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Chapter 9 Meta-Regression (20 and 9 Studies) Primary questions: a meta-analysis of studies assessing the incidence of emergency admissions due to adverse drug effects (ADEs) was very heterogenous. A meta-analysis of the risk of infarction in patients with coronary artery disesase and collateral coronary arteries was heterogeneous. What were the causal factors of these heterogeneities. Heterogeneity in meta-analysis makes pooling of the overall data pretty meaningless. Instead, a careful examination of the potential causes has to be accomplished.
Therefore, we will perform a linear regression, and adjust the outcome variable for the differences in days of observation using weighted least square regression. Coefficientsa, Model 1 a b b (Constant) Treat Psych Soc Unstandardized coefficients Standardized coefficients B Std. 237 t Sig. WLS Weight: days of observation…. OK. The above table shows the results. A largely similar pattern is observed, but treatment modality is no more statistically significant. We will now perform a Poisson regression which is probably more appropriate for rate data.
We, subsequently, use again linear regression but now for categorical analysis of race. OK Coefficientsa Model 1 a (Constant) Race2 Race3 Race4 Age Gender Unstandardized coefficients Standardized coefficients B Std. 215 t Sig. 017 Dependent Variable: strengths core The above table shows that race 2–4 are significant predictors of physical strength. The results can be interpreted as follows. , the best predicted physical strength score of a white male of 25 years of age would equal y ¼ 72:65 þ 9:66 À 0:14Ã 25 þ 5:89Ã 1 ¼ 84:7 (on a linear scale from 0 to 100), * ( = sign of multiplication).