Causes of heterogeneity between studies were looked for.
The meta-regression may have identified the causes of heterogeneity as the intervention method, comparator and trial design.
Specifically, we examine underlying causes of heterogeneity in randomized clinical trials (RCTs) conducted thus far and highlight the complexity of the human oxytocin system.
This chapter outlines current theories explaining the origin and proposed genomic causes of heterogeneity before discussing the potential influences of nongenetic context-dependent mechanisms.
Addressing the major causes of heterogeneity remains therefore a major issue.
We applied the random effects meta-analysis to adjust for the differences between studies, and the possible causes of heterogeneity were explored if covariate data at baseline (e.g., age, percentage of sex, type of AC injury, approach, number of bundle, timing of plate removal, duration before surgery and type of studies) were available.
This was used to calculate a weight term that accounted for variations between studies (w_{i}^ = frac{1}{{text{var} (p_{1} ) = tau^{2} }}) and then the pooled prevalence was estimated using the random effects model as follows: (95% ;{text{CI}} = bar{p} ^ pm frac{1.96}{{sqrt {sum w_{i}^.) Meta-regression analysis was then applied to explore causes of heterogeneity [15, 54].
Twelve reviews investigated causes of heterogeneity.
Nineteen reviews (59%) did not explore potential causes of heterogeneity for relevant trial groups.
Lastly, subgroup analyses and meta-regression are, by disposition, exploratory tools to provide pointers towards possible sources of heterogeneity - they cannot be taken as confirmatory tests for definitive conclusions and interpretations about the causes of heterogeneity.
The conclusions of this review only allow us to make some cautious recommendations, due to the limited data and the problem of non-independence when exploring possible causes of heterogeneity in effect size.
