University of Pennsylvania 7th annual conference on statistical issues in clinical trials: Current issues regarding the use of biomarkers and surrogate endpoints in clinical trials (afternoon panel discussion session)

James Hung: Disclaimer: The comments given here reflect only my personal views and should not be construed to represent the view or the policy of the Food and Drug Administration. The four presentations were clearly and well delivered. I will try to pinpoint the essence of each talk and make some suggestions. Dr Zee’s presentation is very interesting in that a marker or markers yield a true endpoint with relatively good precision, whereas a clinical diagnosis generates a (surrogate) endpoint with measurement error. This scenario is rather different from the scenario we are often used to where, at least in a clinical trial setting, the primary endpoint is based on clinical diagnosis and the marker is often challenged in terms of prognostic or predictive nature. I wonder how good the cerebrospinal fluid (CSF) is as a marker for Alzheimer’s disease; its utility seems to remain unsettled. Regardless, she presented a very viable and good approach to reducing bias in estimation of covariate effect and improving relative efficiency. The approach combines the data of the patients who have surrogate data and true endpoint data (internal validation subsample) and the data of the patients who have only surrogate data (internal non-validation subsample), assuming that the patients in the non-validation set and the patients in the validation set are basically the same type of patients. In the clinical setting, we are often faced with the scenario that the treatment effect on the marker is most likely larger than the treatment effect on the clinical endpoint of interest. In Dr Zee’s article, the covariate effect (b) seems equal for the surrogate and the endpoint. But this is not really important because what is of ultimate interest is the covariate effect on the clinical endpoint. Besides, perhaps in most practical situations, the clinical endpoint is also marker based; for instance, the definition of myocardial infarction may be based on markers (e.g. creatine kinase-MB (CKMB), troponin), where the optimal cutoff values of the markers in determination of the disease or event are still far from certain. Thus, it will be interesting to link this scenario to that in her work. Regarding surrogacy, it can be discussed at a patient level or at a trial level. That is, the question can be how predictive the marker is of the clinical event of interest for each patient, or how well the treatment effect on the marker can translate into the treatment effect on the clinical benefit on average. Multiple trials are needed to address the latter issue. Perhaps this contrast needs to be thought about in further research. Dr Kattan argues that the value of adding a new marker to a model that contains an existing set of markers is primarily about whether the addition can improve prediction substantially. The new model may be quite different from the old models, even in the functional form. Ideally, the best new model with the new marker should be sought and compared to the best old model without the new marker. For the comparison, prediction accuracy is the most important parameter, and thus, it might not be useful to look at correlation, hazard ratio, or p value in multivariate analyses. These are all reasonable points. In practice, however, there is often no gold standard even for defining the disease or actual clinical outcome we care about. Many clinical outcomes are still defined using markers, unless they are clearly symptomatic. In multivariate analyses, the cutoffs used for new markers are very likely to be affected by what established markers are included in the model. People still use these approaches for variable selection in model building, in addition to the domain expert’s suggestion. To me, use of these statistics for variable selection can be very confusing and the domain expert’s suggestion