ArticleThe international journal of biostatistics2011
Clarifying the role of principal stratification in the paired availability design.
Article in The international journal of biostatistics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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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
3 citing papers in PubMed.
- Comments on "sensitivity of estimands in clinical trials with imperfect compliance" by Chen and Heitjan.The international journal of biostatistics · 2024Article
- Latent class instrumental variables: a clinical and biostatistical perspective.Statistics in medicine · 2016Article
- The risky reliance on small surrogate endpoint studies when planning a large prevention trial.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2013Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The paired availability design for historical controls postulated four classes corresponding to the treatment (old or new) a participant would receive if arrival occurred during either of two time periods associated with different availabilities of treatment. These classes were later extended to other settings and called principal strata. Judea Pearl asks if principal stratification is a goal or a tool and lists four interpretations of principal stratification. In the case of the paired availability design, principal stratification is a tool that falls squarely into Pearl's interpretation of principal stratification as "an approximation to research questions concerning population averages." We describe the paired availability design and the important role played by principal stratification in estimating the effect of receipt of treatment in a population using data on changes in availability of treatment. We discuss the assumptions and their plausibility. We also introduce the extrapolated estimate to make the generalizability assumption more plausible. By showing why the assumptions are plausible we show why the paired availability design, which includes principal stratification as a key component, is useful for estimating the effect of receipt of treatment in a population. Thus, for our application, we answer Pearl's challenge to clearly demonstrate the value of principal stratification.
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