arXiv subjects

Suchit Mehrotra

Publications and source records attributed to Suchit Mehrotra.

5 recordsLinked to original sources

arXiv2022

Dimension Reduction via Supervised Clustering of Regression Coefficients: A Review

The development and use of dimension reduction methods is prevalent in modern statistical literature. This paper reviews a class of dimension reduction techniques which aim to simultaneously select relevant predictors and find clusters within them which share a common effect on the response. Such methods have been shown to have superior performance relative to OLS estimates and the lasso [Tibshirani, 1996] especially when multicollinearity in the predictors is present. Their applications, which include genetics, epidemiology, and fMRI studies, are also discussed.

stat.ME↗

arXiv2021

Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claims

We ascertain and compare the performances of AutoML tools on large, highly imbalanced healthcare datasets. We generated a large dataset using historical administrative claims including demographic information and flags for disease codes in four different time windows prior to 2019. We then trained three AutoML tools on this dataset to predict six different disease outcomes in 2019 and evaluated model performances on several metrics. The AutoML tools showed improvement from the baseline random forest model but did not differ significantly from each other. All models recorded low area under the precision-recall curve and failed to predict true positives while keeping the true negative rate high. Model performance was not directly related to prevalence. We provide a specific use-case to illustrate how to select a threshold that gives the best balance between true and false positive rates, as this is an important consideration in medical applications. Healthcare datasets present several challenges for AutoML tools, including large sample size, high imbalance, and limitations in the available features types. Improvements in scalability, combinations of imbalance-learning resampling and ensemble approaches, and curated feature selection are possible next steps to achieve better performance. Among the three explored, no AutoML tool consistently outperforms the rest in terms of predictive performance. The performances of the models in this study suggest that there may be room for improvement in handling medical claims data. Finally, selection of the optimal prediction threshold should be guided by the specific practical application.

cs.LG↗

arXiv2021

Variational Inference for Shrinkage Priors: The R package vir

We present vir, an R package for variational inference with shrinkage priors. Our package implements variational and stochastic variational algorithms for linear and probit regression models, the use of which is a common first step in many applied analyses. We review variational inference and show how the derivation for a Gibbs sampler can be easily modified to derive a corresponding variational or stochastic variational algorithm. We provide simulations showing that, at least for a normal linear model, variational inference can lead to similar uncertainty quantification as the corresponding Gibbs samplers, while estimating the model parameters at a fraction of the computational cost. Our timing experiments show situations in which our algorithms converge faster than the frequentist LASSO implementations in glmnet while simultaneously providing superior parameter estimation and variable selection. Hence, our package can be utilized to quickly explore different combinations of predictors in a linear model, while providing accurate uncertainty quantification in many applied situations. The package is implemented natively in R and RcppEigen, which has the benefit of bypassing the substantial operating system specific overhead of linking external libraries to work efficiently with R.

stat.CO↗

arXiv2021

Divide-and-Conquer MCMC for Multivariate Binary Data

The analysis of large scale medical claims data has the potential to improve quality of care by generating insights which can be used to create tailored medical programs. In particular, the multivariate probit model can be used to investigate the correlation between multiple binary responses of interest in such data, e.g. the presence of multiple chronic conditions. Bayesian modeling is well suited to such analyses because of the automatic uncertainty quantification provided by the posterior distribution. A complicating factor is that large medical claims datasets often do not fit in memory, which renders the estimation of the posterior using traditional Markov Chain Monte Carlo (MCMC) methods computationally infeasible. To address this challenge, we extend existing divide-and-conquer MCMC algorithms to the multivariate probit model, demonstrating, via simulation, that they should be preferred over mean-field variational inference when the estimation of the latent correlation structure between binary responses is of primary interest. We apply this algorithm to a large database of de-identified Medicare Advantage claims from a single large US health insurance provider, where we find medically meaningful groupings of common chronic conditions and asses the impact of the urban-rural health gap by identifying underutilized provider specialties in rural areas.

stat.ME↗

arXiv2019

Simultaneous Variable Selection, Clustering, and Smoothing in Function on Scalar Regression

We address the problem of multicollinearity in a function-on-scalar regression model by using a prior which simultaneously selects, clusters, and smooths functional effects. Our methodology groups effects of highly correlated predictors, performing dimension reduction without dropping relevant predictors from the model. We validate our approach via a simulation study, showing superior performance relative to existing dimension reduction approaches in the function-on-scalar literature. We also demonstrate the use of our model on a data set of age specific fertility rates from the United Nations Gender Information database.

stat.AP↗