Abstract
Meta-analysis of economic evaluation (MAEE) studies is a novel method to quantitatively summarize economic evidence. The Immunization and Vaccine-related Implementation Research Advisory Committee suggested that MAEEs may be useful for providing decision-makers with clear policy recommendations and could facilitate decision-making in countries where context-specific economic evaluations are not available. There is an extra layer of complexity in preparing data for MAEE from multiple studies due to the absence or inconsistent reporting of different economic parameters, and multiple sources of heterogeneity in the data. In this paper, we aim to provide a step-by-step process to prepare the data and statistical methods for performing MAEE. Data harmonization methods were constructed to account for variability in data availability, economic parameters, and heterogeneity of economic evaluation studies (i.e., country income level, currency, time horizon, perspective, modeling approach, and willingness to pay). The basic methods of MAEEs, including identifying and selecting relevant studies, are similar to other systematic reviews. We developed five data extraction scenarios based on the availability of data reported in studies, including the incremental cost (ΔC), incremental effectiveness (ΔE), and incremental cost-effectiveness ratio (ICER), and their associated dispersion. Study reports the mean and variance (Scenario 1) or 95% confidence interval (Scenario 2) of ΔC, ΔE, and ICER for incremental net benefit (INB) and variance calculations. Scenario 3: ΔC, ΔE, and variances are available, but not for the ICER; a Monte Carlo was used to simulate ΔC and ΔE data, variance and covariance can be then estimated leading INB calculation. Scenario 4: Only the cost-effectiveness plane was available, ΔC and ΔE data can be extracted; means of ΔC, ΔE, and variance/covariance can be estimated accordingly, leading to INB and variance estimates. Scenario 5: Only mean cost/outcomes and ICER are available but not for variance and the cost-effectiveness plane. A variance INB can be borrowed from other studies which are similar characteristics, including country income, ICERs, intervention-comparator, time period, country region, and model type and inputs (i.e., discounting, time horizon). The INB and variance were estimated and pooled across studies using a random-effects model as suggested by the comparative efficiency research (COMER). Our data harmonization and meta-analytic methods should be useful for researchers for the synthesis of economic evidence to aid policymakers in decision making.