ArticleAmerican journal of epidemiology2017
The "Dry-Run" Analysis: A Method for Evaluating Risk Scores for Confounding Control.
Article in American journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed.
- Tuning LASSO Models for Propensity Score Weighting and Using Synthetic Negative Control Exposures for Residual Bias Detection.Statistics in medicine · 2026Article
- Methods to Adjust for Confounding in Test-Negative Design COVID-19 Effectiveness Studies: Simulation Study.JMIR formative research · 2025Article
- Favorable changes in the eGFR slope after dapagliflozin treatment and its association with the initial dip.Clinical and experimental nephrology · 2024Article
- Use of clinical pharmacy services by American Indians and Alaska Native adults with cardiovascular disease.Journal of the American College of Clinical Pharmacy : JACCP · 2022Article
- Synthetic Negative Controls: Using Simulation to Screen Large-scale Propensity Score Analyses.Epidemiology (Cambridge, Mass.) · 2022Article
- A likely responder approach for the analysis of randomized controlled trials.Contemporary clinical trials · 2022Article
- Standardizing Discrete-Time Hazard Ratios With a Disease Risk Score.American journal of epidemiology · 2020Article
- Using Real-World Data to Predict Clinical and Economic Benefits of a Future Drug Based on its Target Product Profile.Drugs - real world outcomes · 2020Article
- On the aggregation of published prognostic scores for causal inference in observational studies.Statistics in medicine · 2020Article
- Controlling Confounding in a Study of Oral Anticoagulants: Comparing Disease Risk Scores Developed Using Different Follow-Up Approaches.EGEMS (Washington, DC) · 2019Article
- The use of prognostic scores for causal inference with general treatment regimes.Statistics in medicine · 2019Article
- Evaluating Community-Based Translational Interventions Using Historical Controls: Propensity Score vs. Disease Risk Score Approach.Prevention science : the official journal of the Society for Prevention Research · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
A propensity score (PS) model's ability to control confounding can be assessed by evaluating covariate balance across exposure groups after PS adjustment. The optimal strategy for evaluating a disease risk score (DRS) model's ability to control confounding is less clear. DRS models cannot be evaluated through balance checks within the full population, and they are usually assessed through prediction diagnostics and goodness-of-fit tests. A proposed alternative is the "dry-run" analysis, which divides the unexposed population into "pseudo-exposed" and "pseudo-unexposed" groups so that differences on observed covariates resemble differences between the actual exposed and unexposed populations. With no exposure effect separating the pseudo-exposed and pseudo-unexposed groups, a DRS model is evaluated by its ability to retrieve an unconfounded null estimate after adjustment in this pseudo-population. We used simulations and an empirical example to compare traditional DRS performance metrics with the dry-run validation. In simulations, the dry run often improved assessment of confounding control, compared with the C statistic and goodness-of-fit tests. In the empirical example, PS and DRS matching gave similar results and showed good performance in terms of covariate balance (PS matching) and controlling confounding in the dry-run analysis (DRS matching). The dry-run analysis may prove useful in evaluating confounding control through DRS models.
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