A Simple Regression-based Approach to Account for Survival Bias in Birth Outcomes Research. Issue 4 (July 2015)

• Record Type: Journal Article
• Authors: Tchetgen Tchetgen, Eric J.; Phiri, Kelesitse; Shapiro, Roger
• Is Part Of: Epidemiology. Volume 26:Issue 4(2015:Jul.)
• Publication Date: 2015-07
• Subjects: Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000000317
• Languages: English
• ISSNs: 1044-3983

Abstract

Abstract : In perinatal epidemiology, birth outcomes such as small for gestational age (SGA) may not be observed for a pregnancy ending with a stillbirth. It is then said that SGA is truncated by stillbirth, which may give rise to survival bias when evaluating the effects on SGA of an exposure known also to influence the risk of a stillbirth. In this article, we consider the causal effects of maternal infection with human immunodeficiency virus (HIV) on the risk of SGA, in a sample of pregnant women in Botswana. We hypothesize that previously estimated effects of HIV on SGA may be understated because they fail to appropriately account for the over-representation of live births among HIV negative mothers, relative to HIV positive mothers. A simple yet novel regression-based approach is proposed to adjust effect estimates for survival bias for an outcome that is either continuous or binary. Under certain straightforward assumptions, the approach produces an estimate that may be interpreted as the survivor average causal effect of maternal HIV, which is, the average effect of maternal HIV on SGA among births that would be live irrespective of maternal HIV status. The approach is particularly appealing, because it recovers an exposure effect which is robust to survival bias, even if the association between the risk of SGA and that of a stillbirth cannot be completely explained by adjusting for observed shared risk factors. The approach also gives a formal statistical test of the null hypothesis of no survival bias in the regression framework. Abstract : Supplemental Digital Content is available in the text.