New semiparametric method for predicting high ‐cost patients
Summary
Motivated by the Medical Expenditure Panel Survey containing data from individuals’ medical providers and employers across the United States, we propose a new semiparametric procedure for predicting whether a patient will incur high medical expenditure. Problems of the same nature arise in many other important applications where one would like to predict if a future response occurs at the upper (or lower) tail of the response distribution. The common practice is to artificially dichotomize the response variable and then apply an existing classification method such as binomial regression or a classification tree. ...
Source: Biometrics - December 11, 2017 Category: Biotechnology Authors: Adam Maidman, Lan Wang Tags: ORIGINAL ARTICLE Source Type: research
C ‐learning: A new classification framework to estimate optimal dynamic treatment regimes
Summary
A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective...
Source: Biometrics - December 11, 2017 Category: Biotechnology Authors: Baqun Zhang, Min Zhang Tags: ORIGINAL ARTICLE Source Type: research
Motivating sample sizes in adaptive Phase I trials via Bayesian posterior credible intervals
Summary
In contrast with typical Phase III clinical trials, there is little existing methodology for determining the appropriate numbers of patients to enroll in adaptive Phase I trials. And, as stated by Dennis Lindley in a more general context, “[t]he simple practical question of ‘What size of sample should I take’ is often posed to a statistician, and it is a question that is embarrassingly difficult to answer.” Historically, simulation has been the primary option for determining sample sizes for adaptive Phase I trials, and although useful, can be problematic and time‐consuming when a sample size is needed re...
Source: Biometrics - December 1, 2017 Category: Biotechnology Authors: Thomas M. Braun Tags: ORIGINAL ARTICLE Source Type: research
An approximate joint model for multiple paired longitudinal outcomes and time ‐to‐event data
Summary
Joint modeling of multivariate paired longitudinal data and time‐to‐event data presents computational challenges that supersede full likelihood estimation due to the large dimensional random effects vector needed to capture correlation due to clustering with respect to pairs, subjects, and outcomes. We propose an alternative, computationally simpler approach to estimation of complex shared parameter models where missing data is imputed based on the Posterior Predictive Distribution from a Conditional Linear Model (CLM) approximation. Existing methods for complete data are then implemented to obtain estimates of...
Source: Biometrics - December 1, 2017 Category: Biotechnology Authors: Angelo F. Elmi, Katherine L. Grantz, Paul S. Albert Tags: ORIGINAL ARTICLE Source Type: research
A Bayesian nonparametric approach to causal inference on quantiles
Summary
We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approa...
Source: Biometrics - December 1, 2017 Category: Biotechnology Authors: Dandan Xu, Michael J. Daniels, Almut G. Winterstein Tags: ORIGINAL ARTICLE Source Type: research
Bayesian enhancement two ‐stage design for single‐arm phase II clinical trials with binary and time‐to‐event endpoints
Summary
Simon's two‐stage design is one of the most commonly used methods in phase II clinical trials with binary endpoints. The design tests the null hypothesis that the response rate is less than an uninteresting level, versus the alternative hypothesis that the response rate is greater than a desirable target level. From a Bayesian perspective, we compute the posterior probabilities of the null and alternative hypotheses given that a promising result is declared in Simon's design. Our study reveals that because the frequentist hypothesis testing framework places its focus on the null hypothesis, a potentially efficaci...
Source: Biometrics - December 1, 2017 Category: Biotechnology Authors: Haolun Shi, Guosheng Yin Tags: ORIGINAL ARTICLE Source Type: research
A wild bootstrap approach for the Aalen –Johansen estimator
Summary
We suggest a wild bootstrap resampling technique for nonparametric inference on transition probabilities in a general time‐inhomogeneous Markov multistate model. We first approximate the limiting distribution of the Nelson–Aalen estimator by repeatedly generating standard normal wild bootstrap variates, while the data is kept fixed. Next, a transformation using a functional delta method argument is applied. The approach is conceptually easier than direct resampling for the transition probabilities. It is used to investigate a non‐standard time‐to‐event outcome, currently being alive without immunosuppress...
Source: Biometrics - December 1, 2017 Category: Biotechnology Authors: Tobias Bluhmki, Claudia Schmoor, Dennis Dobler, Markus Pauly, Juergen Finke, Martin Schumacher, Jan Beyersmann Tags: ORIGINAL ARTICLE Source Type: research
An alternative robust estimator of average treatment effect in causal inference
Summary
The problem of estimating the average treatment effects is important when evaluating the effectiveness of medical treatments or social intervention policies. Most of the existing methods for estimating the average treatment effect rely on some parametric assumptions about the propensity score model or the outcome regression model one way or the other. In reality, both models are prone to misspecification, which can have undue influence on the estimated average treatment effect. We propose an alternative robust approach to estimating the average treatment effect based on observational data in the challenging situati...
Source: Biometrics - December 1, 2017 Category: Biotechnology Authors: Jianxuan Liu, Yanyuan Ma, Lan Wang Tags: ORIGINAL ARTICLE Source Type: research
Generalized accelerated recurrence time model for multivariate recurrent event data with missing event type
Summary
Recurrent events data are frequently encountered in biomedical follow‐up studies. The generalized accelerated recurrence time (GART) model (Sun et al., 2016), which formulates covariate effects on the time scale of the mean function of recurrent events (i.e., time to expected frequency), has arisen as a useful secondary analysis tool to provide meaningful physical interpretations. In this article, we investigate the GART model in a multivariate recurrent events setting, where subjects may experience multiple types of recurrent events and some event types may be missing. We propose methods for the GART model that ...
Source: Biometrics - December 1, 2017 Category: Biotechnology Authors: Huijuan Ma, Limin Peng, Zhumin Zhang, HuiChuan J. Lai Tags: ORIGINAL ARTICLE Source Type: research
A scalable multi ‐resolution spatio‐temporal model for brain activation and connectivity in fMRI data
Summary
Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs)—coarser o...
Source: Biometrics - December 1, 2017 Category: Biotechnology Authors: Stefano Castruccio, Hernando Ombao, Marc G. Genton Tags: ORIGINAL ARTICLE Source Type: research
Regression analysis for secondary response variable in a case ‐cohort study
Summary
Case‐cohort study design has been widely used for its cost‐effectiveness. In any real study, there are always other important outcomes of interest beside the failure time that the original case‐cohort study is based on. How to utilize the available case‐cohort data to study the relationship of a secondary outcome with the primary exposure obtained through the case‐cohort study is not well studied. In this article, we propose a non‐parametric estimated likelihood approach for analyzing a secondary outcome in a case‐cohort study. The estimation is based on maximizing a semiparametric likelihood function...
Source: Biometrics - December 1, 2017 Category: Biotechnology Authors: Yinghao Pan, Jianwen Cai, Sangmi Kim, Haibo Zhou Tags: ORIGINAL ARTICLE Source Type: research
Reader reaction on the fast small ‐sample kernel independence test for microbiome community‐level association analysis
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - November 29, 2017 Category: Biotechnology Source Type: research
A regression framework for assessing covariate effects on the reproducibility of high ‐throughput experiments
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - November 29, 2017 Category: Biotechnology Source Type: research
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Biometrics, Ahead of Print. (Source: Biometrics)
Source: Biometrics - November 29, 2017 Category: Biotechnology Source Type: research
