Article type
Year
Abstract
Background:
Meta-analysis of prognostic studies usually pools reported measures of association for common factors across studies. Failure to consider non-significant factors that were excluded or not reported in regression models may overestimate pooled measures of association.
Objectives:
Using systematic reviews of predictors of persistent postsurgical pain or unemployment after breast cancer surgery, we explored the impact of imputing data for missing non-significant data on overall associations of risk factors.
Methods:
We pooled predictors to explore their association with persistent pain or unemployment after breast cancer surgery using random-effects models and the DerSimonian-Laird method. For our primary analyses, we imputed an odds ratio (OR) of "1" for predictors that were excluded or not reported in multivariable analyses due to non-significant association. We acquired the associated variance using the hot deck approach. We performed sensitivity analysis by excluding the imputed data for non-significant predictors (analysis of adjusted data reported). We calculated the ratio of odds ratio to estimate the difference between these two approaches.
Results:
Fifty-six studies with 66,740 patients were included. Most of the studies either excluded factors that were not significant in bivariate analysis (32%; 18 of 56) or failed to present data for non-significant predictors in their final regression models (68%; 38 of 56). 24 of 27 risk factors contained missing data for non-significant factors (Table 1).
The median ratio of odds ratio of pooling analyses using imputed data vs. only reported data in the final multivariable models was 1.07 (range: 1.01 to 1.15) for 9 poolable risk factors for persistent pain and 1.05 (range: 1.01 to 1.80) for 15 poolable predictors of unemployment (Figures 1 to 4). All pooled associations were larger in meta-analyses based on reported data only vs. when imputed missing data was considered, which exaggerated the magnitude of association by 1%-55%.
Conclusions:
Primary studies exploring prognostic factors often fail to report data for non-significant predictors. Failure to impute for missing non-significant predictors in meta-analyses systematically overestimates pooled measures of association. Systematic reviewers should acquire missing data for non-significant predictors from authors when possible, and impute data when not.
Patient or healthcare consumer involvement:
No
Meta-analysis of prognostic studies usually pools reported measures of association for common factors across studies. Failure to consider non-significant factors that were excluded or not reported in regression models may overestimate pooled measures of association.
Objectives:
Using systematic reviews of predictors of persistent postsurgical pain or unemployment after breast cancer surgery, we explored the impact of imputing data for missing non-significant data on overall associations of risk factors.
Methods:
We pooled predictors to explore their association with persistent pain or unemployment after breast cancer surgery using random-effects models and the DerSimonian-Laird method. For our primary analyses, we imputed an odds ratio (OR) of "1" for predictors that were excluded or not reported in multivariable analyses due to non-significant association. We acquired the associated variance using the hot deck approach. We performed sensitivity analysis by excluding the imputed data for non-significant predictors (analysis of adjusted data reported). We calculated the ratio of odds ratio to estimate the difference between these two approaches.
Results:
Fifty-six studies with 66,740 patients were included. Most of the studies either excluded factors that were not significant in bivariate analysis (32%; 18 of 56) or failed to present data for non-significant predictors in their final regression models (68%; 38 of 56). 24 of 27 risk factors contained missing data for non-significant factors (Table 1).
The median ratio of odds ratio of pooling analyses using imputed data vs. only reported data in the final multivariable models was 1.07 (range: 1.01 to 1.15) for 9 poolable risk factors for persistent pain and 1.05 (range: 1.01 to 1.80) for 15 poolable predictors of unemployment (Figures 1 to 4). All pooled associations were larger in meta-analyses based on reported data only vs. when imputed missing data was considered, which exaggerated the magnitude of association by 1%-55%.
Conclusions:
Primary studies exploring prognostic factors often fail to report data for non-significant predictors. Failure to impute for missing non-significant predictors in meta-analyses systematically overestimates pooled measures of association. Systematic reviewers should acquire missing data for non-significant predictors from authors when possible, and impute data when not.
Patient or healthcare consumer involvement:
No