Efficiency gain in association studies based on population surveys by augmenting outcome data from the target population

TL;DR

This study explores how combining large population survey data with register data can improve the efficiency of association studies, especially when using multiple imputation for missing data, with findings varying by outcome type.

Contribution

It introduces a combined analysis approach using multiple imputation to enhance efficiency in association studies with survey and register data, addressing missing data mechanisms.

Findings

Multiple imputation improved efficiency for binary outcomes.

Complete-case analysis was as effective as imputation for time-to-event outcomes.

Increasing survey participation and better handling of missing data are needed for implementation.

Abstract

Routinely collected nation-wide registers contain socio-economic and health-related information from a large number of individuals. However, important information on lifestyle, biological and other risk factors is available at most for small samples of the population through surveys. A majority of health surveys lack detailed medical information necessary for assessing the disease burden. Hence, traditionally data from the registers and the surveys are combined to have necessary information for the survey sample. Our idea is to base analyses on a combined sample obtained by adding a (large) sample of individuals from the population to the survey sample. The main objective is to assess the bias and gain in efficiency of such combined analyses with a binary or time-to-event outcome. We employ (i) the complete-case analysis (CCA) using the respondents of the survey, (ii) analysis of the full survey sample with both unit- and item-nonresponse under the missing at random (MAR) assumption and (iii) analysis of the combined sample under mixed type of missing data mechanism. We handle the missing data using multiple imputation (MI)-based analysis in (ii) and (iii). We utilize simulated as well as empirical data on ischemic heart disease obtained from the Finnish population. Our results suggested that the MI methods improved the efficiency of the estimates when we used the combined data for a binary outcome, but in the case of a time-to-event outcome the CCA was at least as good as the MI using the larger datasets, in terms of the the mean absolute and squared errors. Increasing the participation in the surveys and having good statistical methods for handling missing covariate data when the outcome is time-to-event would be needed for implementation of the proposed ideas.