Standard multiple imputation of survey data didn't perform better than simple substitution in enhancing an administrative dataset: the example of self-rated health in …

F Popham, E Whitley, O Molaodi, L Gray - Emerging themes in …, 2021 - Springer
Background Health surveys provide a rich array of information but on relatively small
numbers of individuals and evidence suggests that they are becoming less representative …

Multiple imputation in a longitudinal cohort study: a case study of sensitivity to imputation methods

H Romaniuk, GC Patton, JB Carlin - American journal of …, 2014 - academic.oup.com
Multiple imputation has entered mainstream practice for the analysis of incomplete data. We
have used it extensively in a large Australian longitudinal cohort study, the Victorian …

Multiple imputation to evaluate the impact of an assay change in national surveys

M Sternberg - Statistics in medicine, 2017 - Wiley Online Library
National health surveys, such as the National Health and Nutrition Examination Survey, are
used to monitor trends of nutritional biomarkers. These surveys try to maintain the same …

Improving on analyses of self‐reported data in a large‐scale health survey by using information from an examination‐based survey

N Schenker, TE Raghunathan… - Statistics in …, 2010 - Wiley Online Library
Common data sources for assessing the health of a population of interest include large‐
scale surveys based on interviews that often pose questions requiring a self‐report, such …

Recovery of information from multiple imputation: a simulation study

KJ Lee, JB Carlin - Emerging themes in epidemiology, 2012 - Springer
Background Multiple imputation is becoming increasingly popular for handling missing data.
However, it is often implemented without adequate consideration of whether it offers any …

Longitudinal multiple imputation approaches for body mass index or other variables with very low individual-level variability: the mibmi command in Stata

E Kontopantelis, R Parisi, DA Springate, D Reeves - BMC research notes, 2017 - Springer
Background In modern health care systems, the computerization of all aspects of clinical
care has led to the development of large data repositories. For example, in the UK, large …

Multiple imputation to account for missing data in a survey: estimating the prevalence of osteoporosis

A Kmetic, L Joseph, C Berger, A Tenenhouse - Epidemiology, 2002 - journals.lww.com
Background. Nonresponse bias is a concern in any epidemiologic survey in which a subset
of selected individuals declines to participate. Methods. We reviewed multiple imputation, a …

Selection bias was reduced by recontacting nonparticipants

J Karvanen, H Tolonen, T Härkänen, P Jousilahti… - Journal of Clinical …, 2016 - Elsevier
Objective One of the main goals of health examination surveys is to provide unbiased
estimates of health indicators at the population level. We demonstrate how multiple …

Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health …

I Pelgrims, B Devleesschauwer, S Vandevijvere… - BMC Medical Research …, 2023 - Springer
Background In many countries, the prevalence of non-communicable diseases risk factors is
commonly assessed through self-reported information from health interview surveys. It has …

Guided multiple imputation of missing data: using a subsample to strengthen the missing-at-random assumption

G Fraser, R Yan - Epidemiology, 2007 - journals.lww.com
Multiple imputation can be a good solution to handling missing data if data are missing at
random. However, this assumption is often difficult to verify. We describe an application of …