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Title: Overcoming censored predictors with imputation to model the progression of Huntingtons Disease Authors:  Sarah Lotspeich - Wake Forest University (United States) [presenting]
Abstract: Clinical trials to test experimental treatments for Huntington's disease are expensive, so it is prudent to enrol subjects whose symptoms may be most impacted by the treatment during follow-up. However, modeling how symptoms progress to identify such subjects is problematic since time to diagnosis, a key predictor, can be censored. Imputation is an appealing strategy where censored predictors are replaced with their conditional means, the calculation of which requires estimating and integrating over its conditional survival function from the censored value to infinity. However, despite efforts to make conditional mean imputation as flexible as possible, it still makes restrictive assumptions about the censored predictor (such as proportional hazards) that may not hold in practice. We develop a suite of extensions to conditional mean imputation to encourage its applicability to a wide range of clinical settings. We adopt new estimators for the conditional survival function to offer more efficient and robust inference and propose an improved conditional mean calculation. We discuss in simulations when each version of conditional mean imputation is most appropriate and evaluate our methods as we model symptom progression from Huntington's disease data. Our imputation suite is implemented in the open-source R package, imputeCensRd.