ArticleJournal of biopharmaceutical statistics2019
Look before you leap: systematic evaluation of tree-based statistical methods in subgroup identification.
Article in Journal of biopharmaceutical statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Predictors of abstinence, no heavy drinking days, and a 2-level reduction in World Health Organization drinking levels during treatment for alcohol use disorder in the COMBINE study.Alcoholism, clinical and experimental research · 2022Trial
- An Application of the Patient Rule-Induction Method to Detect Clinically Meaningful Subgroups from Failed Phase III Clinical Trials.International journal of clinical biostatistics and biometrics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Subgroup analysis, as the key component of personalized medicine development, has attracted a lot of interest in recent years. While a number of exploratory subgroup searching approaches have been proposed, informative evaluation criteria and scenario-based systematic comparison of these methods are still underdeveloped topics. In this article, we propose two evaluation criteria in connection with traditional type I error and power concepts, and another criterion to directly assess recovery performance of the underlying treatment effect structure. Extensive simulation studies are carried out to investigate empirical performance of a variety of tree-based exploratory subgroup methods under the proposed criteria. A real data application is also included to illustrate the necessity and importance of method evaluation.
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What OpenQuestion holds
Registered trials
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.