSAP-VII • Cardiology

45. Which one of the following is most important to consider when qualitatively evaluating signals identified through data mining of spontaneous reporting databases? A. The strength of the association estimated during signal detection. 237


C. The possibility of differences in reporting requirements by contributing countries. D. The possibility of underreporting within the database.

B. The size of the spontaneous reporting database used to identify the signal. C. The clinical context in which the signal may be occurring. D. The disproportionality statistical method used to identify the signal.

50. A drug company identified 44 adverse reports of QTc prolongation attributed to one of its antihistamine products. The company’s spontaneous reporting database contains 125,764 spontaneous reports, of which 532 mention this antihistamine and 2012 mention QTc prolongation. Using a non-Bayesian approach, which one of the following is the relative reporting ratio (RRR)?

46. After FDA label approval of a new nonsteroidal anti-inflammatory drug, which one of the following examples best represents a signal that might be detected during quantitative signal detection but not flagged for further evaluation during qualitative signal detection? A. B. C. D.

Kidney failure. Heart failure. Gastritis. MI.

A. B. C. D.

47. Which one of the following statements best describes an inherent characteristic of quantitative signal detection methods used in pharmacovigilance?

51. A small U.S. drug company identified 12 spontaneous reports attributing cases of cardiomyopathy to one of their popular cardiovascular drugs. After calculating the RRR by a Bayesian approach, the reports were not flagged for further evaluation. Which one of the following is the most important concern about the company’s analysis?

A. Their performance is independent of the database to which they are applied. B. They can identify even the rarest of signals. C. They rely on Bayesian inference for estimation. D. They assume a causal relationship in their evaluation.

A. The use of a Bayesian approach for calculating the RRR. B. The lack of evaluation of other possible causes for the cardiomyopathy. C. The possibility of masking this effect because of the small database. D. The quality of the adverse reports used in the analysis.

48. Disproportionality analyses of the AERS database identified a higher-than-expected rate of cardiovascular rehospitalization attributed to clopidogrel when used in combination with omeprazole. This signal was then further evaluated by a physician at the FDA. Which one of the following is the most important factor for the clinician to consider in the qualitative review of this signal?

52. Recently, 15 case reports suggesting an association between a new antihypertensive agent and increased risk of sudden cardiac death were submitted to the FDA. This new antihypertensive agent is a third-line agent and is rapidly gaining market share. After review, the FDA suggests additional investigation of this signal. Which one of the following study designs is best to conduct at this time?

A. The pharmacologic basis for a drug interaction between clopidogrel and omeprazole. B. The disproportionality analysis methodology used to identify the signal. C. The size of the AERS database. D. The potential for masking.

A. B. C. D.

49. The FDA and WHO conducted a cooperative evaluation of the WHO spontaneous reporting database to evaluate the signal of a possible class effect of one class of antidiabetic agents on MI. Which one of the following concerns of this evaluation is most likely to require further consideration?

Large, simple trial (LST). Case-control study. Case series. Small RCT.

53. The FDA has suggested that a particular drug company should perform a postmarketing study to evaluate whether their recently marketed drug (approved 3 months ago) causes isolated systolic hypertension. Which one of the following study designs is most likely to clarify the temporal relationship

A. The possibility of different MI coding schemes used by contributing countries. B. The possibility of masking of this effect. Evaluating Drug-Induced Cardiovascular Disease

1.02. 5.17. 75.43. 32.51.

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between the weight-loss drug and isolated systolic hypertension? A. Small RCT. B. LST. C. Case-control study. D. Prospective cohort study.

Questions 58 and 59 pertain to the following case. As described in the text, four published observational studies have provided information on the possible role of TZDs on rate of MI. 58. Which one of the following best describes a common problem occurring across these studies? A. The TZD exposure measurement focuses on drug acquisition versus consumption. B. The temporal sequence between TZD and cardiovascular outcomes is blurred. C. They did not account for baseline differences between the groups compared. D. The assessment of cardiovascular outcomes was not valid.

54. Which one of the following is the best example of confounding by indication when assessing the effect of thiazolidinediones (TZDs) on heart failure rates? A. TZDs cause fluid retention. B. TZDs are used more often by smokers. C. TZDs are used more often by elderly patients. D. TZDs are not first-line therapy.

59. Which of the following statements best describes an important piece of information the clinician can use to assess the risk-to-benefit ratio associated with rosiglitazone? A. The MI rate in a real-world population taking rosiglitazone. B. The causal role of rosiglitazone on MI rates. C. The role of other factors contributing to the effect of rosiglitazone on MI rates. D. The differential effects of pioglitazone and rosiglitazone on MI rates.

55. A drug manufacturer has decided to fund an LST to assess the long-term effects of pioglitazone on cardiovascular mortality. Which of the following statements best describes a necessary assumption to conduct this trial? A. No subgroups of the population will differentially benefit from pioglitazone. B. The relative reduction in mortality must be large to justify the trial’s cost. C. Pioglitazone must have an exceptional safety profile from all studies published to date. D. Trial participants must be amenable to extensive clinical follow-up. 56. A new antihypertensive drug recently approved by the FDA showed marked improvements in blood pressure control during a 6-month phase III trial of 2100 individuals. Which one of the following additional primary outcomes could most feasibly be evaluated using an LST design? A. Decreased rates of stroke. B. Decreased rates of all-cause mortality. C. Increased rates of aplastic anemia. D. Decreased rates of kidney failure. 57. Which one of the following best describes the relationship between pharmacoepidemiology and pharmacovigilance in identifying new adverse events caused by drugs? A. Pharmacovigilance focuses on premarketing and pharmacoepidemiology on postmarketing. B. Pharmacovigilance generates hypotheses to be evaluated by pharmacoepidemiologic studies. C. Pharmacoepidemiologic studies generate hypotheses that can be evaluated by pharmacovigilance. D. Pharmacoepidemiology is quantitative, whereas pharmacovigilance is qualitative.


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