Semi-parametric modelling of the distribution of the baseline risk in meta-analysis.
In meta-analysis of clinical trials, often meta-regression analyses are performed to explain the heterogeneity in treatment effects that usually exist between trials. A popular explanatory variable is the risk observed in the control group, the baseline risk. The relationship between the treatment effect and the baseline risk is investigated by fitting a linear model that allows randomness on the true baseline risk by assuming a normal distribution with unknown mean and variance. However, the normality assumption could be too strong to adequately describe the underlying distribution. Therefore, we developed a new semi-parametric method that relaxes the normality assumption to a more flexible and general distribution. We applied a penalized Gaussian mixture distribution to represent the baseline risk distribution. Furthermore, a bivariate hierarchical model is formulated in order to take into account the correlation between the baseline and treatment effect. To fit the proposed model, a penalized likelihood function is maximized by an Expectation Maximization (EM) algorithm. We illustrate our method on a number of simulated data sets and on a published meta-analysis data set.
Ghidey
W
W
Department of Epidemiology and Biostatistics, Erasmus Medical Center, 3000 CA Rotterdam, The Netherlands. w.ghidey@erasmusmc.nl
Lesaffre
E
E
Stijnen
T
T
D000924:Anticholesteremic Agents / Q000627:therapeutic use; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003327:Coronary Disease / Q000188:drug therapy / Q000401:mortality; D015987:Effect Modifier, Epidemiologic; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D015201:Meta-Analysis as Topic; D012016:Reference Values; D012306:Risk; D016008:Statistical Distributions; D018709:Statistics, Nonparametric; D013269:Stochastic Processes; D016019:Survival Analysis; D016896:Treatment Outcome
D000924:Anticholesteremic Agents