Retention in Psychosocial Treatment of Cocaine Dependence ...

The American Journal on Addictions 11:24- 40, 2002 # 2002 American Academy of Addiction Psychiatry

Published by Brunner-Routledge 1055-0496/02 $12.00 + .00

Retention in Psychosocial Treatment of Cocaine Dependence: Predictors and Impact on Outcome Lynne Siqueland, Ph.D., Paul Crits-Christoph, Ph.D., Robert Gallop, Ph.D., Jacques P. Barber, Ph.D., Margaret L. Griffin, Ph.D., Michael E. Thase, M.D., Denis Daley, M.A., Arlene Frank, Ph.D., David R. Gastfriend, M.D., Jack Blaine, M.D., Mary Beth Connolly, Ph.D., Madeline Gladis, Ph.D. This report describes retention in treatment in the National Institute on Drug Abuse Collaborative Cocaine Treatment Study (CCTS), a multi-site trial of four psychosocial treatments for 487 cocaine dependent patients. Younger, African-American, and unemployed patients were retained in treatment for fewer days than their counterparts. African-American patients who lived with a partner were retained in treatment for less time than if they lived alone. Higher psychiatric severity kept men in treatment longer but put women at risk for dropping out sooner. Patients who completed the full treatment used drugs less often than patients who dropped out, but outcome did not diåer at each month. Patients in the drug counseling condition stayed in treatment for fewer days than patients in psychotherapy, but they were more likely to be abstinent after dropout. Patients with higher psychiatric severity were more at risk for continuing to use drugs after dropout. (Am J Addict 2001;11:24- 40)

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ment,see 1 where longer time in treatment has generally been associated with better outcome.2- 4 In samples of primarily opiate-dependent patients, those patients who completed treatment were more likely to be drug-free, to have lower relapse rates,

his report examines the variables associated with retention in treatment in a large, multi-site psychotherapy study for patients with cocaine dependence. Retention of patients has been a particular concern in substance dependence treat-

Received February 2, 2001; accepted March 26, 2001. From the University of Pennsylvania Medical School, Philadelphia (Drs. Siqueland, Crits-Christoph , Gallop, Barber, Connolly and Gladis); McLean Hospital, Belmont, Mass. (Dr. Gri¬n); Western Psychiatric Institute and Clinic/University of Pittsburgh, Pittsburgh, (Dr. Thase and Mr. Daley); Health Enhancement Services, Inc., Research Division (Dr. Frank); Massachusetts General Hospital/Harvard Medical School, Boston (Dr. Gastfriend); and the National Institute on Drug Abuse, Bethesda, Md. (Dr. Blaine). Address correspondence to Dr. Siqueland, University of Pennsylvania Medical School, 3535 Market St., 6th Fl., Philadelphia, PA 19104. E-mail: siqueland@ pobox.com. 24

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and to have lower unemployment rates and fewer arrests than those who drop out.1 The majority of cocaine-dependent patients do not even complete brief, 3-month treatment programs.5, see 6 However, the association of time in treatment and outcome has been less clear cut in cocaine-dependent samples than in opiatedependent samples.5 Accurate identi®cation of dropouts from substance abuse treatment may assist in modifying treatments so that retention can be improved and maximal bene®ts achieved. Diåerent investigators and treatment settings have yielded con¯icting ®ndings regarding the patient factors associated with retention in treatment. Several studies have reported that patients who completed treatment were more likely to be younger,7 white,5,7,8 and married or cohabitating versus living alone.5,9 However, age, race, and marital status were unrelated to dropout rates in other studies.10- 12 In the studies that have identi®ed a group of patients who are at risk for not completing treatment, it is a ``disenfranchised group’’ of unemployed minority patients who live alone. The role of severity of drug use in predicting retention in treatment is also unclear. Longer drug use histories and more treatments have been associated with lower dropout rates.6,9 Severity of current drug use has also yielded inconsistent results; where some studies found it to predict earlier dropout,6,13,14 others did not.5,10 Patients with concurrent anxiety and depressive symptoms tend to stay in treatment longer than those without such symptoms.5,15,16 Antisocial personality disorder has been associated with lower retention in some substance abusers;see 1,17 however, Carroll et al5 did not replicate this ®nding with cocaine-dependent patients. In summary, although there is some consistency in the ®ndings that have emerged from studies of retention during

treatment of patients with opiate dependence or mixed substance use, less is known about retention in the treatment of patients with cocaine dependence in particular. The small number of studies of cocaine dependence that have examined diverse treatments and sample sizes have been limited, possibility contributing to the lack of consistency of ®ndings regarding predictors of retention. Moreover, no studies have reported a comprehensive analysis of attrition from treatment of cocaine dependence with an examination of multiple predictors, including interactions among predictor variables, or an attempt to unravel the potentially complicated relationship of retention to outcome. The National Institute on Drug Abuse (NIDA) Collaborative Cocaine Treatment Study (NCCS)18 recently reported that a combination of manual-guided individual plus group drug counseling yielded relatively superior drug use outcomes when compared to cognitive therapy plus group drug counseling, supportive-expressive psychotherapy plus group drug counseling, or group drug counseling alone. Using data from the NCCS, the current article addresses a number of questions regarding retention during the active phase of treatment of the clinical trial. Speci®cally, the following questions are asked: (1) What pre-treatment patient variables, or interactions among these variables, are associated with retention in treatment? (2) Are there diåerent predictors of patients who choose to leave treatment compared to those who have to leave treatment due to a move, jail, or hospitalization? (3) Are there diåerent predictors of attrition for patients who are using drugs after drop-out compared to those who do not continue to use? (4) What is the relationship of time in treatment to outcome, and does this relationship vary by treatment type?

