SynthesisStatistics in medicine2021
Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis.
Synthesis in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
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
20 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Estimating individualized treatment effects using an individual participant data meta-analysis.BMC medical research methodology · 2024Pooled it
- Estimating interactions and subgroup-specific treatment effects in meta-analysis without aggregation bias: A within-trial framework.Research synthesis methods · 2023Pooled it
- Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data.The lancet. Psychiatry · 2021Pooled it
- Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis.Statistics in medicine · 2021Pooled it
- Collagen secretion and maturation in osteogenesis imperfecta: Systematic review and meta-analysis.Bone reports · 2026Article
- Comparing the sleep benefits of Nature-Based Interventions: Evidence from a Bayesian Network meta-analysis.iScience · 2026Article
- Bayesian Hierarchical Models With Calibrated Mixtures of g-priors for Assessing Treatment Effect Moderation in Meta-Analysis.Statistics in medicine · 2026Article
- Post-translational modifications of collagen type I in osteogenesis imperfecta: Systematic review and meta-analysis.Bone reports · 2026Article
- Prediction of long-term uncorrected distance visual acuity in surgically SMILE corrected myopic eyes using machine learning.BMJ open ophthalmology · 2025Article
- Article
- Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.Statistics in medicine · 2024Article
- Use of multiple covariates in assessing treatment-effect modifiers: A methodological review of individual participant data meta-analyses.Research synthesis methods · 2024Review
- Article
- Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.Statistical science : a review journal of the Institute of Mathematical Statistics · 2023Article
- Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis.Diagnostic and prognostic research · 2023Article
- Using individual participant data to improve network meta-analysis projects.BMJ evidence-based medicine · 2023Article
- Decision Curve Analysis for Personalized Treatment Choice between Multiple Options.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Article
- Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions.Statistical methods in medical research · 2022Article
- Bayesian models for aggregate and individual patient data component network meta-analysis.Statistics in medicine · 2022Article
- A two-stage prediction model for heterogeneous effects of treatments.Statistics in medicine · 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
8 authors.
Funding
Abstract
Meta-analysis of individual patient data (IPD) is increasingly used to synthesize data from multiple trials. IPD meta-analysis offers several advantages over meta-analyzing aggregate data, including the capacity to individualize treatment recommendations. Trials usually collect information on many patient characteristics. Some of these covariates may strongly interact with treatment (and thus be associated with treatment effect modification) while others may have little effect. It is currently unclear whether a systematic approach to the selection of treatment-covariate interactions in an IPD meta-analysis can lead to better estimates of patient-specific treatment effects. We aimed to answer this question by comparing in simulations the standard approach to IPD meta-analysis (no variable selection, all treatment-covariate interactions included in the model) with six alternative methods: stepwise regression, and five regression methods that perform shrinkage on treatment-covariate interactions, that is, least absolute shrinkage and selection operator (LASSO), ridge, adaptive LASSO, Bayesian LASSO, and stochastic search variable selection. Exploring a range of scenarios, we found that shrinkage methods performed well for both continuous and dichotomous outcomes, for a variety of settings. In most scenarios, these methods gave lower mean squared error of the patient-specific treatment effect as compared with the standard approach and stepwise regression. We illustrate the application of these methods in two datasets from cardiology and psychiatry. We recommend that future IPD meta-analysis that aim to estimate patient-specific treatment effects using multiple effect modifiers should use shrinkage methods, whereas stepwise regression should be avoided.
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.