Assessing treatment effect heterogeneity in clinical trials with blocked binary outcomes

Biometrical Journal

Volumn 47, Issue 5, 2005, Pages 662-673

  Albert, Jeffrey M ^a   Gadbury, Gary L ^b   Mascha, Edward J ^c  

^a CASE WESTERN RESERVE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY   (United States)

^c LERNER RESEARCH INSTITUTE   (United States)

Author keywords

Bounds; Causal effects; Counterfactuals; Potential outcomes; Randomized block design; Subject treatment interaction

Indexed keywords

BLOCK DESIGNS; BOUND; CAUSAL EFFECT; CLINICAL TRIAL; COUNTERFACTUALS; HETEROGENIETY; POTENTIAL OUTCOMES; RANDOMIZED BLOCK DESIGN; SUBJECT-TREATMENT INTERACTION; TREATMENT EFFECTS;

MEDICAL APPLICATIONS;

EID: 27844511009     PISSN: 03233847     EISSN: None     Source Type: Journal    
DOI: 10.1002/bimj.200510157     Document Type: Review

References (19)

1. Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91, 444-455.
2. Berger, V. W., Rezvani, A., and Makarewicz, V. A. (2003). Direct effect on validity of response run-in selection in clinical trials. Controlled Clinical Trials 24, 156-166.
3. CAST Investigators (1989). Preliminary report: effect of encainide and flecainide on mortality in a randomized trial of arrhythmia suppression after myocardial infrction. New England Journal of Medicine 321, 406-412.
4. Gadbury, G. L. and Iyer, H. K. (2000). Unit-treatment interaction and its practical consequences. Biometrics 56, 882-885.
5. Gadbury, G. L., Iyer, H. K., and Allison, D. (2001). Evaluating subject-treatment interaction when comparing two treatments. Journal of Biopharmaceutical Statistics 11(4), 313-333.
6. Gadbury, G. L., Iyer, H. K., and Albert, J. M. (2004). Individual treatment effects in randomized trials with binary outcomes. Journal of Statistical Planning and Inference 121, 163-174.
7. Goetghebeur, E. and Molenberghs, G. (1996). Causal inference in a placebo-controlled clinical trial with binary outcome and ordered compliance. Journal of the American Statistical Association 91, 928-934.
8. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association 81, 945-960.
9. Longford, N. J. (1999). Selection bias and treatment heterogeneity in clinical trials. Statistics in Medicine 18, 1467-1474.
10. Mascha, E. J. (2005). Assessing treatment effect heterogeneity for binary outcomes. Ph.D. Thesis, Case Western Reserve University.
11. Modification of Diet in Renal Disease (MDRD) Study Group. (Prepared by Levey, A. S., Beck, G. J., Cagguila, A. W., Greene, T., Kusek. J. W., Striker, G. E., Klahr, S.). Trends toward a beneficial effect of a low protein diet during additional follow-up in the Modification of Diet in Renal Disease Study. XXXIInd Congress of the European Renal Association, European Dialysis and Transplant Association, Athens, Greece, June 11-14, 1995.
12. Neter, J., Wasserman, W., and Kutner, M. H. (1985). Applied Linear Statistical Models. Irwin, Homewood, Illinois.
13. Prystowsky E. N., Katz, A., and Knilans, T. K. (1990). Ventricular arrhythmias: risk stratification and approach to therapy after the Cardiac Arrhythmia Suppression Trial (CAST). Pacing Clinical Electrophysiology 13, 1480-1487.
14. Rosenbaum, P. R. and Rubin, D. B. (1983a). Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. Journal of the Royal Statistical Society. Series B (Methodological) 45, 212-218.
15. Rosenbaum, P. R. and Rubin, D. B. (1983b). The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55.
16. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 688-701.
17. Rubin, D. B. (1980). Comment on Basu's randomization analysis of experimental data. Journal of the American Statistical Association 75, 591-593.
18. Schall, R. (1995). Assessment of individual and population bioequivalence using the probability that bioavailabilities are similar. Biometrics 51, 615-626.
19. Sheiner, L. B. (1992). Bioequivalence revisited. Statistics in Medicine 11, 1777-1788.