THE AMERICAN JOURNAL ON ADDICTIONS

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Retention in Treatment of Cocaine

The large sample size (487 randomized patients) permitted an analysis of multiple predictor variables, including demographic factors, severity and mode of drug use, comorbid psychiatric symptoms, and antisocial personality, as well as selected interactions among predictors. Focusing on the literature on opiate dependence, mixed substance use, and cocaine dependence, we hypothesized that patients who drop out from treatment are more likely to be unemployed, younger, non-white, and living alone. No speci®c hypotheses were generated about other predictor variables (drug severity, psychiatric severity, antisocial personality, interactions among predictors) because of the mixed ®ndings, or lack of studies, in the literature on these variables. METHOD Patients

A total of 487 patients were randomized to treatment at ®ve sites: the University of Pennsylvania (Philadelphia, Pa.), Massachusetts General Hospital (Boston, Mass.), McLean Hospital (Belmont, Mass.), the University of Pittsburgh (Pittsburgh, Pa.), Western Psychiatric Institute and Clinic (Pittsburgh, Pa.), and Brookside Hospital (Nashua, NH). Patients were recruited from a number of sources: 22% from substance abuse treatment centers; 46% by newspaper or ¯yer, 18% referred by friend/zacquaintance, 8% from mental health centers, and 6% from private mental health providers. The inclusion criteria were cocaine use in the past 30 days, principal diagnosis DSM-IV cocaine dependence (current or in early partial remission), and ages 18- 60. Exclusion criteria included for diagnoses: principal diagnosis of alcohol dependence, opioid dependence (current or in early partial remission), polysubstance dependence,

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dementia or other irreversible organic brain syndrome, psychotic symptoms, and history of Bipolar I disorder. Principal diagnosis was established using a 0 to 8 severity rating scale adapted from the Anxiety Disorders Interview Scheduleö Revised19 that re¯ects the evaluation of subjective distress/functional impairment. Additional exclusion criteria included imminent suicide or homicide risk, unwillingness to discontinue current psychotherapeutic treatment, need to be maintained on a psychotropic medication, life-threatening or unstable medical illness, impending incarceration, inpatient drug hospitalization for more than 10 of the past 30 days, mandate for treatment by legal or Children Protective Services, residence in a halfway house, patient > 12 weeks pregnant, patient not interested in participating in study, patient out of area for 1 year, patient cannot meet demands of study (group, sessions per week), patient does not have stable living situation, and patient unable to understand forms or give consent. Percentages of patients excluded for various reasons can be found in Crits-Christoph et al.18 Patients were screened over the telephone and invited for an intake visit, if appropriate. After the intake visit and informed consent, patients began an orientation phase that included both attendance and assessment requirements designed to select patients motivated enough to attend at least a few sessions. The patient was required to attend three clinic visits within 14 days, including one group session and two case management visits before being randomized to treatment. In the orientation phase, group counselors assessed current drug use and craving, provided education on addiction and relapse, addressed housing, job, or ®nancial needs, encouraged attendance at self-help groups (such as Cocaine or Alcoholics Anonymous), and educated patients regarding HIV risk reduction. Patients

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who met attendance requirements completed a post-orientation assessment of 1 to 2 days. The mean length of time from the start of the orientation phase to randomization to treatment was 12 days (SD = 6). Randomization Procedure

Following satisfactory completion of post-orientation assessments, patients were randomized to treatment centrally from the coordinating center and separately at each site using a computerized ``urn’’ randomization procedure.20 Treatments were balanced on several likely predictors of outcome and variables relevant to study hypotheses: mode of cocaine use, psychiatric severity, antisocial personality traits score, gender, marital status, and employment status.

were held twice weekly for the ®rst 12 weeks, weekly during weeks 13 to 24, and monthly during the booster phase. Group drug counseling sessions were one and a half hours in length and were held once a week for the 6 months of the active phase. During the booster phase, patients in GDC alone met with the group counselor individually once a month for half an hour. All treatment was provided free of charge. Therapists

Therapists and counselors were carefully selected, trained, and assessed for competence in delivering the treatments in the initial training phase.see 18,27 Diåerent therapists/counselors worked at each site. Fifteen CT therapists, 13 SE therapists, 12 individual drug counselors, and 10 group drug counselors participated in the clinical trial.

Treatments

MEASURES

The treatment approaches have been described in more detail in previous reports.21 Group drug counseling (GDC) followed a manual designed to educate patients about the recovery process from addiction, strongly encourage participation in 12-step programs, and provide a supportive group atmosphere for discussing the initiation of abstinence and establishing an alternative lifestyle.22 Individual drug counseling (IDC) followed a manual with speci®c stages, tasks, and goals based on the 12-step philosophy.23 Cognitive therapy (CT) followed a detailed manual for CT of substance abuse/dependence based on Beck’s cognitive model.24 Brief supportive-expressive (SE) psychodynamic psychotherapy was a modi®cation for cocaine dependence25 of the general SE treatment manual of Luborsky.26 Treatment consisted of a 6-month active phase and a 3-month booster phase. Individual treatment sessions (50 minutes)

Assessments were completed at intake, at the end of orientation, monthly during the active phase of treatment, and 9, 12, 15, and 18 months after randomization. The assessment battery addressed multiple domains, including drug use, psychiatric symptoms, and other areas of functioning (eg, employment, family/social, etc). Patients were paid a nominal fee for completing research assessments after intake. Demographic Variables

Basic demographic variables were gathered from a screening form designed for the study. For the purposes of analysis, the following variables were dichotomized: gender, race (minority vs. non-minority), employment (employed full- or part-time vs. unemployed) and living situation (living alone [single, divorced, separated] or with a partner [cohabiting or married]).


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Age and years of education completed were entered into the analysis as continuous variables.

and then averaging over the scales. This composite score was also used in the main outcome report.18

Drug Severity

Reason for Dropout

Drug severity was measured using the Addiction Severity Index (ASI)28 drug composite score and several individual ASI items: number of days of cocaine use in the past month at intake, years of cocaine use, and number of previous drug treatments. In addition, severity of drug use was measured by the presence of an alcohol- or cannabis-dependence disorder based on the Structured Clinical Interview for DSM-IV.29

The Reasons for Early Terminationö Patient and Therapist versions34 was used to assess the reasons and their degree of in¯uence for the patient’s decision to drop out. The measures assess both practical reasons (eg, transportation, time pressures) and treatment speci®c reasons (did not like treatment, therapist, or research program) for dropout. RESULTS

Psychiatric Severity Patient Sample

Psychiatric severity was assessed by a number of diåerent methods: Brief Symptom Inventoryö Global Severity Index score (BSIGSI);30 21-item Beck Depression Inventory (BDI);31 and the Hamilton Depression Rating Scale (HAM-D)32 utilizing the Structured Interview Guide (SIGH-D)33 to provide an interview-based assessment of severity of depression. The total score of the ®rst 17 items of the HAM-D was used in this analysis. The presence or absence of current DSM-IV Axis I cocaine-induced anxiety and depressive disorders and Axis II anti-social personality disorder (with and without childhood conduct disorder) were based on a trained evaluator’s administration of the Structured Clinical Interview for DSM-IV.29 To test the interaction of psychiatric severity and relevant demographic variables on time in treatment, a composite measure of all four psychiatric severity scales (Hamilton Rating Scale for Depression, Beck Anxiety Inventory, Brief Symptom Inventory, and the ASI Psychiatric Severity Composite score) was created by converting each scale to a standard score

