I do not argue the non-ajusted estimates are biased. I argue that in “small” and “moderate” size the exchangeability of treatment arms may be compromised and that small differences in several prognostic factors could lead to significant bias in the estimate of effect. This can not be appreciated in univariate comparisons of the distribution of prognostic factors across treatment groups, which is what is presented in Table 1. Therefore, if I see small differences in several prognostic factors or if I see a large difference in a single prognostic factor, I would present crude and adjusted estimates, and would give more weight to the adjusted one, for the purpose of inferences, if they are different. I also argue that even in the case of “large” trials, adjusting would not introduce bias. This is a direct consequence of the independence between treatment assigned and potential outcome that results from randomization. Therefore, if adjusted and crude estimates differ in a large trial, I’d be inclined to believe something was wrong with the model used for the adjustment. Briefly, there is nothing wrong with adjusting for prognostic factors in a RCT, either from the perspective of precision or bias, unless the model used for the adjustment is misspecified.