The use of external controls: To what extent can it currently be recommended?

With more and better clinical data being captured outside of clinical studies and greater data sharing of clinical studies, external controls may become a more attractive alternative to randomized clinical trials (RCTs). Both industry and regulators recognize that in situations where a randomized study cannot be performed, external controls can provide the needed contextualization to allow a better interpretation of studies without a randomized control. It is also agreed that external controls will not fully replace RCTs as the gold standard for formal proof of efficacy in drug development and the yardstick of clinical research. However, it remains unclear in which situations conclusions about efficacy and a positive benefit/risk can reliably be based on the use of an external control. This paper will provide an overview on types of external control, their applications and the different sources of bias their use may incur, and discuss potential mitigation steps. It will also give recommendations on how the use of external controls can be justified.     

Number of predictors and multicollinearity: What are their effects on error and bias in regression?

The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate multicollinearity is unrelated to Type I error, but increases Type II error. Investigation of bias suggests that multicollinearity increases the variability in parameter bias, while leading to overall underestimation of parameters. Collinearity also increases VIF. In the case of all diagnostics however, increasing the number of predictors interacts with multicollinearity to compound observed problems.