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A total of 2197 patients were screened by telephone: 1777 (81%) met basic inclusion criteria and were invited for an intake visit, while 420 were ineligible for the study. Fifty-three percent (937) of eligible patients attended an intake visit, and most (870 patients) began orientation by attending another visit after the ®rst intake session. Of the 937 patients who came to the ®rst intake visit, 13 met exclusion criteria and 54 did not return. Of the 870 patients who started the orientation phase, 254 (29%) did not complete the attendance requirements, and 129 patients (15%) did not complete the assessment requirements, leaving a ®nal randomized sample of 487. The randomized patient sample (N = 487) was 58% Caucasian, 40% AfricanAmerican, and 2% Hispanic. Most patients were male (77%), lived alone (70%), were employed (60%), and had completed on average 13 years of schooling. The average age was 34 years old. The main mode of cocaine use was crack (79%), followed by intra-nasal (19%) or intravenous (2%) use. Patients were using cocaine 10 days and alcohol 7 days on average in the month


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prior to intake, and had been using cocaine for 7 years (SD = 4.8). Thirty-three percent met criteria for alcohol dependence, 4% cannabis dependence, and 17% cannabis abuse. Twenty-eight percent met criteria for a cocaine-induced mood disorder and 5% for cocaine-induced anxiety disorder. Fourteen percent of patients met full criteria for antisocial personality disorder (ASPD), while another 32% met criteria for ASPD as an adult with no history of childhood conduct disorder. Time in Treatment

The length of time in treatment was computed for each patient and de®ned as the number of days from randomization to the last contact with primary treatment provider (individual therapist or drug counselor). Of the 487 patients randomized to the individual treatments, there were 149 patients (31%) classi®ed as completers (completed at least 5 of the full 6 months of treatment) and 338 (69%) dropouts. Dropout rates by time and treatment condition were reported previously in the primary outcome report by Crits-Christoph et al.18 In addition to dropouts, 50 patients violated the protocol. Protocol violation was de®ned as patients receiving treatment in addition to the study protocol treatment, with the most common violations including inpatient substance abuse treatment (n = 33) or antidepressant medication (n = 8). There was a relatively even distribution across treatment conditions (11 in IDC, 14 in CT, 13 in SE, 12 in GDC). Survival Analysis of Days in Treatment for Randomized Patients

The data were analyzed using a time to event analysis,35 with days to last treatment contact as the dependent variable. The data were also analyzed using either time to

dropout or time to protocol violation (whichever came ®rst), and no signi®cant diåerences between the results of the two analyses were found. We report the results based on time to dropout only since we want to predict which patients chose not to continue in treatment and we have found diåerent predictors of dropout and protocol violation.36 In addition, a moderate percentage of the protocol violators in this study remained in treatment as permitted by the study after the violation. For the survival analysis, we have retained the p µ :05 as the test of signi®cance. Each patient’s treatment status was monitored until 180 days after randomization (the length of the active treatment phase). WHAT PRE-TREATMENT PATIENT VARIABLES, OR INTERACTIONS AMONG THESE VARIABLES, ARE ASSOCIATED WITH RETENTION IN TREATMENT? Main Effects

First, the eåects of all demographic variables (age, race, gender, employment, education, and living situation) on time in treatment were assessed using a Cox proportional hazards model.37 This analysis revealed that when age, race, employment status, years of education, and mode of cocaine use were entered as individual predictors, all had signi®cantly predicted time in treatment (see Table 1). The estimated days in treatment until 50% of patients dropped out was 79 days for minority patients compared to 115 for Caucasian patients; 77 days for unemployed compared to 117 days for employed patients; and 88 days for crack smokers/ IV compared to 134 days for nasal users. For each one-year increase in age, there was a 2.8% increase in the likelihood of completing treatment, and for each one-year increase in education completed,


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TABLE 1.

Survival Analysis: Individual Predictors of Days inTreatment

x square

Variable

p value

Gender Employment Living situation Race Age Education Mode of use

w2

(1) w2 (1) w2 (1) w2 (1) w2 (1) w2 (1) w2 (1)

= = = = = = =

0:40 8:52 1:46 8:12 10:94 10:28 4:49

0.52 0.003 0.22 0.004 0.001 0.001 0.03

Years cocaine # drug treatment ASI drug composite ASI days cocaine

w2 (1) w2 (1) w2 (1) w2 (1)

= = = =

0:07 1:95 3:07 1:55

0.78 0.16 0.08 0.21

Alcohol Dependence Cannabis Dependence Cocaine-Induced Mood disorder ASPD BDI BSI-GSI

w2 (1) w2 (1) w2 (1) w2 (1) w2 (1) w2 (1)

= = = = = =

0:097 2:90 0:33 4:4 1:47 0:51

0.75 0.09 0.57 0.04 0.23 0.47

Note: years cocaine = number of years cocaine use at intake Addiction Severity Index (ASI); # drug treatment = ASI number of previous drug abuse treatments; ASI drug composite = intake drug use composite; ASI days cocaine = intake ASI number of days used cocaine in past month; Alcohol Dep = alcohol dependence; ASPD = diagnosis anti-social personality disorder; BDI = Beck Depression Inventory; BSI-GSI = Brief Symptom Inventoryö Global Severity Index.

there was 8.1% increase in the likelihood of completing treatment. Treatment condition (Wald w2 = 7:6; df = 3; p = 0:05) was also a signi®cant predictor of days in treatment, with pairwise comparisons revealing that IDC ‡ GDC patients had fewer days in treatment than CT ‡ GDC or SE ‡ GDC patients (Wald w2 = 7:5; df = 2; p = 0:02). The estimated number of days until 50% of patients dropped out was 64 days for IDC ‡ GDC, 85 days for GDC alone, 120 days for SE ‡ GDC, and 114 days for CT ‡ GDC. Next, the psychiatric and drug severity variables were tested to see if they predicted days in treatment individually in the Cox regression model. None of the drug severity variables had an impact on time in treatment, nor did any of the self-report,

