Addressing Bias in Non-Experimental Studies Assessing Treatment Outcomes in Prostate Cancer
Abstract
We evaluated the ability of matching techniques to balance baseline characteristics between treatment groups using non-experimental data. We identified a set of balance diagnostics that assessed key differences in baseline covariates with potential for confounding. These diagnostics were used in a novel systematic approach to developing and evaluating models for use in propensity score matching that optimized balance and data retention. We then compared the performance of propensity score and coarsened exact matching strategies in optimizing balance and data retention, using non-experimental data from a pan-Canadian prostate cancer database. Both matching techniques balanced baseline covariates adequately and retained approximately 70% of the data. To further study the role of treatment selection and prostate cancer outcomes, we performed a systematic review and meta-analysis that examined the rate of prostate cancer-specific mortality among those with high-risk non-metastatic prostate cancer who were initially treated with radiation or surgery. No statistically significant difference was found between groups in this analysis. In follow-up to this analysis, we compared the rate of metastatic progression following treatment between those with unfavourable-risk non-metastatic prostate cancer and treated with radiation or surgery, using data acquired from two Ontario cancer centres. Results from this comparison showed no statistically significant difference between treatment groups. In summary, a systematic approach to matching can be effective in balancing baseline covariates and producing more accurate effect estimates from non-experimental data. Moreover, treatment selection in the realm of higher risk prostate cancer does not appear to significantly influence important oncological outcomes.