Thanks for the great references. They are both excellent reads. I would add Ken Kowalski’s paper in Statistics in Biopharmaceutical Research (May 2015, 7(2), pp. 148-159) to the mix as well.
Many of the points raised result from lack of alignment around the purpose of the given study. Is it to explore what might happen and develop hypotheses as to why, is it to continue to build on what we have learned so far and further our understanding, or is it to make decisions? Sheiner argues to “restore intellectual primacy to the questions we ask, not the methods by which we answer them.” Unfortunately, this is not an “either/or” choice. Scientists must recognize that both aspects are required. Asking the right questions with appropriate clarity is essential; however, the methods need to then be tailored to those questions for the intended application. Nor is it possible to eliminate judgments form the design, analysis, or interpretation of research. Good science demands transparency in these judgments and their implication. They cannot be abdicated to “decision-makers”.
Meaningful research cannot be conducted without appropriate consideration to the application of the research. The questions the research is designed to answer need to be relevant to the application, and the methods used to answer the questions then need to meet the specific needs. A set of pairwise comparisons cannot provide information about the time course of drug response, and an exposure-response model cannot determine whether the effect observed for a given dose is larger than the observed variability. Neither answers the question “Does the drug work?”
We should be able to align on which analyses make the most sense to use for which questions for which applications. Certain approaches are better suited for exploration, others for learning, and others for decision-making. There is no reason why data from a given study can’t be used for all, as long as we are transparent in our intentions.
Continued advances in innovative designs and model-informed drug development will require that we work together. We must synergistically apply the best approaches of both sciences with appropriate rigor to accelerate delivery of more effective and safer medicines to patients. Ideally, we can do this while increasing our confidence and transparency in the reliability of our conclusions.