ArticleStatistics in medicine2019
The use of prognostic scores for causal inference with general treatment regimes.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Comparison of disease risk score methods to study treatment effect heterogeneity: a simulation study.American journal of epidemiology · 2026Article
- Article
- Sex, male origin microchimerism, and mortality in a Danish cohort.European journal of epidemiology · 2025Article
- Targeted learning in observational studies with multi-valued treatments: An evaluation of antipsychotic drug treatment safety.Statistics in medicine · 2024Article
- Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods.American journal of epidemiology · 2024Article
- Research considerations for prospective studies of patients with coma and disorders of consciousness.Brain communications · 2024Review
- Dealing with confounding in observational studies: A scoping review of methods evaluated in simulation studies with single-point exposure.Statistics in medicine · 2023Article
- Practical recommendations on double score matching for estimating causal effects.Statistics in medicine · 2022Observational
- A likely responder approach for the analysis of randomized controlled trials.Contemporary clinical trials · 2022Article
- On the aggregation of published prognostic scores for causal inference in observational studies.Statistics in medicine · 2020Article
- The use of prognostic scores for causal inference with general treatment regimes.Statistics in medicine · 2019Article
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Authors and funding
2 authors.
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Abstract
In nonrandomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. While both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Indeed, many treatments are not assigned in a binary fashion and require a certain extent of dosing. Hence, researchers may often be interested in estimating treatment effects across multiple exposures. To the best of our knowledge, the prognostic score analysis has not been yet generalised to this case. In this article, we describe the theory of prognostic scores for causal inference with general treatment regimes. Our methods can be applied to compare multiple treatments using nonrandomised data, a topic of great relevance in contemporary evaluations of clinical interventions. We propose estimators for the average treatment effects in different populations of interest, the validity of which is assessed through a series of simulations. Finally, we present an illustrative case in which we estimate the effect of the delay to Aspirin administration on a composite outcome of death or dependence at 6 months in stroke patients.
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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.