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or interviewer ratings of psychiatric symptoms or any Axis I diagnoses from the SCID. Of all psychiatric severity variables tested, only the presence of ASPD diagnosis signi®cantly predicted time in treatment. Both the full ASPD diagnosis with childhood conduct disorder (CD) (Wald w2 = 4:42; df = 1; p = :04) and the adult ASPD without childhood CD (Wald w2 = 4:06; df = 1; p = :04) were signi®cant when entered as sole predictors. Fifty percent of patients with ASPD diagnosis dropped out by 80 days, compared to 117 days for patients without the ASPD diagnosis. When all signi®cant individual predictors of dropout were entered together into the same model to control for the eåect of each variable on the others, race (Wald w2 = 12:47; df = 1; p < :001),


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employment (Wald w2 = 3:85; df = 1; p = :05), age (Wald w2 = 12:29; df = 1; p = :001), education (Wald w2 = 4:37; df = 1; p = :04) and treatment (Wald w2 = 8:45; df = 3; p = :04) remained signi®cant predictors. If either ASPD variable is entered into the model with the signi®cant demographic variables described above, it is no longer a signi®cant predictor or even a trend. Further investigation of multi-colinearity revealed that both the full and partial ASPD diagnoses are associated with mode of cocaine use (particularly crack use), age (younger), and employment status (unemployed). Therefore ASPD was not included in this ®nal model with the signi®cant demographics. Interactions Among Variables

Next, we tested for signi®cant interactions among these demographic variables, interactions of demographic variables with treatment condition, and interactions of demographic variables with drug or psychiatric severity on retention in treatment. We were interested in investigating whether multiple risk factors for retention could be identi®ed or whether certain patients were poorly retained in a particular type of treatment. None of the interactions of demographic variables with treatment condition were signi®cant, meaning there were no diåerences in retention across the demographic variables in the diåerent treatments. In terms of demographic by demographic interactions, there were three signi®cant interactions using Wald w2 of Cox proportional hazards model. First, there was a signi®cant marital status by race interaction (w2 (1) = 7:15; p = :01). A signi®cant gender by employment status interaction was also noted (w2 (1) = 4:89; p = :03). Finally, there was a gender by years of education interaction (w2 (1) =

9:42; p = :001) where both males and females with more education had better retention, though the diåerences were larger for females (see Table 2 for breakdown of days in treatment by demographics). Finally, we were interested in examining interactions of the demographic variables and treatment condition with drug and psychiatric severity variables in predicting time in treatment. The intake ASI drug use composite was used to test the interaction with drug severity. There were no signi®cant interactions of demographic variables or treatment and drug severity score on retention. The psychiatric severity interaction was tested using the composite psychiatric severity variable. The only signi®cant interaction with intake psychiatric severity in the survival analysis of time in treatment was gender (w2 = 4:16; p = :04). The odds ratio for males was .91 (CI = .78- 1.06) and for females was 1.31 (CI = .95- 1.79). For males, with each unit increase in the psychiatric severity composite, there is a 9% reduction in likelihood of dropping out of treatment; however, in females, there is a 31% increase in likelihood of dropping out with each unit increase in psychiatric severity. TABLE 2. Demographic by Demographic Interactions: Median Days inTreatment

Minority 91 40

Caucasian 99 137 Employed

Male Female

Unemployed 82 56

Male Female

High School Dropout 90 63

High School Graduate 111 180

Live alone Live with partner

103 148

Note: Table represents median days in treatment for each interaction.


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Retention in Treatment of Cocaine ARE THERE DIFFERENT PREDICTORS OF PATIENTS WHO CHOOSE TO LEAVE TREATMENT COMPARED TO THOSE WHO HAVE TO LEAVE TREATMENT DUE TO A MOVE, JAIL, OR HOSPITALIZATION?

In order to address this question, patients were classi®ed as leaving by choice or forced to leave treatment based on their response to Reasons for Early Termination Form. If patients endorsed that they left treatment because they moved out of the area or were jailed, injured, or hospitalized, they were designated as forced out of treatment. Fifty-one patients out of the total 338 patients that did not complete treatment were classi®ed as forced out of treatment (15% of all dropouts). There was a main eåect on retention time for reason for dropout with patients forced out retained in treatment an average of 35 days compared to 113 days for those patients leaving by their own choice (w2 (1) = 47:79; p < :001). This reason for dropout variable was entered in interaction with the demographic variables to see if there was an eåect on retention time in survival analysis. All interactions were nonsigni®cant, indicating no diåerential rate of retention across the demographic variables for patients who left by choice or were forced out of treatment. ARE THERE DIFFERENT PREDICTORS OF ATTRITION FOR PATIENTS WHO ARE USING DRUGS AFTER DROPOUT COMPARED TO THOSE WHO DO NOT CONTINUE TO USE?

Next, we investigated whether there were patient demographic or severity predictors for patients who continue to use cocaine after dropout as compared to those who do not. Patients are de®ned as not using if they have no admitted days of cocaine use in the past 30 days as measured by the ASI.

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Patients are de®ned as using if they have any admitted days of cocaine use in the past 30 days as measured by the ASI. Of the eight monthly assessments (months 1- 6, 9, 12), patients completed 6.1 assessments on average with no signi®cant diåerences between treatment conditions (CochranMantel-Haenszel w2 (3) = 1:33; p = 0:72) despite the diåerences in dropout rates. At least one of the six active phase post-intake ASI monthly assessments was obtained on 94% of the 487 patients randomized, and 85% of randomized patients completed either a month 5 or month 6 assessment. While it would be ideal to be able to con®rm the self-reports of drug use by urine results, regular urines cannot be obtained from patients who are no longer in treatment. Any analysis of post dropout use, therefore, is limited to self-report data; or otherwise, the results are biased by requiring that patients be coded as nonabstinent when urines are not available. In this study, the correlation between the percentage of cocaine-free urine tests over the ®rst four weeks of treatment and the report of cocaine use in the past 30 days from the ASI was .64 ( p < :0001). We also compared the weekly self-report of cocaine use with urines during the ®rst month of treatment, coding the month as ``nonabstinent’’ if any cocaine was used. The kappa coe¬cient for this comparison was 0.64. Sensitivity (conditional agreement given a drug-negative urine) was 0.74, and speci®city (conditional agreement given a drug-positive urine) was 0.90. Thus, 10% of the urine results indicated some use when the patient denied any use. The ®rst available post-dropout ASI assessment was coded as abstinent or non-abstinent. Post-dropout outcome data were available for 85% (299/354) of patients who dropped out. A signi®cant number of patients (126/299, 42%) were abstinent at the ®rst post-dropout assessment. A logistic regression model


