Analysis of clinical trial outcomes: some comments on subgroup analyses.

This article briefly discusses the various ways in which prognostic information can be included in the analysis of treatment effect in clinical trials. Adjustments in the treatment comparison are usually not warranted, as they do not substantially improve precision, but they may be useful, in addition to the unadjusted comparison, if a potent covariate is by chance maldistributed among the treatment groups. Estimation of interactions between treatment and covariates is usually plagued by insufficient statistical power. Estimation of treatment effect within individual subgroups is also subject to large random errors as well as to the problem of multiplicity, but with these caveats in mind it is an informative and needed complement to an analysis of overall treatment effect.

[1]  D. Schoenfeld,et al.  Sample-size formula for the proportional-hazards regression model. , 1983, Biometrics.

[2]  P. Armitage,et al.  Controversies and achievements in clinical trials. , 1984, Controlled clinical trials.

[3]  R Collins,et al.  Avoidance of large biases and large random errors in the assessment of moderate treatment effects: the need for systematic overviews. , 1987, Statistics in medicine.

[4]  J. Cuzick The assessment of subgroups in clinical trials. , 1982, Experientia. Supplementum.

[5]  D. Altman,et al.  Beneficial Effect of Azathioprine and Prediction of Prognosis in Primary Biliary , 1985 .

[6]  P. Meier,et al.  Choosing covariates in the analysis of clinical trials. , 1989, Controlled clinical trials.

[7]  M. Schumacher,et al.  The impact of heterogeneity on the comparison of survival times. , 1987, Statistics in medicine.

[8]  D. Altman,et al.  Beneficial effect of azathioprine and prediction of prognosis in primary biliary cirrhosis. Final results of an international trial. , 1985, Gastroenterology.

[9]  Byar Dp,et al.  The choice of treatment for cancer patients based on covariate information. , 1980 .

[10]  J M Lachin,et al.  Assessment of stratum-covariate interactions in Cox's proportional hazards regression model. , 1986, Statistics in medicine.

[11]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[12]  Morgan Tm,et al.  Omitting covariates from the proportional hazards model. , 1986 .

[13]  R. Gelber,et al.  Interpretation of results from subset analyses within overviews of randomized clinical trials. , 1987, Statistics in medicine.

[14]  D P Byar,et al.  Assessing apparent treatment--covariate interactions in randomized clinical trials. , 1985, Statistics in medicine.

[15]  S W Lagakos,et al.  Properties of proportional-hazards score tests under misspecified regression models. , 1984, Biometrics.

[16]  J. van Eys,et al.  Interaction between prognostic factors and treatment. , 1983, Controlled clinical trials.

[17]  P Armitage,et al.  Importance of prognostic factors in the analysis of data from clinical trials. , 1981, Controlled clinical trials.

[18]  T. Louis Estimating a population of parameter values using Bayes and empirical Bayes methods , 1984 .

[19]  M. Gail,et al.  Testing for qualitative interactions between treatment effects and patient subsets. , 1985, Biometrics.

[20]  B. Schneider Analysis of clinical trial outcomes: alternative approaches to subgroup analysis. , 1989, Controlled clinical trials.