Moving Beyond the Comfort Zone in Practicing Translational Statistics

Lee-Jen Wei, PhD
Professor in the Department of Biostatistics, Harvard T.H. Chan School of Public Health
Senior Statistician, Statistical and Data Analysis Center
Director, Industry Partnership Program, Harvard T.H. Chan School of Public Health

Abstract
A translational statistic is an empirical summary of data for making inferences about a population quantity of interest, which can be interpreted clinically and comprehended heuristically for decision makings by clinicians and patients, for example, for treatment selections. Like translational medicine, translational statistical research is to utilize basic data science findings to the real-world clinical practice. Unfortunately, many commonly used statistics in clinical studies are not readily translatable. For example, a p-value does not offer any clinically meaningful interpretation. Estimation is more informative. However, some commonly used estimation procedures may result in ambiguous, uninterpretable conclusions. For example, to evaluate an association between a biomarker and an outcome variable in the presence of various confounders, multivariate regression “working” models are routinely used to estimate the regression coefficient to quantify the strength of an association. The resulting estimates from different working models may estimate quite different parameters, which are likely not the original parameter quantifying the association. Therefore, it is difficult, if not impossible, to quantify the impact from this biomarker on the outcome variable. In practice, we still rely on the p-value to assess the association qualitatively. In fact, if the p-value for testing the regression coefficient being 0, is less than 0.05, one usually claims that this biomarker is an “independent predictor.” Such a claim is ambiguous and has very little value in clinical practice.

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