Abstract
When published, a randomized parallel-group drug trial essentially includes a table listing all of the factors, otherwise called baseline characteristics, known possibly to influence outcome. E.g., in case of heart disease these will probably include apart from age and gender, the prevalence in each group of diabetes, hypertension, cholesterol levels, smoking history, other cardiovascular comorbidities, and concomitant medications. If the prevalence of such factors is similar in the two groups, then we can attribute any difference in outcome to the effect of test-treatment over reference-treatment. However, if this is not the case, we have a problem which can be illustrated by an example. Figure 28.1 shows the results of a study where the treatment effects are better in the males than they are in the females. This difference in efficacy does not influence the overall assessment as long as the numbers of males and females in the treatment comparison are equally distributed. If, however, many females received the new treatment, and many males received the control treatment, a peculiar effect on the overall data analysis is observed: the overall regression line is close to horizontal, giving rise to the erroneous conclusion that no difference in efficacy exists between treatment and control. This phenomenon is called confounding, and may have a profound effect on the outcome of a trial. In randomized controlled trials confounding is, traditionally, considered to play a minor role in the data. The randomization ensures that no covariate of the efficacy variable is associated with the randomized treatment (Cleophas et al. 2006a). However, the randomization may fail for one or more variables, making such variables confounders. Then, adjustment of the efficacy estimate should be attempted. Methods include subclassification (Cochran 1968), regression modeling (Cleophas et al. 2006a), and propensity scores (Rosenbaum and Rubin 1983; Rubin 1997). This chapter reviews these three methods and uses hypothesized and real data examples for that purpose.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Beal SL, Sheiner LB (1996) A note on the use of Laplace’s approximations for non-linear mixed-effects models. Biometrika 83:447–452
Begg CB (2000) Commentary: ruminations on the intent to treat. Control Clin Trials 21:241–243
Boeckman AJ, Sheiner LB, Beal SL (1984) NONMEM user guide: part V. NONMEM Project Group, University of California, San Francisco
Cleophas TJ, Tuinenburg E, Van der Meulen J, Kauw FH (1996) Wine drinking and other dietary characteristics in males under 60 before and after acute myocardial infarction. Angiology 47:789–796
Cleophas TJ, Zwinderman AH, Cleophas AF (2006a) Statistics applied to clinical trials. Springer, New York, pp 141–150
Cleophas TJ, Zwinderman AH, Cleophas AF (2006b) Statistics applied to clinical trials. Springer, New York, pp 329–336
Cochran WG (1968) The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24:295–313
Hullsiek KH, Louis TA (2002) Propensity scores modeling strategies for the causal analysis of observational data. Biostatistics 3:179–193
Rosenbaum P, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
Rubin DB (1997) Estimating causal effects from large data sets using propensity score. Ann Intern Med 127:757–763
SAS. http://www.prw.le.ac.uk/epidemiol/personal/ajs22/meta/macros.sas. Accessed 15 Dec 2011
Sobb M, Cleophas TJ, Hadj-Chaib A, Zwinderman AH (2008) Clinical trials: odds ratios, why to assess them, and how to do so. Am J Ther 15:44–53
Soledad Cepeda M, Boston R, Farrer JT, Strom BL (2003) Comparison of logistic regression versus propensity scores when the number of events is low and there are multiple confounders. Am J Epidemiol 158:280–287
SPSS Statistical Software. http://www.spss.com. Accessed 15 Dec 2011
Wickramaratne PJ, Holford TR (1987) Confounding in epidemiological studies: the adequacy of the control groups as a measure of confounding. Biometrics 43:751–765
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Cleophas, T.J., Zwinderman, A.H. (2012). Confounding. In: Statistics Applied to Clinical Studies. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2863-9_28
Download citation
DOI: https://doi.org/10.1007/978-94-007-2863-9_28
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-2862-2
Online ISBN: 978-94-007-2863-9
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)