Evidence-based guidelines recommend adjuvant chemotherapy in early stage breast cancer whenever treatment benefit is considered sufficient to outweigh the associated risks. However, many groups of patients were either excluded from or underrepresented in the clinical trials that form the evidence base for this recommendation. This study aims to determine whether using administrative health care data-real world data-and econometric methods for causal analysis to provide "real world evidence" (RWE) are feasible methods for addressing this gap.
Cases of primary breast cancer in women from 2001 to 2015 were extracted from the Scottish cancer registry (SMR06) and linked to other routine health records (inpatient and outpatient visits). Four methods were used to estimate the effect of adjuvant chemotherapy on disease-specific and overall mortality: (1) regression with adjustment for covariates, (2) propensity score matching, (3) instrumental variables analysis, and (4) regression discontinuity design. Hazard ratios for breast cancer mortality and all-cause mortality were compared to those from a meta-analysis of randomized trials.
A total of 39,805 cases were included in the analyses. Regression adjustment, propensity score matching, and instrumental variables were feasible, whereas regression discontinuity was not. Effectiveness estimates were similar between RWE and randomized trials for breast cancer mortality but not for all-cause mortality.
RWE methods are a feasible means to generate estimates of effectiveness of adjuvant chemotherapy in early stage breast cancer. However, such estimates must be interpreted in the context of the available randomized evidence and the potential biases of the observational methods.