ArticleJournal of the National Cancer Institute2016
Evaluating Markers for Guiding Treatment.
Article in Journal of the National Cancer Institute, 2016. 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, 25 citations in OpenAlex.
- Simple Methods for Evaluating 4 Types of Biomarkers: Surrogate Endpoint, Prognostic, Predictive, and Cancer Screening.Biomarker insights · 2020Review
- Constructing treatment selection rules based on an estimated treatment effect function: different approaches to take stochastic uncertainty into account have a substantial effect on performance.BMC medical research methodology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe subpopulation treatment effect pattern plot (STEPP) is an appealing method for assessing the clinical impact of a predictive marker on patient outcomes and identifying a promising subgroup for further study. However, its original formulation lacked a decision analytic justification and applied only to a single marker.
methodsWe derive a decision-analytic result that motivates STEPP. We discuss the incorporation of multiple predictive markers into STEPP using risk difference, cadit, and responders-only benefit functions.
resultsApplying STEPP to data from a breast cancer treatment trial with multiple markers, we found that none of the three benefit functions identified a promising subgroup for further study. Applying STEPP to hypothetical data from a trial with 100 markers, we found that all three benefit functions identified promising subgroups as evidenced by the large statistically significant treatment effect in these subgroups.
conclusionsBecause the method has desirable decision-analytic properties and yields an informative plot, it is worth applying to randomized trials on the chance there is a large treatment effect in a subgroup determined by the predictive markers.
Indexed as
Identifiers
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.