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predicting post-dropout outcome adjusted for pre-dropout outcome was used. Pre-dropout outcome was de®ned as abstinent or not abstinent the month before dropout. We decided to adjust for pre-dropout outcome because we felt this would be a better test of the impact of dropout on subsequent outcome and because there was a signi®cant association between the pre-dropout and post-dropout measures (w2 (1) = 62:3; p = :001, 74% agreement between the two measures). Of the demographic and severity variables tested, baseline BDI (t (272) = – 2.34, p = .02) and BSI (t (273) = – 2.13, p = .03) were signi®cant predictors of post-dropout outcome adjusting for predropout outcome. For each unit increase in BDI score, there was a 4% lower probability of having an abstinent post-dropout outcome, and for each unit increase in BSI score, there was a 43% lower probability of having an abstinent post-dropout outcome. Treatment condition was also a signi®cant predictor of an abstinent post-dropout assessment, F (3, 275) = 4.13, p = .01. Sixty-four percent of IDC patients had an abstinent post-dropout assessment compared to 45% of SE patients, 46% of GDC patients, and 33% of CT patients. Post hoc analyses revealed signi®cant diåerence between IDC and all three other treatment conditions. WHAT IS THE RELATION OF TIME IN TREATMENT TO OUTCOME, AND DOES THIS RELATION VARY BY TREATMENT TYPE?

Even though there were patient factors that aåected time in treatment, it is important to understand the impact of time in treatment on outcome. Two diåerent approaches were used to address this question. First, the correlation between days in treatment and drug use outcome using the ASI drug composite and days

using cocaine variable both at month 6 and averaged across the 6 months was examined. The partial correlations (controlling for intake drug composite scores) were not signi®cantly diåerent from zero and were quite small ranging from r = – .04 for time in treatment and Month 6 ASI drug composite score to r = – .03 for time in treatment and average drug composite score Month 1- 6. Second, to determine if there were a signi®cant diåerence on outcome for completers versus dropouts, we included a binary indicator of completer status to the general linear mixed model analysis of previously identi®ed signi®cant predictors of outcome as reported in Crits-Christoph et al.18 These signi®cant predictors are: baseline drug usage as measured by the ASI drug composite, baseline psychiatric composite score, baseline sociopathy score, and site.18 Completers were de®ned as those patients who completed 5 months or more of the proposed 6-month active treatment phase. This de®nition of completer was chosen in this study both to match the de®nition used in the main outcome report18 and because the potential 20 out of 24 sessions attended represented an adequate dose of treatment. A signi®cant main eåect for completer status (F (1,447) = 6.41, p = 0.01) was found in which dropouts, on average, had higher average drug usage from month 1 through month 6 than completers. Estimates of the average ASI drug use composite score over month 1 to month 6, adjusted for all covariates, were completers, 0.12; and dropouts, 0.13; with a pooled standard deviation of 0.09. However, if the data are examined cross-sectionally, comparing drug use outcome for patients still in treatment or dropped out at each month, there is no signi®cant diåerence in drug use outcome between dropouts and completers at each month. In order to test if this diåerence between completers and dropouts was con-


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sistent across treatment condition, month, and treatment by month, a two-way interaction with completer status by treatment condition was included in general linear mixed model. Then, all two-way interactions and a three-way interaction of treatment condition, month, and completer status were included in the model. A signi®cant treatment condition by completer status interaction on outcome was found (F (3, 444) = 2.56, p = :05), indicating the diåerence between completers and dropouts on outcome was diåerent across the four treatments. In order to investigate the interaction, pairwise contrasts were performed indicating signi®cant diåerences between completers and dropouts for SE only. Table 3 illustrates the diåerences in ASI drug use composite scores between completers and dropouts across the four treatments adjusting for all covariates. In order to examine consistency in results with the self-report data, the analyses above were also tested using the composite cocaine score as outcome. The composite cocaine use outcome measure was constructed by pooling information across multiple measures (urines, ASI, weekly self-reports of cocaine use) to code each month of treatment as abstinent versus any cocaine use. Any indication of cocaine use across the three measures would lead to a ``not abstinent’’ month. If TABLE 3. Average Addiction Severity Index Drug Outcome (Month 1- 6) by Dropout Status and Treatment Condition

IDC Dropout Completer

a

0.11 0.11a

CT

SE a

0.13 0.13a

GDC a

0.15 0.11b

0.14a 0.12a

Note: Means followed by the same letter are not statistically diåerent ( p > 0:05). IDC = individual drug counseling; CT = cognitive therapy; SE = supportive-expressive therapy; GDC = group drug counseling. Dropout = patients who completed 5 months or less of therapy. Completer = patients who completed 5 months or more of treatment.

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no information was available for a given month (19% of the time), the month was coded as ``not abstinent.’’ This analysis con®rms the results using the ASI composite score of a signi®cant completer status eåect, indicating completers had signi®cantly better average outcome than dropouts. Again there was no diåerence between completers and dropouts on composite outcome compared crosssectionally at each month. However, with the cocaine composite, the three-way interaction of completer status, month, and treatment condition was not signi®cant likely because the outcome is binary (abstinent or not abstinent), limiting power. DISCUSSION

In this multi-center clinical trial of psychosocial treatments for cocaine dependence, age, race, employment status, education, mode of cocaine use, and treatment condition were signi®cant predictors of retention in treatment. Though ®ndings have been contradictory regarding the importance of patient demographic predictors, these results are consistent with the studies that have found signi®cant predictors.eg, 5,7- 9 Speci®cally, younger, minority, unemployed, and less educated patients stayed in treatment for less time. Interestingly, living situation had a diåerent impact on retention for this study’s African-American patients than Caucasian patients: African-American patients spent more time in treatment if they lived alone than if they lived with a partner, while the opposite was true for Caucasian patients. It may be that patients only stay in treatment longer if the partner or spouse they live with is supportive of treatment. Even though we do not have data on drug use by the patient’s partner or spouse, many of our African-American patients reported that it was di¬cult to continue with treatment and stay clean


Siqueland et al.

