The BBR Course Notes §§14.4–5 thoroughly impeach change-from-baseline analysis. I had previously avoided the practice simply on regression-toward-the-mean grounds and because @f2harrell said so. But I find myself really astounded to (re?)discover how comprehensive and multifaceted a case can be made against it!
That said, I think sometimes the best way to discourage bad practices is to provide positive examples of the good practice. This may be especially true when we are reaching out to non-statisticians who may have real, pressing interests in promoting sound statistical analyses. (FYI, this question is prompted by this petition from lymphoma patient advocate Karl Schwartz.)
Can anyone point to exemplary analyses of QOL, especially in the clinical trials literature, that avoid change-from-baseline pitfalls?
10 Dec 2019 Addendum
In case it helps focus this question a bit better, I’d like to articulate formally the aims of Karl Schwartz’s petition linked above, as I’ve come to understand them after some further discussion with him. As I now understand, the problem originates with the use of surrogate endpoints such as progression-free survival (PFS) in regulatory decision-making. The reliability of such surrogates as predictors of meaningful clinical benefit is debated. From a biostatistical perspective, a natural remedy for such a ‘noisy’ univariate outcome would be to augment it to obtain a multivariate outcome that incorporates other information as well. The petition asks specifically that QoL information derived from patient-reported outcomes (PROs) be incorporated together with PFS in regulatory decisions. Interestingly, this placing of PFS and QoL on a similar footing (i.e., as components of a multivariate outcome) further underscores the correctness of comparing QoL endpoints between arms, as opposed to QoL ‘changes’ from baseline in individuals.