Evidence map›Paper›PMID 29682776›Full record

ArticleStatistics in medicine2018

Bootstrap inference when using multiple imputation.

Michael Schomaker, Christian Heumann

Open access · greenAbstract read
In one paragraph

Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 227 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
227citing papers in PubMed, 5 pooled it
38.6field-weighted citation impact, top 1% of its field
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

227 citing papers in PubMed, 5 syntheses or guidelines pooled it, 426 citations in OpenAlex.

  1. Causally Interpretable Meta-analysis: Application in Adolescent HIV Prevention.Prevention science : the official journal of the Society for Prevention Research · 2022
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  10. Effectiveness of the smart program: Stepped-care HIV prevention for gay and bisexual adolescent boys.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2025
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  18. Deeply Tailored Adaptive Interventions to Reduce College Student Drinking: a Real-World Application of Q-Learning for SMART Studies.Prevention science : the official journal of the Society for Prevention Research · 2022
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167 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

2 authors at 2 institutions in 2 countries.

Michael SchomakerCentre for Infectious Disease Epidemiology & Research, University of Cape Town, Falmouth Building, Observatory, Cape Town, 7925, South Africa.ORCID 0000-0002-8475-0591
Christian HeumannChristian Heumann, Institut für Statistik, Ludwig-Maximilians Universität München, München, Germany.
Ludwig-Maximilians-Universität München · DEUniversity of Cape Town · ZA

Funding

Observational Antiretroviral Studies In Southern Africa (OASIS) CollaborationU01AI069924 · NIAID · UNIVERSITAT BERN · PI Cleophas Chimbetete, Mary-Ann Davies · 2006 to 2026
$55.9M
WADA (West Africa Database on Antiretroviral Therapy) CollaborationU01AI069919 · NIAID · UNIVERSITY OF BORDEAUX II · PI Didier Koumavi Ekouevi, Antoine Jaquet · 2006 to 2026
$29.9M
NIAID NIH HHS U01 AI069919NIAID NIH HHS U01 AI069924
6 · The paper itself

Abstract

Many modern estimators require bootstrapping to calculate confidence intervals because either no analytic standard error is available or the distribution of the parameter of interest is nonsymmetric. It remains however unclear how to obtain valid bootstrap inference when dealing with multiple imputation to address missing data. We present 4 methods that are intuitively appealing, easy to implement, and combine bootstrap estimation with multiple imputation. We show that 3 of the 4 approaches yield valid inference, but that the performance of the methods varies with respect to the number of imputed data sets and the extent of missingness. Simulation studies reveal the behavior of our approaches in finite samples. A topical analysis from HIV treatment research, which determines the optimal timing of antiretroviral treatment initiation in young children, demonstrates the practical implications of the 4 methods in a sophisticated and realistic setting. This analysis suffers from missing data and uses the g-formula for inference, a method for which no standard errors are available.

Indexed as

Models, StatisticalAnti-Retroviral AgentsBiometryComputer SimulationHIV InfectionsHumansAnti-Retroviral Agentscausal inferenceg-methodsHIVmissing dataresampling

Identifiers

PMID29682776
PMCPMC5986623
OpenAlexW2291743888

What OpenQuestion holds

Textmetadata
LicenceTDM
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.