Evaluating the interaction between the therapy and the treatment in clinical trials by the propensity score weighting?method

被引:1
作者
Fujii, Yosuke [1 ]
Henmi, Masayuki [2 ]
Fujita, Toshiharu [2 ]
机构
[1] Pfizer Japan, Clin Stat, Shibuya Ku, Tokyo 1518589, Japan
[2] Risk Anal Res Ctr, Inst Stat Math, Tokyo, Japan
关键词
propensity scores; interaction; clinical trials; selective registration; RISK DIFFERENCES; PERFORMANCE;
D O I
10.1002/sim.4400
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In clinical trials for antihypertensive drugs, a combination therapy trial and a monotherapy trial are often conducted simultaneously. In this situation, it can be a clinical concern to know the difference of the safety or efficacy of the new drug between the two therapies, in other words, to investigate the interaction between the therapy (monotherapy or combination therapy) and the treatment (test or control). However, because patients are often registered in either of these trials on the basis of their background characteristics, specific patients may be selected to participate in the monotherapy trial or combination therapy trial and not chosen at random, whereas the treatment is assigned randomly in each trial after registration. If this fact is not considered, the statistical analysis of the interaction may be biased. In this paper, we aim to evaluate the interaction between the two aforementioned factors by adjusting for covariates that may affect registration in the two trials. For this purpose, we apply the propensity score weighting method to suit the problem. The propensity score in this case is decomposed into the usual propensity score for the registration and the assignment probability for the random treatment assignment on the basis of their two-stage structure. We also discuss the augmented estimator known as the doubly robust estimator. In addition, we apply this method to data of a clinical trial for an antihypertensive drug that was conducted in Japan and conduct a simulation study to evaluate the performance of our proposed method. Copyright (C) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:235 / 252
页数:18
相关论文
共 27 条
[1]   The performance of two data-generation processes for data with specified marginal treatment odds ratios [J].
Austin, Peter C. ;
Stafford, James .
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2008, 37 (06) :1039-1051
[2]   A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study [J].
Austin, Peter C. ;
Grootendorst, Paul ;
Anderson, Geoffrey M. .
STATISTICS IN MEDICINE, 2007, 26 (04) :734-753
[3]   The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies [J].
Austin, Peter C. .
STATISTICS IN MEDICINE, 2010, 29 (20) :2137-2148
[4]   A Data-Generation Process for Data with Specified Risk Differences or Numbers Needed to Treat [J].
Austin, Peter C. .
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2010, 39 (03) :563-577
[5]   Doubly robust estimation in missing data and causal inference models [J].
Bang, H .
BIOMETRICS, 2005, 61 (04) :962-972
[6]  
Baser O, 2007, EC B, V9, P1
[7]   Variable selection for propensity score models [J].
Brookhart, M. Alan ;
Schneeweiss, Sebastian ;
Rothman, Kenneth J. ;
Glynn, Robert J. ;
Avorn, Jerry ;
Sturmer, Til .
AMERICAN JOURNAL OF EPIDEMIOLOGY, 2006, 163 (12) :1149-1156
[8]   Dealing with limited overlap in estimation of average treatment effects [J].
Crump, Richard K. ;
Hotz, V. Joseph ;
Imbens, Guido W. ;
Mitnik, Oscar A. .
BIOMETRIKA, 2009, 96 (01) :187-199
[9]   Causal effects in, nonexperimental studies: Reevaluating the evaluation of training programs [J].
Dehejia, RH ;
Wahba, S .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1999, 94 (448) :1053-1062
[10]  
Greenland S, 1993, ENVIRON HEALTH PERSP, V101, pS4