This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Yoshida, M. Articles by Ohashi, Y. Search for Related Content PubMed PubMed Citation Articles by Yoshida, M. Articles by Ohashi, Y. Social Bookmarking                   What's this?

Estimation of treatment effect adjusting for dependent censoring using the IPCW method: an application to a large primary prevention study for coronary events (MEGA study)

Biometrics Division, The Clinical Service Provider, EPS Co., Ltd., 2-3-19 Koraku, Bunkyo-ku, Tokyo 112-0004, Japan

Yutaka Matsuyama

Department of Biostatistics/Epidemiology and Preventive Health Sciences, School of Health Sciences and Nursing, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan, matuyama{at}epistat.m.u-tokyo.ac.jp

Yasuo Ohashi

Department of Biostatistics/Epidemiology and Preventive Health Sciences, School of Health Sciences and Nursing, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Background The MEGA study is a randomized controlled trial conducted in Japan to evaluate the primary preventive effect of pravastatin against coronary heart disease (CHD), in which 8214 subjects are randomized to diet or diet plus pravastatin. Pravastatin reduces the incidence of CHD (hazard ratio = 0.67; 95%CI: 0.49—0.91). In the MEGA study, in addition to the usual loss to follow-up cases, there is another problem of drop-outs due to the refusal of further follow-up at 5 years.

Purpose To estimate the treatment effect adjusting for some types of dependent censorings observed in the MEGA study and to assess the sensitivity of standard analysis results for these censoring cases.

Methods The proposed method is a straightforward extension of the inverse probability of censoring weighted (IPCW) method for settings with more than one reason for censoring, where the propensities for drop-outs are modeled separately for each reason. Simulation studies are also conducted to compare the properties of the IPCW estimate with the standard analysis assuming independent censorings.

Results Simulation studies show that the IPCW estimate can correct for selection bias due to dependent censoring that can be explained by measured factors, while the standard analysis is biased. Applying the proposed method to the MEGA study data, several prognostic factors are associated with the censoring processes, and after adjusting for these dependent censorings, slightly larger treatment effects for pravastatin are observed for both CHD (primary endpoint) and stroke (secondary endpoint) events.

Limitations The method developed is based on the fundamental assumption of sequentially ignorable censoring.

Conclusions Our proposed method provides a valuable approach for estimating treatment effect adjusting for several types of dependent censorings. Dependent censorings observed in the MEGA study did not cause a severe selection bias attributable to the covariates and the results from the standard analysis were robust in relation to the censorings. Clinical Trials 2007; 4: 318—328. http:// ctj.sagepub.com

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?