ArticleStatistics in medicine2019
Avoiding pitfalls when combining multiple imputation and propensity scores.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed.
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- The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation.Statistics in medicine · 2026Article
- Outcome-Assisted Multiple Imputation of Missing Treatments.Observational studies · 2026Article
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- Longitudinal associations between iron status and patient-reported outcomes in incident dialysis patients: a DOMESTICO substudy.Journal of nephrology · 2025Article
- Effects of Outdoor Therapy on Delirium in Patients With Prolonged Intensive Care Unit Stays: A Single-Centre Retrospective Study.Nursing in critical care · 2025Article
- Association of early cardiac rehabilitation on mortality in patients with dilated cardiomyopathy using national inpatient database.Scientific reports · 2025Article
- Association between endotracheal intubation and outcomes of nonshockable out-of-hospital cardiac arrest in Japan.BMC emergency medicine · 2025Article
- Hyperkalemia and maintenance of renin-angiotensin system inhibitor therapy after initiating SGLT-2 or DPP-4 inhibitors.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2025Article
- Comparative effectiveness of subcutaneous sarilumab 200 mg biweekly, subcutaneous Tocilizumab 162 mg biweekly, and intravenous Tocilizumab 8 mg/kg every 4 weeks in patients with rheumatoid arthritis: a prospective cohort study.Arthritis research & therapy · 2025Article
- Treatments with versus without medication for children with behavioural difficulties in clinical practice: an economic evaluation with observational data.Journal of child psychology and psychiatry, and allied disciplines · 2025Observational
- Addressing immortal time bias in precision medicine: Practical guidance and methods development.Health services research · 2025Article
- Using Electronic Medical Records to Estimate the Prevalence of Pediatric Obesity in Mississippi.Journal of public health in the deep South · 2025Article
6 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Overcoming bias due to confounding and missing data is challenging when analyzing observational data. Propensity scores are commonly used to account for the first problem and multiple imputation for the latter. Unfortunately, it is not known how best to proceed when both techniques are required. We investigate whether two different approaches to combining propensity scores and multiple imputation (Across and Within) lead to differences in the accuracy or precision of exposure effect estimates. Both approaches start by imputing missing values multiple times. Propensity scores are then estimated for each resulting dataset. Using the Across approach, the mean propensity score across imputations for each subject is used in a single subsequent analysis. Alternatively, the Within approach uses propensity scores individually to obtain exposure effect estimates in each imputation, which are combined to produce an overall estimate. These approaches were compared in a series of Monte Carlo simulations and applied to data from the British Society for Rheumatology Biologics Register. Results indicated that the Within approach produced unbiased estimates with appropriate confidence intervals, whereas the Across approach produced biased results and unrealistic confidence intervals. Researchers are encouraged to implement the Within approach when conducting propensity score analyses with incomplete data.
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