There are many ways to personalize the diagnosis and treatment of diseases, pharmacogenomics being one of them. Personalization can be based on routinely collected information, molecular signatures, or on repeated trials on the patient whose treatment plan is being devised. However, current emphases in personalized medicine research often ignore characteristics known to impact treatment benefit, in favor of tests that either generate more revenue or are developed with research that is perhaps easier to fund than "low-tech" research. Failure of the research community to fully utilize rich datasets generated by randomized clinical trials only hightens this concern.
Research supporting personalized medicine can be made more rigorous and relevant. For example in acute diseases, multi-period crossover studies can be used to measure individual response to therapy, and these studies can provide an upper bound on the genome by treatment interaction. When patient by treatment interaction is demonstrated, crossover studies can form an ideal basis for pharmacogenomics. However, even with the best within-patient data, group average treatment effects need to be incorporated in order for predictions for individual patients to have high precision.
There are a few ways to do personalized medicine well but a multitude of ways to do it poorly. Biomarker research in particular has not fulfilled its early promises, a major reason being flawed methodology. The flaws include faulty experimental design, bias, overfitting, weak validation, irreproducible research, data processing and analysis practices, and failure to rigorously show that the new markers add information to readily available clinical data. This will be discussed in terms of Platt's concept of "strong inference", seeking alternative explanations of findings, and sensitivity analysis.
This talk is also a call for the biostatistics and clinical epidemiology communities to be more integrally involved in research related to personalized medicine.