In prevention research, count or frequency variables are common, such as the number of drinks consumed per week. Such outcomes are typically highly skewed with high frequencies of zeroes and more appropriately modeled using zero-altered count regression approaches. Another key challenge in meta-analysis is how to accommodate studies with heterogeneous designs, including differing numbers of treatment arms and longitudinal assessments. Typical options such as collapsing multiple treatment arms into a single arm, excluding treatment arms or studies, or conducting multiple univariate meta-analyses are not ideal due to loss of information and inefficient use of data.
We present a Bayesian multilevel modeling (BMLM) approach to highly skewed count outcomes with many zeroes in a one-step IPD meta-analysis that simultaneously accommodates between-study differences in the number of treatment groups and the number and timing of assessments when deriving an overall effect size estimate. A version of this modeling approach was used in a previous substantively-focused paper, which has been developed further for dissemination. We will provide an in-depth examination of the methodological issues encountered in our previous IPD meta-analysis, a rationale for the analysis approach we developed, and how prevention researchers can apply the same method to their own meta-analyses, using provided code in R.
Methods: We used a novel formulation of a Bayesian multilevel hurdle negative binomial model to calculate study-specific and overall treatment effects on zero-altered drinking outcomes across heterogenous studies. Illustrative data come from Project INTEGRATE, an IPD meta-analysis study of brief motivational interventions to reduce excessive alcohol use and related harm among college students.
Results: The one-step BMLM we developed was a feasible approach for producing study-specific and overall estimates of treatment effects with highly skewed count data. Sensitivity analyses supported the validity and robustness of the treatment effect estimates.
Conclusions: One-step IPD meta-analysis provides a feasible and flexible approach for combining IPD from heterogeneous studies that leverages all available information, while accommodating common differences in study design (e.g., varying treatment arms).