Evidence map›Paper›PMID 29165547›Full record

ArticleAmerican journal of epidemiology2018

Multiple Imputation for Incomplete Data in Epidemiologic Studies.

Ofer Harel, Emily M Mitchell, Neil J Perkins, Stephen R Cole, Eric J Tchetgen Tchetgen, BaoLuo Sun, Enrique F Schisterman

Abstract readMulticenter Study
In one paragraph

Article in American journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
143citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

143 citing papers in PubMed, 1 synthesis or guideline pooled it.

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83 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ofer HarelDepartment of Statistics, College of Liberal Arts and Sciences, University of Connecticut, Storrs, Connecticut.
Emily M MitchellCenters for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality, Rockville, Maryland.
Neil J PerkinsEunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, Maryland.
Stephen R ColeDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Eric J Tchetgen TchetgenDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
BaoLuo SunDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Enrique F SchistermanEunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, Maryland.

Funding

ROC Curve MethodologyZIAHD008761 · NICHD · EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT · PI SCHISTERMAN, ENRIQUE F. · 2009 to 2021
$2.1M
Dealing with missing data in HIV prevention trialsK01MH087219 · NIMH · UNIVERSITY OF CONNECTICUT STORRS · PI HAREL, OFER · 2010 to 2014
$889k
NIMH NIH HHS K01 MH087219
6 · The paper itself

Abstract

Epidemiologic studies are frequently susceptible to missing information. Omitting observations with missing variables remains a common strategy in epidemiologic studies, yet this simple approach can often severely bias parameter estimates of interest if the values are not missing completely at random. Even when missingness is completely random, complete-case analysis can reduce the efficiency of estimated parameters, because large amounts of available data are simply tossed out with the incomplete observations. Alternative methods for mitigating the influence of missing information, such as multiple imputation, are becoming an increasing popular strategy in order to retain all available information, reduce potential bias, and improve efficiency in parameter estimation. In this paper, we describe the theoretical underpinnings of multiple imputation, and we illustrate application of this method as part of a collaborative challenge to assess the performance of various techniques for dealing with missing data (Am J Epidemiol. 2018;187(3):568-575). We detail the steps necessary to perform multiple imputation on a subset of data from the Collaborative Perinatal Project (1959-1974), where the goal is to estimate the odds of spontaneous abortion associated with smoking during pregnancy.

Indexed as

Data AccuracyData Interpretation, StatisticalEpidemiologic Research DesignEpidemiologic StudiesBiasFemaleHumansPregnancy

Identifiers

PMID29165547
PMCPMC5860387

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.