8 citations · 11 across the 9 of their papers we have counts for
9 papers
Accounting for Heavy Censoring in Evaluating the Risk Stratification Abilities of Existing Models for Time to Diagnosis of Huntington Disease
Kyle F. Grosser, Abigail G. Foes, Stellen Li +3
Huntington disease (HD) is a neurodegenerative disease with progressively worsening symptoms. Accurately modeling time to HD diagnosis is essential for clinical trial design. Langb…
Improved computational efficiency and stability when imputing censored covariates: Analytic and numerical approaches
Sarah C. Lotspeich, Ethan M. Alt
Imputation is a popular approach to handling censored, missing, and error-prone covariates -- all coarsened data types for which the true values are unknown. However, there are nua…
The `Why' behind including `Y' in your imputation model
Lucy D'Agostino McGowan, Sarah C. Lotspeich, Staci A. Hepler
Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used…
Mission Imputable: Correcting for Berkson Error When Imputing a Censored Covariate
Kyle F. Grosser, Sarah C. Lotspeich, Tanya P. Garcia
To select outcomes for clinical trials testing experimental therapies for Huntington disease, a fatal neurodegenerative disorder, analysts model how potential outcomes change over…
Quantifying the HIV reservoir with dilution assays and deep viral sequencing
Sarah C. Lotspeich, Brian D. Richardson, Pedro L. Baldoni +2
People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these res…
Extrapolation before imputation reduces bias when imputing censored covariates
Sarah C. Lotspeich, Tanya P. Garcia
Modeling symptom progression to identify informative subjects for a new Huntington's disease clinical trial is problematic since time to diagnosis, a key covariate, can be heavily…
Three-phase generalized raking and multiple imputation estimators to address error-prone data
Gustavo Amorim, Ran Tao, Sarah Lotspeich +4
Validation studies are often used to obtain more reliable information in settings with error-prone data. Validated data on a subsample of subjects can be used together with error-p…
Correcting Conditional Mean Imputation for Censored Covariates and Improving Usability
Sarah C. Lotspeich, Kyle F. Grosser, Tanya P. Garcia
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. Wh…
Optimal Multi-Wave Validation of Secondary Use Data with Outcome and Exposure Misclassification
Sarah C. Lotspeich, Gustavo G. C. Amorim, Pamela A. Shaw +2
The growing availability of observational databases like electronic health records (EHR) provides unprecedented opportunities for secondary use of such data in biomedical research.…