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Statistics in Medicine

Shu Jiang, Richard J Cook
A mixture model is described, which accommodates different Markov processes governing disease progression in a finite set of latent classes. We give special attention to the setting in which individuals are examined intermittently and transition times are consequently interval censored. A score test is developed to identify genetic markers associated with class membership. Simulation studies are conducted to validate the algorithm, assess the finite sample properties of the estimators, and assess the frequency properties of the score tests...
April 10, 2019: Statistics in Medicine
Yong Zang, Beibei Guo, Yan Han, Sha Cao, Chi Zhang
It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker-stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta-binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis...
April 9, 2019: Statistics in Medicine
Li C Cheung, Qing Pan, Noorie Hyun, Hormuzd A Katki
We propose an extension of Harrell's concordance (C) index to evaluate the prognostic utility of biomarkers for diseases with multiple measurable outcomes that can be prioritized. Our prioritized concordance index measures the probability that, given a random subject pair, the subject with the worst disease status as of a time τ has the higher predicted risk. Our prioritized concordance index uses the same approach as the win ratio, by basing generalized pairwise comparisons on the most severe or clinically important comparable outcome...
April 7, 2019: Statistics in Medicine
Iván Díaz
The consistency of doubly robust estimators relies on the consistent estimation of at least one of two nuisance regression parameters. In moderate-to-large dimensions, the use of flexible data-adaptive regression estimators may aid in achieving this consistency. However, n1/2 -consistency of doubly robust estimators is not guaranteed if one of the nuisance estimators is inconsistent. In this paper, we present a doubly robust estimator for survival analysis with the novel property that it converges to a Gaussian variable at an n1/2 -rate for a large class of data-adaptive estimators of the nuisance parameters, under the only assumption that at least one of them is consistently estimated at an n1/4 -rate...
April 4, 2019: Statistics in Medicine
Xiaofang Yan, Younathan Abdia, Somnath Datta, K B Kulasekera, Beatrice Ugiliweneza, Maxwell Boakye, Maiying Kong
In observational studies, generalized propensity score (GPS)-based statistical methods, such as inverse probability weighting (IPW) and doubly robust (DR) method, have been proposed to estimate the average treatment effect (ATE) among multiple treatment groups. In this article, we investigate the GPS-based statistical methods to estimate treatment effects from two aspects. The first aspect of our investigation is to obtain an optimal GPS estimation method among four competing GPS estimation methods by using a rank aggregation approach...
April 2, 2019: Statistics in Medicine
Colman H Humphrey, Dylan S Small, Shane T Jensen, Kevin G Volpp, David A Asch, Jingsan Zhu, Andrea B Troxel
Many health issues require adherence to recommended daily activities, such as taking medication to manage a chronic condition, walking a certain distance to promote weight loss, or measuring weights to assess fluid balance in heart failure. The cost of nonadherence can be high, with respect to both individual health outcomes and the healthcare system. Incentivizing adherence to daily activities can promote better health in patients and populations and potentially provide long-term cost savings. Multiple incentive structures are possible...
April 2, 2019: Statistics in Medicine
Sarah C Anoke, Sharon-Lise Normand, Corwin M Zigler
The literature on causal effect estimation tends to focus on the population mean estimand, which is less informative as medical treatments are becoming more personalized and there is increasing awareness that subpopulations of individuals may experience a group-specific effect that differs from the population average. In fact, it is possible that there is underlying systematic effect heterogeneity that is obscured by focusing on the population mean estimand. In this context, understanding which covariates contribute to this treatment effect heterogeneity (TEH) and how these covariates determine the differential treatment effect (TE) is an important consideration...
March 31, 2019: Statistics in Medicine
Takanori Kawabata, Ryo Emoto, Jo Nishino, Kunihiko Takahashi, Shigeyuki Matsui
One of main roles of omics-based association studies with high-throughput technologies is to screen out relevant molecular features, such as genetic variants, genes, and proteins, from a large pool of such candidate features based on their associations with the phenotype of interest. Typically, screened features are subject to validation studies using more established or conventional assays, where the number of evaluable features is relatively limited, so that there may exist a fixed number of features measurable by these assays...
March 31, 2019: Statistics in Medicine
Tao Feng, Pallavi Basu, Wenguang Sun, Hsun Teresa Ku, Wendy J Mack
High-throughput screening (HTS) is a large-scale hierarchical process in which a large number of chemicals are tested in multiple stages. Conventional statistical analyses of HTS studies often suffer from high testing error rates and soaring costs in large-scale settings. This article develops new methodologies for false discovery rate control and optimal design in HTS studies. We propose a two-stage procedure that determines the optimal numbers of replicates at different screening stages while simultaneously controlling the false discovery rate in the confirmatory stage subject to a constraint on the total budget...
March 28, 2019: Statistics in Medicine
Kim May Lee, Stefanie Biedermann, Robin Mitra
Multiarm trials with follow-up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. We develop an optimal design framework for trials with repeated measurements, which takes potential dropouts into account, and we provide designs for linear mixed models where the presence of dropouts is noninformative and dependent on design variables...
