Statistical Inference in Diagnostic Studies: Tackling Boundaries and Imperfect Measures

• Applicants: Dr. Felix Fischer; Professor Dr. Frank Konietschke
• Subject Area: Epidemiology and Medical Biometry/Statistics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 530401393

This project is a collaborative effort of the Institute for Biometry and Clinical Epidemiology and the Department of Psychosomatic Medicine at Charité Universitätsmedizin Berlin. Our common objective is to develop novel statistical methods to achieve precise and correct statistical inference in diagnostic studies, in particular (1) when model parameters are either close to their boundary or (2) when diagnostic tools do not have perfect accuracy. Although common, both issues are frequently ignored; estimates and statistical inference from diagnostic and epidemiological studies can hence be substantially biased, negatively impacting subsequent trials as well as medical routine, and bearing a significant risk of misinformation of researchers and health policy makers. We take a general biometric and an epidemiological disease-specific perspective to tackle both issues. First, we will develop robust statistical procedures that control the nominal error levels even when the true parameters are close to their boundary (e.g., AUC > .9), including unbiased estimates of variance of the AUC and confidence intervals for a wide range of designs. We will achieve these goals by developing unbiased variance estimators (using U-statistics) as well as a convex combination approach, which combines standard (approximate) with newly inverted score tests, for the computation of the confidence intervals. The quality of the results will be investigated both theoretically as well as empirically in extensive simulation studies. Second, we will properly account for imperfect diagnostic accuracy using Bayesian Latent Class Models in diagnostic and epidemiological studies of depression. Here, the key challenge is to develop appropriate informative prior distributions appropriately reflecting diagnostic properties of both reference standards and commonly used diagnostic tests. We will achieve this by synthesizing the available evidence on diagnostic accuracy of reference standards and incorporating those into diagnostic meta-analysis, developing novel methods to optimize prevalence estimation based on imperfect diagnostic tests, e.g., by avoiding dichotomization of screening tests and by investigating the impact of these methods in meta-analysis and population-based studies of depression prevalence. Together, we will establish a fundamental statistical inference framework along with freely available implementations that explicitly takes imperfect reference standards into account. We will therefore enable researchers to draw accurate statistical inference in a wide range of applications, e.g., diagnostic studies with small samples as well as population-based studies which commonly use imperfect diagnostic tools, and therefore greatly contribute to the accurate analysis of data in diagnostic and epidemiological studies across many different fields.

• DFG Programme: Research Grants