ORIGINAL ARTICLE
Extracting numerical data from published reports of pharmacokinetics investigations: method description and validation Irismar Reis de Oliveiraa*, Roge´rio Santos-Jesusa, Alain Li Wan Pob, Nalinee Poolsupb a
Department of Neuropsychiatry, Faculty of Medicine, Federal University of Bahia, CEP 40110-100, Vale do Canela, Salvador-Bahia, Brazil b Centre for Evidence-Based Pharmacotherapy, Aston University, Aston Triangle, Birmingham B4 7ET, UK
Keywords graphs, meta-analysis, haloperidol
Received 6 February 2002; revised 6 November 2002; accepted 17 January 2003
ABSTRACT
A method has been proposed for extracting numerical data when only graphical results are presented. Reports with both graphical and tabular data were identified and the graphs were electronically scanned. The coordinates of each point were read using the cross-hair facility of Adobe Photoshop 7.0. To improve the precision of these coordinates, each point was read at 1600% magnification. The agreement between the observers was almost perfect (R > 0.99). The proposed method makes possible use of data in meta-analyses that, would otherwise be discarded.
Abstract at http://www. biomedcentral.com/abstracts/ cochrane/1/pc139/ (9th International Cochrane Colloquium, Lyon, France, 9–13 October 2001).
*Correspondence and reprints:
[email protected]
Authors often present data in graphical form only. Discarding graphical data is a waste of useful information. Therefore, reliable methods for abstracting such information from research reports are important but do not appear to have been reported in the literature. The application of the proposed method has been illustrated by using three published reports [1,2,3] identified from a previous meta-analysis [4], investigating the relationship between plasma level of haloperidol and clinical response. Each graph, electronically scanned, was analyzed using Adobe Photoshop 7.0 (Adobe Systems Incorporated, USA). A typical graph would include a series of points, expressed in the present example by drug blood level as the X axis and clinical outcome, notably the Brief Psychiatric Rating Scale (BPRS) score, as percentage improvement, on the Y axis. The coordinates of each point were read using the
cross-hair facility of Adobe Photoshop 7.0. To improve the precision of these coordinates four readings were taken for each point at 1600% magnification. The two extreme X values and the two extreme Y values were read for each point. The coordinates of the point were taken as the average of the two X values and the two Y values. To overcome possible double counting, each point was erased immediately after reading of the coordinates. The coordinates of each point were fed into a database (Excel 2000) and then transformed again into BPRS score and blood level. Agreement between data retrieved from the graphs, as well as agreement between such data and the values provided by the authors, were assessed using an intraclass correlation coefficient R [5]. Figure 1 is an illustration of the process used in this analysis. Once a point has been identified in the graph,
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Percentage of improvement
50 40 30 20 10 0 –10 –20
0
Figure 1 Illustration of the process of extracting numerical data. The box magnified contains points representing three patients.
5 10 15 20 25 30 35 40 45 50 55 ng/mL
Table I Intraclass correlation coefficients comparing original data obtained by two observers and those provided in graphs. Intraclass correlation (R) References
Ob.1 · Original
Ob. 2 · Original
Ob.1 · Ob. 2
Linkowsky et al. [1] Concentration
0.985
0.985
1.000
Response
0.996
0.996
1.000
Smith et al. [2] Concentration
0.999
0.999
0.999
Response
0.999
0.999
1.000
Kelly et al. [3] Concentration
0.999
0.999
1.000
Response
0.999
0.999
1.000
table supplied by the authors. Values obtained from this graph by observers 1 and 2 demonstrated a perfect agreement (R ¼ 1.00). Unfortunately, data presented by authors in graphical form only are often discarded, for instance, in metaanalyses. This is a waste of useful information. This method provides a means for reliably obtaining numerical data from graphs. The computerized method of extracting numerical data from graphs should be applicable to other situations such as when mean and standard deviation values are reported in graphical form only or when boxplots are reported. Examples of these applications are available from the first author on request. REFERENCES
its coordinates were read by magnifying it up to 1600% (detail amplified in the box of the Figure 1). Table I displays agreement between data retrieved from the graphs, as well as the agreement between such data and the values provided by the authors in three different publications, which provided data in both tabular and graphical form. The agreement between the observers, in all the circunstances, was almost perfect (intraclass correlation coefficient R > 0.99). The only exception corresponded to the smaller agreement between observers 1 and 2 and the original data obtained from the study by Linkowski et al. [1]. However, inspection of the plot clearly shows that some of the points do not correspond to the information provided by the authors in the table reported in the same paper. There was clearly a mistake in the data reported either in the graph or the
1 Linkowski P., Hubain P., von Frenckell R., Mendlewicz J. Haloperidol plasma levels and clinical response in paranoid schizophrenics. Eur. Arch. Psychiatr. Neurol. Sci. (1984) 234 231–236. 2 Smith R.C., Baumgartner R., Misra C.H. et al. Plasma levels and prolactin response as predictors of clinical improvement in schizophrenia: chemical v. radioreceptor plasma level assays. Arch. Gen. Psychiatry (1984) 41 1044–1049. 3 Kelly M.W., Perry P.J., Coryell W.H., Miller D.D., Arndt S.V. Reduced haloperidol plasma concentration and clinical response in acute exacerbations of schizophrenia. Psychopharmacol. (1990) 102 514–520. 4 De-Oliveira I.R., de-Sena E.P., Pereira E.L.A. et al. Haloperidol blood levels and clinical outcome: a meta-analysis of studies relevant to testing the therapeutic window hypothesis. J. Clin. Pharm. Ther. (1996) 21 229–236. 5 Fermanian J. l’accord entre deux juges: cas quantitatif. Rev. d’Epide´miol. Sante´ Publique (1984) 32 408–413.
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