Trial reportBiometrics2014
Evaluating marker-guided treatment selection strategies.
Trial report in Biometrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Weight loss at your fingertips: personalized nutrition with fasting glucose and insulin using a novel statistical approach.European journal of clinical nutrition · 2019Trial
- Adjusting for covariates in evaluating markers for selecting treatment, with application to guiding chemotherapy for treating estrogen-receptor-positive, node-positive breast cancer.Contemporary clinical trials · 2017Trial
- Machine learning and personalized nutrition: a promising liaison?European journal of clinical nutrition · 2024Article
- Post hoc subgroup analysis and identification-learning more from existing data.European journal of clinical nutrition · 2023Article
- The role of baseline serum 25(OH)D concentration for a potential personalized vitamin D supplementation.European journal of clinical nutrition · 2022Article
- Case-only approach to identifying markers predicting treatment effects on the relative risk scale.Biometrics · 2018Article
- Quantile-Optimal Treatment Regimes.Journal of the American Statistical Association · 2018Article
- An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2018Article
- Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions.Statistica Sinica · 2018Article
- Article
- Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2017Article
- On Estimation of Optimal Treatment Regimes For MaximizingJournal of the Royal Statistical Society. Series B, Statistical methodology · 2017Article
- Tree-based Ensemble Methods For Individualized Treatment Rules.Biostatistics & epidemiology · 2017Article
- The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment.Journal of the National Cancer Institute · 2015Article
- What is the promise of personalised nutrition?Journal of nutritional scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.
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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.