Improved Analysis of sibling survival data Taking Into Account Survivor Bias, Zero-surviving reporters and Recall Bias

In the absence of a well-functioning vital registration system to track mortality in a population, health planners often rely on routine health surveys to provide this most basic health information. Sibling survival histories, where a survey respondent is asked about each of his or her siblings’ births and, if applicable, deaths, provide a direct way to estimate adult mortality by survey. The purpose of this paper is to refine the methods which account for the selection bias, zero-surviving reporters and recall bias inherent in these surveys to generate plausible estimates of adult mortality rates even in the presence of a relationship between family size and adult mortality.

We have implemented changes to the previous method, referred to as the Corrected Sibling Survival (CSS) method, such that it (1) uses appropriate survival weights that account for the study design, and (2) recovers the mortality experience of the families that are not represented because none of the siblings is alive and eligible to respond to the survey. We validate these methodological developments in a range of simulation environments. We also present new ways of adjusting for recall bias and handling sparse data in survey designs where the age range of the respondents is narrower than the age range desired for estimation.