Faculty Opinions recommendation of Heterogeneity of treatment effects in an analysis of pooled individual patient data from randomized trials of device closure of patent foramen ovale after stroke.

2021 ◽  
Author(s):  
George Davey Smith
Keyword(s):  
Patent Foramen Ovale ◽  
Treatment Effects ◽  
Randomized Trials ◽  
Individual Patient Data ◽  
Patient Data ◽  
Foramen Ovale ◽  
Device Closure ◽  
Heterogeneity Of Treatment Effects

Heterogeneity of Treatment Effects in an Analysis of Pooled Individual Patient Data From Randomized Trials of Device Closure of Patent Foramen Ovale After Stroke

JAMA ◽  
2021 ◽  
Vol 326 (22) ◽  
pp. 2277
Author(s):  
David M. Kent ◽  
Jeffrey L. Saver ◽  
Scott E. Kasner ◽  
Jason Nelson ◽  
John D. Carroll ◽  
...  
Keyword(s):  
Patent Foramen Ovale ◽  
Treatment Effects ◽  
Randomized Trials ◽  
Individual Patient Data ◽  
Patient Data ◽  
Foramen Ovale ◽  
Device Closure ◽  
Heterogeneity Of Treatment Effects

scholarly journals Extending the I-squared statistic to describe treatment effect heterogeneity in cluster, multi-centre randomized trials and individual patient data meta-analysis

2020 ◽  
pp. 096228022094855
Author(s):  
Karla Hemming ◽  
James P Hughes ◽  
Joanne E McKenzie ◽  
Andrew B Forbes
Keyword(s):  
Randomized Trial ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Randomized Trials ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Cluster Randomized Trial ◽  
Patient Data ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

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.

Evaluation of treatment effects in patients with endometrial cancer and POLE mutations: An individual patient data meta‐analysis

Cancer ◽  
2021 ◽  
Author(s):  
Jessica N. McAlpine ◽  
Derek S. Chiu ◽  
Remi A. Nout ◽  
David N. Church ◽  
Pascal Schmidt ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Treatment Effects ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data

scholarly journals Age and organ damage correlate with poor survival in myeloma patients: meta-analysis of 1435 individual patient data from 4 randomized trials

Haematologica ◽  
2013 ◽  
Vol 98 (6) ◽  
pp. 980-987 ◽  
Author(s):  
S. Bringhen ◽  
M. V. Mateos ◽  
S. Zweegman ◽  
A. Larocca ◽  
A. P. Falcone ◽  
...  
Keyword(s):  
Randomized Trials ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Organ Damage ◽  
Patient Data ◽  
Poor Survival
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