SynthesisJournal of comparative effectiveness research2018
Handling missing data in propensity score estimation in comparative effectiveness evaluations: a systematic review.
Synthesis in Journal of comparative effectiveness research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- Incorporation of missing indicator with multiple imputation in propensity score analysis with partially observed covariates: A simulation study.Statistical methods in medical research · 2025Article
- Robust estimation of dementia prevalence from two-phase surveys with non-responders via propensity score stratification.BMC medical research methodology · 2023Article
- Propensity score analysis with missing data using a multi-task neural network.BMC medical research methodology · 2023Article
- Food insecurity in households of children with ASD in COVID-19 pandemic: A comparative analysis with the Household Pulse Survey data using stabilized inverse probability treatment weights.Disability and health journal · 2022Article
- Propensity score matching with R: conventional methods and new features.Annals of translational medicine · 2021Review
- Observational
- Associations of Depressive Symptoms, COVID-19-Related Stressors, and Coping Strategies. A Comparison Between Cities and Towns in Germany.Frontiers in psychiatry · 2021Article
- Examining the effectiveness of zinc treatment in children admitted with diarrhoea in Kenya's public hospitals: an observational comparative effectiveness study.Journal of global health · 2019Observational
- Avoiding pitfalls when combining multiple imputation and propensity scores.Statistics in medicine · 2019Article
- A comparison of different methods to handle missing data in the context of propensity score analysis.European journal of epidemiology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
aimEven though systematic reviews have examined how aspects of propensity score methods are used, none has reviewed how the challenge of missing data is addressed with these methods. This review therefore describes how missing data are addressed with propensity score methods in observational comparative effectiveness studies.
methodsPublished articles on observational comparative effectiveness studies were extracted from MEDLINE and EMBASE databases.
resultsOur search yielded 167 eligible articles. Majority of these studies (114; 68%) conducted complete case analysis with only 53 of them stating this in the methods. Only 16 articles reported use of multiple imputation.
conclusionFew researchers use correct methods for handling missing data or reported missing data methodology which may lead to reporting biased findings.
Indexed as
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What OpenQuestion holds
Registered trials
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.