when many of the people in their living and social environment continued to use drugs. This ®nding might suggest involving patients’ signi®cant others to support the goals of treatment, identifying signi®cant others who are impeding treatment, or helping patients identify other support systems to promote abstinence. Two other interesting interactions among the demographic variables indicate some of the complexity of retaining women in substance dependence treatment. Overall, gender did not predict retention in treatment. However, unemployed males were retained in treatment longer than unemployed females (82 vs. 56 days), but employed females had higher retention rates than employed males (148 vs. 103 days). While both males and females with more education had better retention rates than patients with less education, the diåerence was larger for females. These ®ndings suggest that the unemployed female with less education may require treatment interventions that target her other psychosocial needs, such as employment and ®nancial concerns, to retain her in treatment. Finally, while higher intake psychiatric severity scores meant men were retained in treatment longer, higher psychiatric severity put women at risk for dropping out sooner. These interactions among variables are important to study since, like gender, psychiatric severity was not a predictor of dropout on its own. In general, the existing literature suggests that comorbid psychiatric symptoms usually keep patients in treatment, but these ®ndings may have been based on samples that were predominantly male.5,10 None of the measures of drug severity or psychiatric severity alone predicted time in treatment. The more severe drug users stayed in treatment as long as less severe users. Anti-social personality disorder (with and without childhood conduct disorder) appeared to put patients at higher risk for dropping out; however, these variables

were no longer signi®cant predictors when demographic variables were controlled for. Multi-colinearity between many of the variables associated with time in treatment may contribute to the inconsistent ®ndings regarding ASPD reported in the literature. It may be important to diåerentiate patients who choose to leave treatment and those who are forced out because of a move, being jailed, or hospitalized, because these latter patients might return to treatment at some later point. However, these patients could not be diåerentiated from patients who left on their own based on demographic or severity characteristics alone. In contrast to the pilot/training phase of the same study, where we found that only younger patients dropped out sooner and patients with comordid psychiatric symptoms stayed in treatment longer,16 there were more demographic predictors of retention in this clinical trial. The clinical trial described in this report included an orientation phase that required three visits to the clinic prior to randomization, while the pilot phase required that patients be able to achieve a week of abstinence from cocaine prior to randomization, perhaps leading to diåerent patient samples. However, in order to achieve a week of abstinence in the pilot phase, patients were required to show up for three or more visits at the clinic to give urine samples. It may have been that severity of cocaine use was such a potent predictor of dropout in early abstinence phase of the pilot study16 that it limited the predictive power of other variables, whereas the motivational hurdle of the clinical trial may have required engagement with treatment staå that was more di¬cult for certain at-risk patients (eg, unemployed, African-American, etc.). The motivational interviewing approacheseg, 38 and other engagement strategies may need to be designed for these groups. Overall, patients who completed treatment had better outcome than patients


35


who did not. However, the minimal diåerences in average ASI outcome between completers and dropouts and the lack of signi®cant eåects between the two groups at each month suggest that there is a minimal relationship between time in treatment and outcome for cocaine patients in this study. This is consistent with other studies, where there seems to be a weaker association between time in treatment and outcome for cocaine-dependent patients.5 In contrast, there is quite a strong eåect in opiate dependent samples as patients who stay in treatment 90 days or more have signi®cantly better outcome.2- 4 Patients in the psychotherapy conditions stayed in treatment signi®cantly longer than patients in the drug counseling conditions. However, patients in the combination of individual and group drug counseling had better drug use outcome than the patients in psychotherapy despite these signi®cantly lower retention rates.18 Patients in the more structured treatments, cognitive therapy, and individual drug counseling did equally well if they completed treatment or dropped out, while for the more exploratory treatment, supportive expressive therapy, patients needed more time in treatment to show bene®cial eåects on drug use outcome. This study allows comparisons of retention rates from the shorter 3-month treatment protocols5,10 and longer 6-month protocols for cocaine dependence.39,40 The ®ndings of this study raise the empirical question of whether dropout always signi®es a poor outcome in cocaine-dependent samples. Although patients who completed the full course of treatment had better drug use outcome than patients who left early, many patients dropped out when they were doing well and continued to stay abstinent for at least a number of months after leaving treatment. More than half of the patients in the individual drug counseling condition dropped out by 65 days and still had better

36

outcome overall. However, patients in individual drug counseling were more likely to be clean after dropout than patients in the other treatment conditions, suggesting that individual drug counseling may provide patients with skills that help to maintain abstinence after treatment ends. Individual drug counseling may provide information as well as speci®c techniques and strategies for managing craving or relapse. In addition, drug counseling approaches push patients toward more involvement in self-help programs that may help maintain abstinence.41 Patients with higher baseline psychiatric severity scores are more at risk for having poor outcome after they drop out of treatment, especially if patients demonstrate a high level of general psychiatric distress. It may be particularly important to retain these patients in treatment until its completion or try to re-engage them in treatment if they drop out. Their outcome might be signi®cantly improved if their psychiatric symptoms are stabilized. These results are consistent with the ®ndings of the main study report that higher psychiatric severity is an overall predictor of worse outcome.18 Unfortunately, patients who drop out and remain abstinent could not be diåerentiated from patients who continued to use based on any other pre-treatment demographic or severity variables. Failure to assess outcome after dropout, especially in the presence of diåerent attrition rates across treatments, can lead to distortion or biases in outcome results because missing data across are non-random.42 One treatment condition can appear to be most eåective because the patients that remain and provide data appear to be functioning well; however, a majority of patients might have dropped out of that condition because they were doing poorly. Or, as seen in this study, dropouts from treatment may fare diåerently depending on their initial treatment assignment.


Siqueland et al.

The results for this report also need to be interpreted taking into account which patients left treatment before randomization during the intake and orientation phase. Siqueland et al43 reported that AfricanAmerican patients and unemployed patients were less likely to keep their intake appointment and to be randomized to treatment. The biggest diåerences were found on which patients showed up for intake. In contrast, younger patients were less likely to show up for an intake appointment or to be randomized to treatment; however, larger diåerences were found at randomization. When looking at the results from the current report and the pre-randomization study described above, minority patients, unemployed patients, and younger patients are less likely to both become engaged and to remain in treatment. Therefore, it could be that the younger patients, minority patients, and unemployed patients randomized to treatment in this study represent a particularly motivated group of patients who persevered with treatment. Severity of drug use is a potent predictor of showing up for an intake visit but less important once treatment has begun. These diåerent ®ndings at diåerent stages of engagement suggest that alternative strategies may be needed to increase retention at the diåerent phases of treatment. There are several limitations of the NIDA CCTS that are relevant to this report. First, the orientation phase, which required a demonstration of minimal motivation for treatment prior to randomization, could limit generalizability to other studies and patient samples. Patients did have to complete visit requirements and assessment procedures that were possible burdens for some patients. However, patients began their clinical contact on the ®rst visit to the clinic and met with drug counselors one to two times a week during the orientation phase. Patients usually began their randomized treatment within 1- 2 weeks of their intake visit. Wõth

