arXiv subjects

Eric S. Kawaguchi

Publications and source records attributed to Eric S. Kawaguchi.

4 recordsLinked to original sources

arXiv2026

Addressing errors in multiple variables using generalized raking and cumulative probability models

Routinely collected data, such as electronic health record (EHR) data, are frequently used for biomedical research, but these data are prone to errors, which can bias study findings. Validating data in subsamples of records can reduce bias, and the efficiency of estimates can be improved by incorporating in analyses both the error-prone data available on the entire cohort and the validated data available on the subsample. One approach to incorporate both data sources is with generalized raking, which calibrates validation sampling weights using error-prone data from the entire cohort. Motivated by an EHR study of maternal weight gain during pregnancy with a validation subsample, we develop and illustrate generalized raking techniques for cumulative probability models (CPMs). CPMs are robust, rank-based and semiparametric models for continuous, ordinal, or mixed type outcome data. We develop efficient generalized raking estimators for CPMs, evaluate their performance relative to competing methods, and demonstrate the utility and strengths of generalized raking with CPMs in a study that examines factors associated with weight gain during pregnancy.

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arXiv2024

Probability-scale residuals for event-time data

The probability-scale residual (PSR) is defined as $E\{sign(y, Y^*)\}$, where $y$ is the observed outcome and $Y^*$ is a random variable from the fitted distribution. The PSR is particularly useful for ordinal and censored outcomes for which fitted values are not available without additional assumptions. Previous work has defined the PSR for continuous, binary, ordinal, right-censored, and current status outcomes; however, development of the PSR has not yet been considered for data subject to general interval censoring. We develop extensions of the PSR, first to mixed-case interval-censored data, and then to data subject to several types of common censoring schemes. We derive the statistical properties of the PSR and show that our more general PSR encompasses several previously defined PSR for continuous and censored outcomes as special cases. The performance of the residual is illustrated in real data from the Caribbean, Central, and South American Network for HIV Epidemiology.

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arXiv2019

Scalable Algorithms for Large Competing Risks Data

This paper develops two orthogonal contributions to scalable sparse regression for competing risks time-to-event data. First, we study and accelerate the broken adaptive ridge method (BAR), an $\ell_0$-based iteratively reweighted $\ell_2$-penalization algorithm that achieves sparsity in its limit, in the context of the Fine-Gray (1999) proportional subdistributional hazards (PSH) model. In particular, we derive a new algorithm for BAR regression, named cycBAR, that performs cyclic update of each coordinate using an explicit thresholding formula. The new cycBAR algorithm effectively avoids fitting multiple reweighted $\ell_2$-penalizations and thus yields impressive speedups over the original BAR algorithm. Second, we address a pivotal computational issue related to fitting the PSH model. Specifically, the computation costs of the log-pseudo likelihood and its derivatives for PSH model grow at the rate of $O(n^2)$ with the sample size $n$ in current implementations. We propose a novel forward-backward scan algorithm that reduces the computation costs to $O(n)$. The proposed method applies to both unpenalized and penalized estimation for the PSH model and has exhibited drastic speedups over current implementations. Finally, combining the two algorithms can yields $>1,000$ fold speedups over the original BAR algorithm. Illustrations of the impressive scalability of our proposed algorithm for large competing risks data are given using both simulations and a United States Renal Data System data.

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arXiv2018

Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge

This paper develops a new scalable sparse Cox regression tool for sparse high-dimensional massive sample size (sHDMSS) survival data. The method is a local $L_0$-penalized Cox regression via repeatedly performing reweighted $L_2$-penalized Cox regression. We show that the resulting estimator enjoys the best of $L_0$- and $L_2$-penalized Cox regressions while overcoming their limitations. Specifically, the estimator is selection consistent, oracle for parameter estimation, and possesses a grouping property for highly correlated covariates. Simulation results suggest that when the sample size is large, the proposed method with pre-specified tuning parameters has a comparable or better performance than some popular penalized regression methods. More importantly, because the method naturally enables adaptation of efficient algorithms for massive $L_2$-penalized optimization and does not require costly data driven tuning parameter selection, it has a significant computational advantage for sHDMSS data, offering an average of 5-fold speedup over its closest competitor in empirical studies.

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