ArticlePharmacoepidemiology and drug safety2022
Transparency of high-dimensional propensity score analyses: Guidance for diagnostics and reporting.
Article in Pharmacoepidemiology and drug safety, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Association of anesthesia methods for limb and lower abdominal surgeries with postoperative outcomes in patients requiring home oxygen therapy: a nationwide cohort study in Japan.Journal of anesthesia · 2026Article
- Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.BMC medical research methodology · 2026Article
- Safety Comparison of Risk of Liver Dysfunction between Generic and Brand Statin Drugs Marketed in Japan: A Cohort Study Using MID-NET®.Therapeutic innovation & regulatory science · 2026Article
- Applying High-Dimensional Propensity Scores in a Study of Inhaled Corticosteroids and COVID-19 Outcomes.Pharmacoepidemiology and drug safety · 2025Article
- Evaluation of Methods Adjusting for Unmeasured Confounding Using Large Healthcare Databases: An Empirical Study Concerning Drugs Inducing Prematurity.Clinical and translational science · 2025Article
- Simultaneously Dealing With Immortal Time Bias and Residual Confounding: A Case Study of a High-Dimensional Propensity Score Approach With a Nested Case-Control Framework in Multiple Sclerosis Research.Pharmacoepidemiology and drug safety · 2025Article
- Improving ACS prediction in T2DM patients by addressing false records in electronic medical records using propensity score.Scientific reports · 2025Article
- How Effective Are Machine Learning and Doubly Robust Estimators in Incorporating High-Dimensional Proxies to Reduce Residual Confounding?Pharmacoepidemiology and drug safety · 2025Article
- Identifying markers of health-seeking behaviour and healthcare access in UK electronic health records.BMJ open · 2024Observational
- High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data.American journal of epidemiology · 2024Article
- Application of High-Dimensional Propensity Score Methods to the National Health and Aging Trends Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Article
- A population-based cohort of drug exposures and adverse pregnancy outcomes in China (DEEP): rationale, design, and baseline characteristics.European journal of epidemiology · 2024Article
- Associations Between Postdischarge Care and Cognitive Impairment-Related Hospital Readmissions for Ketoacidosis and Severe Hypoglycemia in Adults With Diabetes.Diabetes care · 2024Article
- High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting.Pharmacoepidemiology and drug safety · 2023Article
- Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting.Pharmacoepidemiology and drug safety · 2022Review
- Transparency of high-dimensional propensity score analyses: Guidance for diagnostics and reporting.Pharmacoepidemiology and drug safety · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeThe high-dimensional propensity score (HDPS) is a semi-automated procedure for confounder identification, prioritisation and adjustment in large healthcare databases that requires investigators to specify data dimensions, prioritisation strategy and tuning parameters. In practice, reporting of these decisions is inconsistent and this can undermine the transparency, and reproducibility of results obtained. We illustrate reporting tools, graphical displays and sensitivity analyses to increase transparency and facilitate evaluation of the robustness of analyses involving HDPS.
methodsUsing a study from the UK Clinical Practice Research Datalink that implemented HDPS we demonstrate the application of the proposed recommendations.
resultsWe identify seven considerations surrounding the implementation of HDPS, such as the identification of data dimensions, method for code prioritisation and number of variables selected. Graphical diagnostic tools include assessing the balance of key confounders before and after adjusting for empirically selected HDPS covariates and the identification of potentially influential covariates. Sensitivity analyses include varying the number of covariates selected and assessing the impact of covariates behaving empirically as instrumental variables. In our example, results were robust to both the number of covariates selected and the inclusion of potentially influential covariates. Furthermore, our HDPS models achieved good balance in key confounders.
conclusionsThe data-adaptive approach of HDPS and the resulting benefits have led to its popularity as a method for confounder adjustment in pharmacoepidemiological studies. Reporting of HDPS analyses in practice may be improved by the considerations and tools proposed here to increase the transparency and reproducibility of study results.
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