SynthesisStatistical methods in medical research2021
Extending the I-squared statistic to describe treatment effect heterogeneity in cluster, multi-centre randomized trials and individual patient data meta-analysis.
Synthesis in Statistical methods in medical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- L5 vs. pelvic fixation as the lowest instrumented vertebra in long-segment fusion for adult spinal deformity: a systematic review and meta-analysis.Spine deformity · 2026Pooled it
- Long-term patient-reported outcomes and mechanical complications following spinal fusion in patients with surgical site infection managed with retained instrumentation versus non-infected patients: a systematic review and meta-analysis.Journal of spine surgery (Hong Kong) · 2026Article
- Impact of frailty on surgical outcomes in degenerative cervical myelopathy: a systematic review and meta-analysis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- Impact of Cervical Sagittal Alignment Changes on Postoperative C5 Palsy: A Systematic Review and Meta-Analysis.Neurosurgery practice · 2026Review
- Impact of Expandable versus Static Interbody Cages on Subsidence and Outcomes in Minimally Invasive Transforaminal Lumbar Interbody Fusion: A Systematic Review and Meta-Analysis.Global spine journal · 2026Review
- Reflections on the I-squared index for measuring inconsistency in meta-analysis.Research synthesis methods · 2026Review
- Change ofFrontiers in psychology · 2026Article
- Article
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- Should intra-articular hyaluronic acid be used routinely for knee osteoarthritis pain?PM & R : the journal of injury, function, and rehabilitation · 2022Article
- Dolutegravir Monotherapy as Maintenance Strategy: A Meta-Analysis of Individual Participant Data From Randomized Controlled Trials.Open forum infectious diseases · 2022Article
- All-cause and in-hospital mortality after aspirin use in patients hospitalized with COVID-19: a systematic review and meta-analysis.Biology methods & protocols · 2022Review
- Risk factors for cervical kyphotic deformity following laminoplasty in degenerative cervical myelopathy: A systematic review and meta-analysis.Journal of craniovertebral junction & spineReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Treatment effect heterogeneity is commonly investigated in meta-analyses to identify if treatment effects vary across studies. When conducting an aggregate level data meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. The effect of a treatment might also vary across clusters in a cluster randomized trial, or across centres in multi-centre randomized trial, and it can be of interest to explore this at the analysis stage. In cross-over trials and other randomized designs, in which clusters or centres are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. Here we derive and evaluate a comparable I-squared measure to describe the magnitude of heterogeneity in treatment effects across clusters or centres in randomized trials. We further show how this methodology can be used to estimate treatment effect heterogeneity in an individual patient data meta-analysis.
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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.