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Multiple imputation of missing data in nested case-control and case-cohort studies.

TitleMultiple imputation of missing data in nested case-control and case-cohort studies.
Publication TypeJournal Article
Year of Publication2018
AuthorsKeogh RH, Seaman SR, Bartlett JW
Secondary AuthorsWood AM
JournalBiometrics
Volume74
Issue4
Pagination1438-1449
Date Published2018 12
ISSN1541-0420
KeywordsBiometry, Case-Control Studies, Cohort Studies, Computer Simulation, Data Interpretation, Statistical, Humans
Abstract

The nested case-control and case-cohort designs are two main approaches for carrying out a substudy within a prospective cohort. This article adapts multiple imputation (MI) methods for handling missing covariates in full-cohort studies for nested case-control and case-cohort studies. We consider data missing by design and data missing by chance. MI analyses that make use of full-cohort data and MI analyses based on substudy data only are described, alongside an intermediate approach in which the imputation uses full-cohort data but the analysis uses only the substudy. We describe adaptations to two imputation methods: the approximate method (MI-approx) of White and Royston (2009) and the "substantive model compatible" (MI-SMC) method of Bartlett et al. (2015). We also apply the "MI matched set" approach of Seaman and Keogh (2015) to nested case-control studies, which does not require any full-cohort information. The methods are investigated using simulation studies and all perform well when their assumptions hold. Substantial gains in efficiency can be made by imputing data missing by design using the full-cohort approach or by imputing data missing by chance in analyses using the substudy only. The intermediate approach brings greater gains in efficiency relative to the substudy approach and is more robust to imputation model misspecification than the full-cohort approach. The methods are illustrated using the ARIC Study cohort. Supplementary Materials provide R and Stata code.

DOI10.1111/biom.12910
Alternate JournalBiometrics
PubMed ID29870056
PubMed Central IDPMC6481559
Grant ListHHSN268201100012C / HL / NHLBI NIH HHS / United States
HHSN268201100009I / HL / NHLBI NIH HHS / United States
RG/13/13/30194 / BHF_ / British Heart Foundation / United Kingdom
HHSN268201100008C / HL / NHLBI NIH HHS / United States
HHSN268201100007C / HL / NHLBI NIH HHS / United States
HHSN268201100011I / HL / NHLBI NIH HHS / United States
HHSN268201100011C / HL / NHLBI NIH HHS / United States
MR/L003120/1 / MRC_ / Medical Research Council / United Kingdom
HHSN268201100005I / HL / NHLBI NIH HHS / United States
MR/M014827/1 / MRC_ / Medical Research Council / United Kingdom
HHSN268201100007I / HL / NHLBI NIH HHS / United States
G0700463 / MRC_ / Medical Research Council / United Kingdom
MR/K014811/1 / MRC_ / Medical Research Council / United Kingdom
MRC_U105260558 / MRC_ / Medical Research Council / United Kingdom
HHSN268201100010C / HL / NHLBI NIH HHS / United States
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / HL / NHLBI NIH HHS / United States
MC_UU_00002/10 / MRC_ / Medical Research Council / United Kingdom
HHSN268201100006C / HL / NHLBI NIH HHS / United States
HHSN268201100009C / HL / NHLBI NIH HHS / United States
MC_UU_00002/10 / MRC_ / Medical Research Council / United Kingdom
HHSN268201100005C / HL / NHLBI NIH HHS / United States