SynthesisPloS one2019
Assessing heterogeneity of treatment effect analyses in health-related cluster randomized trials: A systematic review.
Synthesis in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05358522 (Learning Health System Clinician Feedback Using Patient-reported Data to Increase Implementation of Post-operative Opioid Prescribing Guidelines), which is not on this map. Cited by 12 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.
Learning Health System Clinician Feedback Using Patient-reported Data to Increase Implementation of Post-operative Opioid Prescribing Guidelines: a Stepped-wedge Cluster Randomized Trial
Who cites it
12 citing papers in PubMed.
- The design of embedded pragmatic clinical trials: methodological developments and statistical lessons learned from the first cycle of the NIA IMPACT collaboratory.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Article
- Refining, implementing, and evaluating an anesthesia choice conversation aid for older adults with hip fracture: protocol for a stepped wedge cluster randomized trial.Implementation science communications · 2024Article
- Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs.Statistical methods in medical research · 2024Article
- Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials.Statistical methods in medical research · 2024Article
- Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes.Statistics in medicine · 2023Article
- Designing three-level cluster randomized trials to assess treatment effect heterogeneity.Biostatistics (Oxford, England) · 2023Article
- A quantitative assessment of the frequency and magnitude of heterogeneous treatment effects in studies of the health effects of social policies.SSM - population health · 2023Article
- Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity.BMC medical research methodology · 2023Article
- Heterogeneous treatment effects in social policy studies: An assessment of contemporary articles in the health and social sciences.Annals of epidemiology · 2022Article
- Accounting for unequal cluster sizes in designing cluster randomized trials to detect treatment effect heterogeneity.Statistics in medicine · 2022Article
- Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials.Statistics in medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundCluster-randomized trials (CRTs) are being increasingly used to test a range of interventions, including medical interventions commonly used in clinical practice. Policies created by the NIH and the Food and Drug Administration (FDA) require the reporting of demographics and the examination of demographic heterogeneity of treatment effect (HTE) for individually randomized trials. Little is known about how frequent demographics are reported and HTE analyses are conducted in CRTs.
objectivesWe sought to understand the prevalence of HTE analyses and the statistical methods used to conduct them in CRTs focused on treating cardiovascular disease, cancer, and chronic lower respiratory diseases. Additionally, we also report on the proportion of CRTs that reported on baseline demographics of its populations and conducted demographic HTE analyses. DATA SOURCES: We searched PubMed and Embase for CRTs published between 1/1/2010 and 3/29/2016 that focused on treating the top 3 Center for Disease Control causes of death (cardiovascular disease, chronic lower respiratory disease, and cancer). Evidence Screening And Review: Of 1,682 unique titles, 117 abstracts were screened. After excluding 53 articles, we included 64 CRT publications and abstracted information on study characteristics and demographic information, statistical analysis, HTE analysis, and study quality.
resultsAge and sex were reported in greater than 95.3% of CRTs, while race and ethnicity were reported in only 20.3% of CRTs. HTE analyses were conducted in 28.1% (n = 18) of included CRTs and 77.8% (n = 12) were prespecified analyses. Four CRTs conducted a demographic subgroup analysis. Only 6/18 CRTs used interaction testing to determine whether HTE existed.
conclusionsBaseline demographic reporting was high for age and sex in CRTs, but was uncommon for race and ethnicity. HTE analyses were uncommon and was rare for demographic subgroups, which limits the ability to examine the extent of benefits or risks for treatments tested with CRT designs.
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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.