The Use of Individual Participant Data (IPD) for Examining Heterogeneity in Meta-analysis of Observational Studies: An Application to Biomechanical Workplace Risk Factors and Low Back Pain

Background: The use of meta-analysis to combine the results of observational studies is controversial. Despite its common use, methodological work in this area is lacking. Because of the diversity of study designs, exposure and outcome measures, and differential adjustment for confounding variables, the identification of sources of heterogeneity among study effect estimates is particularly important when combining data from observational studies. This thesis presents the results of a study that examines the relative ability of individual participant data (IPD) meta-analysis (which was considered a “gold standard”) and traditional aggregate data (AD) meta-analysis to identify sources of heterogeneity among studies examining mechanical exposure and low back pain (LBP) in workers. Materials and Methods: A systematic literature search was conducted to identify relevant articles. The corresponding author of each article was contacted to request their individual-level data. Because the outcome definitions and exposure measures were not uniform across studies, two sub-studies were conducted 1) to identify sets of outcome definitions that could be combined in a meta-analysis and 2) to develop methods to translate mechanical exposure onto a common metric. IPD analyses were conducted using generalized estimating equation (GEE) regression to identify variables that acted as strong confounders and effect modifiers. Traditional AD meta-analysis was also conducted and potential sources of heterogeneity were tested using meta-regression. Key Findings: (1) Overall, we found an association between both forces and postures on LBP, although the magnitude varied depending on the exposure-outcome combination. Among the outcomes, the ORs tended to be highest for sick leave due to LBP. (2) There was very little evidence of strong confounders in the relationship between mechanical exposure and LBP; thus differential adjustment for confounders in studies would not likely be an important source of heterogeneity in an AD meta-analysis. (3) AD meta-analysis was able to identify the same study-level effect modifiers as IPD meta-analysis, but did not consistently identify individual-level effect modifiers. Both individual-level characteristics (older age and being male), and study-level characteristics (population-based studies and self-reported mechanical exposure), were associated with an increased OR for many of the LBP outcome and mechanical exposure combinations. Conclusion: AD meta-analysis is likely sufficient to detect heterogeneity for study-level factors but is not sufficient to identify individual-level effect modifiers. When the primary source of evidence in a research area is observational studies and when there is controversy despite several systematic reviews, IPD meta-analysis can be used to better understand sources of heterogeneity and provide contextPh