Abstract Number:
3179
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Kyle Grosser (1), Tanya Garcia (2), Sarah Lotspeich (3)
Institutions:
(1) University of North Carolina, N/A, (2) N/A, N/A, (3) Wake Forest University, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Huntington disease is a rare neurodegenerative disorder for which no effective therapies have yet been discovered. To help clinicians search for effective therapies that halt or slow the progression of the disease, analysts seek to model the progression of symptoms before and after diagnosis. However, many studies that track symptom progression end before all participants have been diagnosed. This presents a challenge: How do we model symptom progression given time of diagnosis, a right-censored covariate? Analysts frequently meet this challenge by imputing the time of diagnosis for undiagnosed participants. However, if the model used to impute the time to diagnosis is misspecified, it can introduce bias into the symptom progression model. This bias arises because the misspecified imputation model generates values that are prone to significant errors. To mitigate this estimation bias, we adopt a semiparametric technique to correct for imputation errors, enabling us to reliably estimate linear longitudinal models even in the presence of covariate censoring. Our novel approach is presented, and we assess its performance when applied to an observational study of Huntington disease.
Keywords:
censored covariate|imputation|longitudinal data|measurement error|semiparametric theory|
Sponsors:
Biometrics Section
Tracks:
Missing Data
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