this level of clinical contact in the orientation phase, patients did not report concerns about delays in ``treatment.’’ Second, excluding patients who were on medication, with imminent suicide risk, or with bipolar disorder likely limited the range of psychiatric severity in the CCTS population. Finally, post-dropout data were limited to self-report outcome, which could lead to some under-reporting of use by some patients. The strength of the CCTS design is that it allowed for careful tracking of the number and types of patients who dropped out at the various phases of treatment. This systematic approach provides important information rarely reported regarding which patients ®rst become engaged in and then stay in treatment to allow for appropriate interpretation of results. The preparation of this manuscript was funded in part by grants U01-DA07090 (Dr. CritsChristoph), U01-DA07663 (Dr. Frank), U01-DA07673 (Michael Thase, M.D.), U01DA07693 (Roger D. Weiss, M.D.), and U01-DA07085 (Dr. Luborsky) from the National Institute on Drug Abuse (NIDA), Bethesda, Md., and NIDA Career Development Award K05DA00168 (Dr. Luborsky), NIDA Career Development Award K02-DA 00326 (Roger D. Weiss, M.D.), NIMH Clinical Research Center P30-MH-45178 (Dr. Crits-Christoph), and NIMH Career Development Award K02MH00756 (Dr. Crits-Christoph) The NIDA Collaborative Cocaine Treatment Study is a National Institute on Drug Abuse-funded Cooperative Agreement involving four clinical sites, a Coordinating Center, and NIDA staå. The Coordinating Center at the University of Pennsylvania includes: P. Crits-Christoph, Ph.D. (PI), L. Siqueland, Ph.D. (Project Coordinator), K. Moras, Ph.D. (Assessment Unit Director), J. Chittams, M.A. (Director of Data Management/Analysis), and L. Muenz, Ph.D. (Statistician). The collaborating scientists at the Treatment Research Branch, Division of Clinical


37


and Research Services at NIDA include J. Blaine, M.D. and L.S. Onken, Ph.D. The four participating clinical sites are: University of Pennsylvaniaö L. Luborsky, Ph.D. (PI), J.P. Barber, Ph.D. (CO-PI), D. Mercer, Ph.D. (Project Director); Brookside Hospital/Harvard Medical Schoolö A. Frank, Ph.D. (PI), S.F. Butler, Ph.D. (CO-PI/Innovative Training Systems), S. Bishop, M.A (Project Director); McLean/Massachusetts General Hospital- Harvard University Medical Schoolö R.D. Weiss, M.D. (PI), D.R. Gastfriend, M.D. (CO-PI), L.M. Najavits, Ph.D., and M.L. Gri¬n, Ph.D. (Project Directors); University of Pittsburgh/Western Psychiatric Institute and Clinicö M.E. Thase, M.D. (PI), D. Daley, M.S.W. (CO-PI), I.M. Salloum, M.D. (CO-PI), and J. Lis, M.S.N. (Project Director). The Training Unit includes Heads of Cognitive

Therapy Training Unit: A.T. Beck, M.D. (University of Pennsylvania) and B. Liese, Ph.D. (University of Kansas Medical Center); Heads of Supportive-Expressive Therapy Training Unit: L. Luborsky, Ph.D. and D. Mark, Ph.D. (University of Pennsylvania); Heads of the Individual Drug Counseling: G. Woody, M.D. (Veterans Administration/University of Pennsylvania Medical School); and Group Drug Counseling Unit: D. Mercer (Head), D. Daley (Assistant Head; University of Pittsburgh/Western Psychiatric Institute and Clinic), and G. Carpenter, M.Ed., (Assistant Head; Treatment Research Unit- University of Pennsylvania. The Monitoring Board includes L. Beutler, Ph.D., J. Klett, Ph.D., B. Rounsaville, M.D., and T. Shea, Ph.D. The contributions of J. Boren, Ph.D. and D. Grossman, M.A., NIDA, the project o¬cer for this cooperative agreement, are also gratefully acknowledged.

REFERENCES

1. Stark MJ. Dropping out of substance abuse treatment: a clinically oriented review. Clin Psychol Rev. 1992;12:93- 116. 2. Hubbard RL, Marsden ME, Rachal JV, Harwood HJ, Cavanaugh ER, Ginzburg HM. Drug Abuse Treatment: A National Study of Eåectiveness. Chapel Hill, NC: University of North Carolina Press; 1989. 3. Simpson DD. Treatment of drug abuse: follow-up outcome and length of time spent. Arch Gen Psychiatry. 1979;38:875- 880. 4. Simpson DD. The relation of time spent in drug abuse treatment to posttreatment outcome. Am J Psychiatry. 1981;136:1449- 1453. 5. Carroll KM, Rounsaville BJ, Gordon LT, Nich C, Jatlow P, Bisighini RM, Gawin FH. Psychotherapy and pharmacotherapy for ambulatory cocaine abusers. Arch Gen Psychiatry. 1994;51:177- 187. 6. Gainey RR, Wells EA, Hawkins JD, Catalano RF. Predicting treatment retention among cocaine users. Int J Addict. 1993;28(6):487- 505. 7. Kleinman PH, Kang S, Lipton DS, Woody GE, Kemp J, Todd TC, Millman RB. Retention of cocaine abusers in outpatient psychotherapy. Am J Drug Alcohol Abuse. 1992;18(1):29- 43. 8. Agosti V, Nunes E, Stewart JW, Quitkin FM. Patient factors related to early attrition from

38

9.

10.

11.

12.

13.

14.

outpatient research clinics: a preliminary report. Int J Addict. 1991;26:327- 334. Means LB, Small M, Capone DM, Capone TJ, Condren R, Peterson M, Hayward B. Client demographics and outcome in outpatient cocaine treatment. Int J Addict. 1989; 24:765- 783. Carroll KM, Rounsaville BJ, Gawin FH. A comparative trial of psychotherapies for ambulatory cocaine abusers: relapse prevention and interpersonal psychotherapy. Am J Drug Alcohol Abuse. 1991;17:229- 247. Gawin FH, Kleber HD, Byck R, Rounsaville BJ, Kosten TR, Jatlow PI, Morgan C. Desipramine facilitation of intitial cocaine abstinence. Arch Gen Psychiatry. 1989;46: 117-121. Hoåman JA, Caudill BD, Koman JJ, Luckey JW, Flynn PM, Hubbard RL. Comparative cocaine abuse treatment strategies: enhancing client retention and treatment exposure. J Addict Dis. 1994;13(4):115- 128. Stark MJ, Campbell BK. Personality, drug use, and early attritio n from substance abuse treatment. Am J Drug Alcohol Abuse. 1988; 14:475- 487. Steer RA. Retention in drug free counseling. Int J Addict. 1983;18:1109- 1114.


