Table 4.
Recommendations for design and analysis of observational studies on glucose-lowering medications and incidence of cancer
| - Include patients who initiate the drug(s) of interest (new user design) which increases the chances of identifying more comparable patients with respect to the underlying risk of cancer, and is particularly suited to detect and evaluate medication effects that vary over time |
| - Identify comparison groups of new users of medications with similar medical indications and disease stage (active comparator) |
| - Follow a well-defined temporal sequence of study inclusion, covariate assessment, exposure definition, and start of follow-up, to reduce the chances of immortal time bias |
| - Consider specific cancers and specific glucose-lowering medications; consider validating the outcomes if previously validating algorithms are not available |
| - Exclude individuals with prior cancer history |
| - Consider lag and latency times based on cancer biology and use sensitivity analyses to explore and identify the optimal exposure risk window with regard to the lag time between exposure and start of follow-up and the grace period after drug discontinuation |
| - Adjust adequately for all baseline factors associated with the choice of treatment and the risk of specific cancers. Consider implementing techniques that guarantee a high-dimensional confounding adjustment, such as propensity score methodology |
| - Investigate the possibility of differential surveillance and detection bias prior to and following diabetes therapy initiation and account for it in the analysis or in the interpretation of the results |
| - Include intention-to-treat analyses that do not censor patients when a treatment is stopped or changes, but also consider analyses accounting for adherence to the initial treatment over follow-up. If data on the drivers of adherence and treatment changes are reliably collected, adjustments should be done using methods that allow for control for time-varying confounders (marginal structural models and other g-estimation methods). |
| - Consider multiple sensitivity analyses to assess robustness of findings |