Comparison of multiple imputation and two-phase logistic regression to analyse two-phase case–control studies with rich phase 1: a simulation study. Issue 11 (24th July 2018)

• Record Type: Journal Article
• Authors: Enders, Dirk; Kollhorst, Bianca; Engel, Susanne; Linder, Roland; Pigeot, Iris
• Is Part Of: Journal of statistical computation and simulation. Volume 88:Issue 11(2018)
• Page Start: 2201
• Page End: 2214
• Publication Date: 2018-07-24
• Subjects: Conditional maximum likelihood -- imputation model -- missing-at-random assumption -- participation model -- pseudo likelihood -- secondary data; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2018.1452926
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Two-phase case–control studies cope with the problem of confounding by obtaining required additional information for a subset (phase 2) of all individuals (phase 1). Nowadays, studies with rich phase 1 data are available where only few unmeasured confounders need to be obtained in phase 2. The extended conditional maximum likelihood (ECML) approach in two-phase logistic regression is a novel method to analyse such data. Alternatively, two-phase case–control studies can be analysed by multiple imputation (MI), where phase 2 information for individuals included in phase 1 is treated as missing. We conducted a simulation of two-phase studies, where we compared the performance of ECML and MI in typical scenarios with rich phase 1. Regarding exposure effect, MI was less biased and more precise than ECML. Furthermore, ECML was sensitive against misspecification of the participation model. We therefore recommend MI to analyse two-phase case–control studies in situations with rich phase 1 data.