Siqueland et al.

15. Carroll KM, Power MD, Bryant K, Rounsaville BJ. One year follow-up status of treatmentseeking cocaine abusers: psychopathology and dependence severity as predictors of outcome. J Nerv Ment Dis. 1993;181(2):71- 79. 16. Siqueland L, Crits-Christop h P, Frank A, Daley D, Weiss RD, Chittams J, Blaine U, Luborsky L. Predictors of dropout from psychosocial treatment of cocaine dependence. Drug Alcohol Depend. 1998a;52(1):1- 13. 17. Woody GE, McLellan AT, Luborsky L, O’Brien CP. Sociopathy and psychotherapy outcome. Arch Gen Psychiatry. 1985;42: 1081-1086. 18. Crits-Christop h P, Siqueland L, Blaine J, Frank A, Luborsky L, Onken LS, Muenz L, Thase ME, Weiss RD, Gastfriend DR, Woody G, Barber JP, Butler SF, Daley D, Bishop S, Najavits LM, Lis J, Mercer D, Gri¬n ML, Moras K, Beck A. Psychosocial treatments for cocaine dependence: results of the NIDA Cocaine Collaborative Study. Arch Gen Psychiatry. 1999; 56:493- 502. 19. DiNardo PA, Barlow DH Anxiety Disorders Interview Scheduleö Revised (ADISö R). Albany, NY: Phobia and Anxiety Disorders Clinic; 1988. 20. Wei, LJ. An application of an urn model to the design of sequential controlled trials. J Amer Stat Assoc. 1978;73:559- 563. 21. Crits-Christop h P, Siqueland L, Blaine J, Frank A, Luborsky L, Onken L, Muenz L, Thase M, Weiss R, Gastfriend D, Woody G, Barber J, Butler S, Daley D, Salloum IM, Bishop S, Lis J, Mercer D, Najavits L, Beck A, Moras K. The NIDA Collaborative Cocaine Treatment Study: rationale & methods. Arch Gen Psychiatry. 1977;54:721- 726. 22. Mercer D, Carpenter G, Daley D, Patterson C, Volpicelli J. Addiction Recovery Manual. Vol 2. Philadelphia, Pa: Treatment Research Unit, University of Pennsylvania; 1994. 23. Mercer D, Woody G. Addiction Counseling. Philadelphia, Pa: Center for Studies of Addiction, University of Pennsylvania/Philadelphia VAMC; 1992. 24. Beck AT, Wright FD, Newman CF, Liese BS. Cognitive Therapy of Substance Abuse. New York: Guilford Press; 1993. 25. Mark D, Luborsky L. A Manual for the Use of Supportive-Expressive Psychotherapy in the Treatment of Cocaine Abuse. Philadelphia, Pa: Department of Psychiatry, University of Pennsylvania; 1992. 26. Luborsky L. Principles of Psychoanalytic Psychotherapy: A Manual forSupportive-Expressive Treatment. New York: Basic Books; 1984.

27. Crits-Christop h P, Siqueland L, Chittams J, Barber JP, Beck A, Liese B, Luborsky L, Mark D, Mercer D, Simon Onken L, Najavits LM, Thase ME, Woody G. Training in cognitive therapy, supportive-expressive therapy, and drug counseling therapies for cocaine dependence. J Consult Clin Psychol. 1998; 66(3):484- 492. 28. McLellan AT, Kushner H, Metzger D, Peters R, Smith I, Grissom G, Pettinati H, Argeriou M. Addiction Severity Index. 5th ed. J Subst Abuse Treat. 1992;9:199- 213. 29. First M, Spitzer RL, Gibbon M, Wõlliams JBW. Structured Interview for Axis I and II for DSM-IV. New York: New York Psychiatric Center; 1994. 30. Derogatis LR. Brief Symptom Inventory. Baltimore, MD. Clinical Psychometric Research Incorporated; 1992. 31. Beck AT, Steer RA, Garbin MG. Psychometric properties of the Beck Depression Inventory: twenty-®ve years later. Clin Psychol Rev. 1988;8: 77- 100. 32. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56- 62. 33. Wõlliams JBW. A structured interview guide for the Hamilton Depression Rating Scale. Arch Gen Psychiatry. 1988;45:742- 747. 34. Moras K. Reasons for Ending Formö Patient and Therapist Version. Unpublished scale. Philadelphia, Pa.: University of Pennsylvania; 1992. 35. Cox DR, Oakes D. Analysis of Survival Data. London: Chapman & Hill; 1984. 36. Siqueland L, Frank A, Gastfriend D, Muenz L, Crits-Christop h P, Chittams J, Thase M, Mercer D. Protocol deviator: characterization and implications for clinical trials research. Psychother Res. 1986b;8(3):287- 306. 37. Cox DR. Regression models and life tables. Journal of the Royal Statistical Society. 1972;34: 187-220. 38. Miller WR, Rollnick S. Motivational Interviewing: Preparing People to Change Addictive Behavior. New York: Guilford Press; 1991. 39. Higgins ST, Budney AJ, Bickel WK, Hughes JR, Foeg F, Badger G. Achieving cocaine abstinence with a behavioral approach. Am J Psych. 1993;150:763- 769. 40. Higgins ST, Budney AJ, Bickel WK, Badger G. Participation of signi®cant others in outpatient behavioral treatment predicts greater cocaine abstinence. Am J Drug Alcohol Abuse. 1994; 20(1):47- 56. 41. Weiss RD, Gri¬n ML, Gallop R, Onken LS, Gastfriend DR, Daley D, Crits-Christop h P, Bishop S, Barber JP. Frequency and patterns of self-help group attendance and participation


39


in cocaine-dependent patients over time. Drug Alcohol Depend. 2000;60:169- 177. 42. Lavori PW. Clinical trials in psychiatry: should protocol deviation censor patient data? Neuropsychopharmacology. 1992;6(1):39- 48.

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43. Siqueland L, Crits-Christop h P, Gallop B, Gastfriend D, Lis J, Frank A, Gri¬n M, Blaine J, Luborsky L. Who starts treatment: Engagement in the NIDA Collaborative Cocaine Treatment Study. Am J Addict. 2002; 11(1):10- 23.
