Issues in adjusting for covariates arising postrandomization in clinical trials

Drug Information Journal

Volumn 33, Issue 4, 1999, Pages 1219-1228

  Rochon, James ^a  

^a GEORGE WASHINGTON UNIVERSITY   (United States)

Author keywords

Clinical trials; Confounding; Intention to treat analysis; Statistical adjustment; Time dependent covariates

Indexed keywords

ARTICLE; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; COVARIANCE; DRUG DEVELOPMENT; GOOD CLINICAL PRACTICE; GOOD MANUFACTURING PRACTICE; PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL; STATISTICAL ANALYSIS; STATISTICS;

EID: 0032692995     PISSN: 00928615     EISSN: None     Source Type: Journal    
DOI: 10.1177/009286159903300425     Document Type: Article

References (17)

1. Wickramaratne PJ, Holford TR. Confounding in epidemiologic studies: The adequacy of the control group as a measure of confounding. Biometrics. 1987;43:751-765.
2. Miettinen OS, Cook EF. Confounding: essence and detection. Am J Epidemiol. 1981;114:593-603.
3. Oakes D, Moss AJ, Fleiss JL, Bigger JT, Therneau T, Eberly SW, McDermott MP, Manatunga A, Carleen E, Benhorin J. Use of compliance measures in an analysis of the effect of Diltiazem on mortality and reinfarction after myocardial Infarction. J Am Stat Assoc. 1993;88:44-49.
4. Andersen PK. Time-dependent covariates and Markov processes In: Moolgavkar SH, Prentice RL, eds. Modern Statistical Methods in Chronic Disease Epidemiology. New York, NY: John Wiley & Sons; 1986:82-103.
5. Efron B, Feldman D. Compliance as an explanatory variable in clinical trials (with discussion). J Am Star Assoc. 1991;86:9-26.
6. Robins J. The analysis of randomized and nonrandomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In: Sechrest L, Freeman H, Mulley A, eds. Health Service Research Methodology: A Focus on AIDS. Washington, DC: NCHSR, U.S. Public Health Service; 1989: 113-159.
7. Robins JM, Tsiatis AA. Correcting for non-compliance in randomized trials using rank preserving structural failure time models. Comm Stat Theory Meth. 1991;20:2609-2631.
8. Mark SD, Robins JM. A method for the analysis of randomized trials with compliance information: an application to the Multiple Risk Factor Intervention Trial. Control Clin Trials. 1993;14:79-97.
9. Robins JM. Correcting for non-compliance in randomized trials using structural nested mean models. Comm Stat Theory Meth. 1994;23:2379-2412.
10. Wu MC, Bailey KR. Estimation and comparison of changes in the presence of informative censoring: conditional linear model. Biometrics. 1989;45:939-955.
11. Wu MC, Carroll RJ. Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics. 1988;44:175-188.
12. Diggle P. Informative drop-out in longitudinal data analysis (with discussion). Appl Stat. 1994;43: 49-93.
13. Robins J, Greenland S. Adjusting for differential rates of prophylaxis therapy for PCP in high- versus low-dose AZT treatment arms in an AIDS randomized Trial. J Am Stat Assoc. 1994;89:737-749.
14. Rochon J. Supplementing the Intent-To-Treat analysis: accounting for covariates observed post randomization in clinical trials. J Am Stat Assoc. 1995;90: 292-300.
15. Rochon J. Accounting for covariates observed post randomization for discrete and continuous repeated measures data. J Roy Stat Soc B. 1996;58:205-219.
16. Yusuf S, Wittes J, Probstfield J, Tyroler HA. Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. JAMA. 1991;266:93-98.
17. Rochon J. Analyzing bivariate repeated measures for discrete and continuous outcome variables. Biometrics. 1996;52:740-750.