One-stage individual participant data meta-analysis models for continuous and binary outcomes: Comparison of treatment coding options and estimation methods
| Autor: | Joie Ensor, Danielle L. Burke, Kym I E Snell, Tim P. Morris, Ian R. White, Amardeep Legha, Dan Jackson, Richard D Riley |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Epidemiology Restricted maximum likelihood HA Binary number 01 natural sciences Generalized linear mixed model law.invention 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Randomized controlled trial Bias law Statistics Cluster Analysis Humans Computer Simulation 030212 general & internal medicine 0101 mathematics Cluster analysis Mathematics Models Statistical Confidence interval Meta-analysis H1 Linear Models Coding (social sciences) |
| Zdroj: | Statistics in medicineReferences. 39(19) |
| ISSN: | 1097-0258 0277-6715 |
| Popis: | A one-stage individual participant data (IPD) meta-analysis synthesises IPD from multiple studies using a general or generalised linear mixed model. This produces summary results (e.g. about treatment effect) in a single step, whilst accounting for clustering of participants within studies (via a stratified study intercept, or random study intercepts) and between-study heterogeneity (via random treatment effects). We use simulation to evaluate the performance of restricted maximum likelihood (REML) and maximum likelihood (ML) estimation of one-stage IPD meta-analysis models for synthesising randomised trials with continuous or binary outcomes. Three key findings are identified. Firstly, for ML or REML estimation of stratified intercept or random intercepts models, a t-distribution based approach generally improves coverage of confidence intervals for the summary treatment effect, compared to a z-based approach. Secondly, when using ML estimation of a one-stage model with a stratified intercept, the treatment variable should be coded using ‘study-specific centering’ (i.e. 1/0 minus the study-specific proportion of participants in the treatment group), as this reduces the bias in the between-study variance estimate (compared to 1/0 and other coding options). Thirdly, REML estimation reduces downward bias in between-study variance estimates compared to ML estimation, and does not depend on the treatment variable coding; for binary outcomes, this requires REML estimation of the pseudo-likelihood, although this may not be stable in some situations (e.g. when data are sparse). Two applied examples are used to illustrate the findings. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text