arXiv subjects

Kyle F. Grosser

Publications and source records attributed to Kyle F. Grosser.

3 recordsLinked to original sources

arXiv2026

Accounting for Heavy Censoring in Evaluating the Risk Stratification Abilities of Existing Models for Time to Diagnosis of Huntington Disease

Huntington disease (HD) is a neurodegenerative disease with progressively worsening symptoms. Accurately modeling time to HD diagnosis is essential for clinical trial design. Langbehn's model, the CAG-Age Product (CAP) model, the Prognostic Index Normed (PIN) model, and the Multivariate Risk Score (MRS) model have all been proposed for this task. However, these models may yield conflicting predictions and few studies have systematically compared their performance. Further, those that have could be misleading due to testing the models on the same data used to train them and failing to account for high rates of right censoring (80%+) in performance metrics. We discuss the theoretical foundations of these models, offering intuitive comparisons about their practical feasibility. We externally validate their risk stratification abilities using data from the ENROLL-HD study and two censoring-appropriate performance metrics, guiding model selection for HD clinical trial design. As these models were developed in HD studies that ended more than a decade ago, we compared their predictive performance using published parameters versus updated ones (re-estimated using ENROLL-HD). We show how these models can be used to estimate sample sizes for an HD clinical trial. Based on either metric and using published or updated parameters, the MRS model, which incorporates the most covariates, performed best. However, the simpler PIN model offered similarly good performance while requiring fewer variables, many of which would require patients to undergo additional tests. In illustrating an HD clinical trial design, we defined an optimal threshold based on model performance metrics to determine which patients are more likely to be diagnosed. Sample size calculations using an optimal threshold based on metrics that did not account for censoring, as in previous studies, are shown to lead to underpowered trials.

stat.AP↗

arXiv2023

Mission Imputable: Correcting for Berkson Error When Imputing a Censored Covariate

To select outcomes for clinical trials testing experimental therapies for Huntington disease, a fatal neurodegenerative disorder, analysts model how potential outcomes change over time. Yet, subjects with Huntington disease are often observed at different levels of disease progression. To account for these differences, analysts include time to clinical diagnosis as a covariate when modeling potential outcomes, but this covariate is often censored. One popular solution is imputation, whereby we impute censored values using predictions from a model of the censored covariate given other data, then analyze the imputed dataset. However, when this imputation model is misspecified, our outcome model estimates can be biased. To address this problem, we developed a novel method, dubbed "ACE imputation." First, we model imputed values as error-prone versions of the true covariate values. Then, we correct for these errors using semiparametric theory. Specifically, we derive an outcome model estimator that is consistent, even when the censored covariate is imputed using a misspecified imputation model. Simulation results show that ACE imputation remains empirically unbiased even if the imputation model is misspecified, unlike multiple imputation which yields >100% bias. Applying our method to a Huntington disease study pinpoints outcomes for clinical trials aimed at slowing disease progression.

stat.ME↗

arXiv2021

Correcting Conditional Mean Imputation for Censored Covariates and Improving Usability

Analysts are often confronted with censoring, wherein some variables are not observed at their true value, but rather at a value that is known to fall above or below that truth. While much attention has been given to the analysis of censored outcomes, contemporary focus has shifted to censored covariates, as well. Missing data is often overcome using multiple imputation, which leverages the entire dataset by replacing missing values with informed placeholders, and this method can be modified for censored data by also incorporating partial information from censored values. One such modification involves replacing censored covariates with their conditional means given other fully observed information, such as the censored value or additional covariates. So-called conditional mean imputation approaches were proposed for censored covariates in Atem et al. [2017], Atem et al.[2019a], and Atem et al. [2019b]. These methods are robust to additional parametric assumptions on the censored covariate and utilize all available data, which is appealing. As we worked to implement these methods, however, we discovered that these three manuscripts provide nonequivalent formulas and, in fact, none is the correct formula for the conditional mean. Herein, we derive the correct form of the conditional mean and demonstrate the impact of the incorrect formulas on the imputed values and statistical inference. Under several settings considered, using an incorrect formula is seen to seriously bias parameter estimation in simple linear regression. Lastly, we provide user-friendly R software, the imputeCensoRd package, to enable future researchers to tackle censored covariates in their data.

stat.ME↗