Robust extraction of covariate information to improve estimation efficiency in randomized trials

Statistics in Medicine

Volumn 30, Issue 19, 2011, Pages 2389-2408

  Moore, Kelly L ^a   Neugebauer, Romain ^b   Valappil, Thamban ^c   van der Laan, Mark J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b DIVISION OF RESEARCH   (United States)

^c CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

Author keywords

Clinical trials; Covariate adjustment; Efficiency; Variable selection

Indexed keywords

ARTICLE; COVARIANCE; COVARIATE ADJUSTMENT; DATA EXTRACTION; LOGISTIC REGRESSION ANALYSIS; MAXIMUM LIKELIHOOD METHOD; METHODOLOGY; PROBABILITY; RANDOMIZED CONTROLLED TRIAL (TOPIC); STATISTICAL ANALYSIS; STATISTICAL MODEL; TARGETED MAXIMUM LIKELIHOOD ESTIMATOR; THEORETICAL MODEL; THERAPY EFFECT;

CLINICAL TRIALS, PHASE IV AS TOPIC; HUMANS; LIKELIHOOD FUNCTIONS; LINEAR MODELS; LOGISTIC MODELS; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RESEARCH DESIGN; STATISTICS AS TOPIC;

EID: 79960565302     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4301     Document Type: Article

References (30)

1. van der Laan MJ, Robins JM. Unified Methods for Censored Longitudinal Data and Causality. Springer: New York, 2003.
2. Pocock SJ, Assmann SE, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Statistics in Medicine 2002; 21:2917-2930.
3. Hernández AV, Steyerberg EW, Habbema JD. Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. Journal of Clinical Epidemiology May 2004; 57(5):454-460.
4. Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet March 2000; 355(9209):1064-1069.
5. Tsiatis AA, Davidian M, Zhang M, Lu X. Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: a principled yet flexible approach. Statistics in Medicine 2008; 27(23):4658-4677.
6. Zhang M, Tsiatis AAA, Davidian M. Improving efficiency of inferences in randomized clinical trials using auxiliary covariates. Biometrics 2008; 64(3):707-715.
7. Braun TM, Feng Z. Misunderstandings between experimentalists and observationalists about causal inference. Journal of the American Statistical Association 2001; 96(456):1424-1432.
8. Gail MH, Mark SD, Carroll RJ, Green SB, DavidPe E. On design considerations and randomization-based inference for community intervention trials. Statistics in Medicine June 1996; 15(11):1069-1092, DOI: 10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q.
9. Raz J. Testing for no effect when estimating a smooth function by nonparametric regression: a randomization approach. Journal of the American Statistical Association 1990; 85(409):132-138.
10. Rosenbaum PR. Covariance adjustment in randomized experiments and observational studies. Statistical Science 2002; 17(3):286-327.
11. Moore KL, van der Laan MJ. Covariate adjustment in randomized trials with binary outcomes: targeted maximum likelihood estimation. Statistics in Medicine 2009; 28(1):39-64.
12. Freedman DA. On regression adjustments to experimental data. Advances in Applied Mathematics 2008; 40(2):180-193.
13. Koch GG, Tangen CM, Jung JW, Amara IA. Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing them. Statistics in Medicine 1998; 17(15-16):1863-1892.
14. Senn S. Testing for baseline balance in clinical trials. Statistics in Medicine 1994; 13(17):1715-1726.
15. Freedman DA. Randomization does not justify logistic regression. Statistical Science 2008; 23(2):237-249.
16. Imai K, King G, Stuart EA. Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society: Series A (Statistics in Society) April 2008 171(2):481-502, DOI: 10.1111/j.1467-985X.2007.00527.x.
17. US FDA. International conference on harmonisation: guidance on statistical principles for clinical trials 1998; 63(179).
18. Robinson LD, Jewell NP. Some surprising results about covariate adjustment in logistic regression models. International Statistical Review/Revue Internationale de Statistique 1991; 59(2):227-240.
19. Robins JM. A new approach to causal inference in mortality studies with a sustained exposure period-application to control of the healthy worker survivor effect. Mathematical Modelling 1986; 7(9-12):1393-1512. Mathematical models in medicine: diseases and epidemics, Part 2.
20. Robins JM. A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. Journal of Chronic Diseases 1987; 40:139S-161S.
21. van der Laan MJ, Rubin D. Targeted maximum likelihood learning. The International Journal of Biostatistics 2006; 2(1):Article 11.
22. Robins JM. Robust estimation in sequentially ignorable missing data and causal inference models. Proceedings of the American Statistical Association, American Statistical Association: Alexandria, VA, 2000.
23. Robins JM, Rotnitzky A. Comment on the Bickel and Kwon article, "Inference for semiparametric models: Some questions and an answer". Statistica Sinica 2001; 11(4):920-936.
24. Neugebauer R, van der Laan M. Why prefer double robust estimators in causal inference?. Journal of Statistical Planning and Inference 2005; 129(1-2):405-426.
25. van der Vaart A, Wellner JA. Weak Convergence and Empirical Processes. Springer: New York, 1996.
26. Begg C. Significance tests of covariate imbalance in clinical trials. Controlled Clinical Trials 1990; 11(4):223-225.
27. Permutt T. Testing for imbalance of covariates in controlled experiments. Statistics in Medicine 1990; 9(12):1455-1462.
28. van der Laan MJ, Dudoit S, Keles S. Asymptotic Optimality of Likelihood Based Cross-Validation, February 2003. U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 125.
29. Sinisi S, van der Laan MJ. The deletion/substitution/addition algorithm in loss function based estimation: applications in genomics. Statistical Applications in Genetics and Molecular Biology 2004; 3(1).
30. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 1995; 57(1):289-300, DOI: 10.2307/2346101.