ArticleStatistics in medicine2017
Patient subgroup identification for clinical drug development.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Subgroup analysis methods for time-to-event outcomes in heterogeneous randomized controlled trials.BMC medical research methodology · 2026Article
- Risk stratification of low-dose cytarabine and venetoclax in patients with AML ineligible for intensive chemotherapy.Blood advances · 2026Article
- MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2026Article
- Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.Nature communications · 2025Article
- Deep learning-based ranking method for subgroup and predictive biomarker identification in patients.Communications medicine · 2025Article
- Article
- BioPred: an R package for biomarkers analysis in precision medicine.Bioinformatics (Oxford, England) · 2024Article
- Predicting clinical progression trajectories of early Alzheimer's disease patients.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- BCL2 Inhibition: A New Paradigm for the Treatment of AML and Beyond.HemaSphere · 2023Review
- A reference-free R-learner for treatment recommendation.Statistical methods in medical research · 2023Article
- Predictive signature development based on maximizing the area between receiver operating characteristic curves.Statistics in medicine · 2022Article
- A Computational Framework for Identifying Age Risks in Drug-Adverse Event Pairs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2022Article
- BCL2 Family Inhibitors in the Biology and Treatment of Multiple Myeloma.Blood and lymphatic cancer : targets and therapy · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Causal mechanism of relationship between the clinical outcome (efficacy or safety endpoints) and putative biomarkers, clinical baseline, and related predictors is usually unknown and must be deduced empirically from experimental data. Such relationships enable the development of tailored therapeutics and implementation of a precision medicine strategy in clinical trials to help stratify patients in terms of disease progression, clinical response, treatment differentiation, and so on. These relationships often require complex modeling to develop the prognostic and predictive signatures. For the purpose of easier interpretation and implementation in clinical practice, defining a multivariate biomarker signature in terms of thresholds (cutoffs/cut points) on individual biomarkers is preferable. In this paper, we propose some methods for developing such signatures in the context of continuous, binary and time-to-event endpoints. Results from simulations and case study illustration are also provided. Copyright © 2017 John Wiley & Sons, Ltd.
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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.