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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.24130 |
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| _version_ | 1866917047869374464 |
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| author | McGuinness, Myra B. McKenzie, Joanne E. Forbes, Andrew Hui, Flora Martin, Keith R. Casson, Robert J. Karahalios, Amalia |
| author_facet | McGuinness, Myra B. McKenzie, Joanne E. Forbes, Andrew Hui, Flora Martin, Keith R. Casson, Robert J. Karahalios, Amalia |
| contents | It is recommended that measures of between-study effect heterogeneity be reported when conducting individual-participant data meta-analyses (IPD-MA). Methods exist to quantify inconsistency between trials via I^2 (the percentage of variation in the treatment effect due to between-study heterogeneity) when conducting two-stage IPD-MA, and when conducting one-stage IPD-MA with approximately equal numbers of treatment and control group participants. We extend formulae to estimate I^2 when investigating treatment-covariate interactions with unequal numbers of participants across subgroups and/or continuous covariates. A simulation study was conducted to assess the agreement in values of I^2 between those derived from two-stage models using traditional methods and those derived from equivalent one-stage models. Fourteen scenarios differed by the magnitude of between-trial heterogeneity, the number of trials, and the average number of participants in each trial. Bias and precision of I^2 were similar between the one- and two-stage models. The mean difference in I^2 between equivalent models ranged between -1.0 and 0.0 percentage points across scenarios. However, disparities were larger in simulated datasets with smaller samples sizes with up to 19.4 percentage points difference between models. Thus, the estimates of I^2 derived from these extended methods can be interpreted similarly to those from existing formulae for two-stage models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24130 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying inconsistency in one-stage individual participant data meta-analyses of treatment-covariate interactions: a simulation study McGuinness, Myra B. McKenzie, Joanne E. Forbes, Andrew Hui, Flora Martin, Keith R. Casson, Robert J. Karahalios, Amalia Methodology It is recommended that measures of between-study effect heterogeneity be reported when conducting individual-participant data meta-analyses (IPD-MA). Methods exist to quantify inconsistency between trials via I^2 (the percentage of variation in the treatment effect due to between-study heterogeneity) when conducting two-stage IPD-MA, and when conducting one-stage IPD-MA with approximately equal numbers of treatment and control group participants. We extend formulae to estimate I^2 when investigating treatment-covariate interactions with unequal numbers of participants across subgroups and/or continuous covariates. A simulation study was conducted to assess the agreement in values of I^2 between those derived from two-stage models using traditional methods and those derived from equivalent one-stage models. Fourteen scenarios differed by the magnitude of between-trial heterogeneity, the number of trials, and the average number of participants in each trial. Bias and precision of I^2 were similar between the one- and two-stage models. The mean difference in I^2 between equivalent models ranged between -1.0 and 0.0 percentage points across scenarios. However, disparities were larger in simulated datasets with smaller samples sizes with up to 19.4 percentage points difference between models. Thus, the estimates of I^2 derived from these extended methods can be interpreted similarly to those from existing formulae for two-stage models. |
| title | Quantifying inconsistency in one-stage individual participant data meta-analyses of treatment-covariate interactions: a simulation study |
| topic | Methodology |
| url | https://arxiv.org/abs/2510.24130 |