Developing a Robust and Efficient Strategy for Censored Covariates to Improve Clinical Trial Design for Neurodegenerative Diseases

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Project Summary: Developing disease-modifying therapies for neurodegenerative diseases has been challenging, in part because accurate statistical models to identify the optimal time for intervention do not exist. Models of how symptoms worsen over time (i.e., the symptom trajectory) before and after a clinical diagnosis can help identify that optimal time. These models can help pinpoint when a therapy could prevent a clinical diagnosis, or slow the disease after a clinical diagnosis. Yet modeling the symptom trajectory is not easy even for Huntington disease, a disease for which researchers can track symptoms in patients guaranteed to develop it. Like other neurodegenerative diseases, Huntington disease progresses slowly over decades, so studies that track symptoms often end before clinical diagnosis. This makes time to clinical diagnosis right-censored (i.e., a patient's motor abnormalities will merit a clinical diagnosis sometime after the last study visit, but exactly when is unknown), leaving researchers with the challenge of trying to model the symptom trajectory before and after clinical diagnosis without full information about when clinical diagnosis occurs. The challenge creates a unique statistical problem of modeling the symptom trajectory as a function of a right-censored covariate, time to clinical diagnosis. Tackling this problem by modeling the distribution for time to clinical diagnosis has long been thought to be the best strategy. For years, we and others worked to develop reliable distribution models, but we found that if the model is even slightly wrong, we get biased estimates of how the symptom trajectory changes as a function of time to clinical diagnosis. This bias causes problems for clinical trials because they are incorrectly powered to determine if a therapy modifies the disease course with statistical significance. We began seeking a strategy that estimates the symptom trajectory as a function of time to clinical diagnosis without needing to accurately model the distribution for time to clinical diagnosis. Our team developed such a strategy for a related problem: estimating a regression model that has a covariate measured with error. Like a right-censored covariate, when a covariate is measured with error, the covariate's true value and distribution are unknown. Rather than finding the correct distribution, our nontraditional strategy accurately estimates the regression model even when the distribution for the covariate is mismodeled. Our overarching objective is to develop a similarly robust strategy when we have a right-censored covariate, which requires tackling challenges in three new areas: noninformative censoring (Aim 1), informative censoring (Aim 2), and handling longitudinal measures of the symptom trajectory (Aim 3). Upon completion, our work will produce robust estimates of the Huntington disease symptom trajectory as a function of time to clinical diagnosis. The work is timely, given recent therapies that show potential for modifying the course of Huntington disease. Correctly powered clinical trials will enable researchers to test these therapies and determine if they modify the disease course. Our strategy could help design these clinical trials and push forward the science of Huntington disease and other neurodegenerative diseases.