March 25, 2019: Statistics in Medicine
Ching-Yun Wang, James Dai
The inverse probability weighted estimator is often applied to two-phase designs and regression with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood-based estimators but, in general, are more robust against model misspecification. In this paper, we propose a best linear inverse probability weighted estimator for two-phase designs and missing covariate regression. Our proposed estimator is the projection of the SIPW onto the orthogonal complement of the score space based on a working regression model of the observed covariate data...
March 25, 2019: Statistics in Medicine
Jonathan J Shuster, Mark Handler
The "MeToo#" movement has been instrumental in delineating the prevalence of alleged sexual harassment complaints in the workplace. In this article, we propose controlled scientific methods for statisticians and credibility assessment experts to jointly collaborate with human resource staff and/or attorneys to help evaluate claims by a class of accusers against an alleged serial harasser. When an accused falsely denies claims as lies, s/he may be guilty of libel/defamation. Hence, even if statutes of limitations for criminal prosecution may have expired, a timely civil suit could be mounted...
March 21, 2019: Statistics in Medicine
Joy Leahy, Howard Thom, Jeroen P Jansen, Emma Gray, Aisling O'Leary, Arthur White, Cathal Walsh
Increasingly, single-armed evidence is included in health technology assessment submissions when companies are seeking reimbursement for new drugs. While it is recognized that randomized controlled trials provide a higher standard of evidence, these are not available for many new agents that have been granted licenses in recent years. Therefore, it is important to examine whether alternative strategies for assessing this evidence may be used. In this work, we examine approaches to incorporating single-armed evidence formally in the evaluation process...
March 20, 2019: Statistics in Medicine
Wenhui Liu, Gary K Grunwald, P Michael Ho
Health care cost data often contain many zero values, for patients who did not use any care. Two-part models with logistic models for part I, probability of use (ie, nonzero cost) and log-link models for part II, mean cost of use (ie, nonzero cost) are often used. Effects of exposures or covariates on total (marginal) cost are often of interest, and recent work has proposed useful methods. Factors that affect total cost do so through a combination of effects on probability of use and cost of use. Such a decomposition is needed to understand and act on factors that affect total cost, but little work has been done on this question...
March 20, 2019: Statistics in Medicine
Xin Qiu, Yuanjia Wang
Treatment response heterogeneity has long been observed in patients affected by chronic diseases. Administering an individualized treatment rule (ITR) offers an opportunity to tailor treatment strategies according to patient-specific characteristics. Overly complex machine learning methods for estimating ITRs may produce treatment rules that have higher benefit but lack transparency and interpretability. In clinical practices, it is desirable to derive a simple and interpretable ITR while maintaining certain optimality that leads to improved benefit in subgroups of patients, if not on the overall sample...
March 19, 2019: Statistics in Medicine
S D Walter, G H Guyatt, D Bassler, M Briel, T Ramsay, H D Han
Stopping rules for clinical trials are primarily intended to control Type I error rates if interim analyses are planned, but less is known about the impact that potential stopping has on estimating treatment benefit. In this paper, we derive analytic expressions for (1) the over-estimation of benefit in studies that stop early, (2) the under-estimation of benefit in completed studies, and (3) the overall bias in studies with a stopping rule. We also examine the probability of stopping early and the situation in meta-analyses...
March 19, 2019: Statistics in Medicine
Jingli Wang, Jialiang Li, Yaguang Li, Weng Kee Wong
Thresholding variable plays a crucial role in subgroup identification for personalized medicine. Most existing partitioning methods split the sample based on one predictor variable. In this paper, we consider setting the splitting rule from a combination of multivariate predictors, such as the latent factors, principle components, and weighted sum of predictors. Such a subgrouping method may lead to more meaningful partitioning of the population than using a single variable. In addition, our method is based on a change point regression model and thus yields straight forward model-based prediction results...
March 18, 2019: Statistics in Medicine
Chenguang Wang, Gary L Rosner
With the wide availability of various real-world data (RWD), there is an increasing interest in synthesizing information from both randomized clinical trials and RWD for health-care decision makings. The task of addressing study-specific heterogeneities is one of the most difficult challenges in synthesizing data from disparate sources. Bayesian hierarchical models with nonparametric extension provide a powerful and convenient platform that formalizes the information borrowing strength across the sources. In this paper, we propose a propensity score-based Bayesian nonparametric Dirichlet process mixture model that summarizes subject-level information from randomized and registry studies to draw inference on the causal treatment effect...
March 18, 2019: Statistics in Medicine
Ellen C Caniglia, James M Robins, Lauren E Cain, Caroline Sabin, Roger Logan, Sophie Abgrall, Michael J Mugavero, Sonia Hernández-Díaz, Laurence Meyer, Remonie Seng, Daniel R Drozd, George R Seage Iii, Fabrice Bonnet, Fabien Le Marec, Richard D Moore, Peter Reiss, Ard van Sighem, William C Mathews, Inma Jarrín, Belén Alejos, Steven G Deeks, Roberto Muga, Stephen L Boswell, Elena Ferrer, Joseph J Eron, John Gill, Antonio Pacheco, Beatriz Grinsztejn, Sonia Napravnik, Sophie Jose, Andrew Phillips, Amy Justice, Janet Tate, Heiner C Bucher, Matthias Egger, Hansjakob Furrer, Jose M Miro, Jordi Casabona, Kholoud Porter, Giota Touloumi, Heidi Crane, Dominique Costagliola, Michael Saag, Miguel A Hernán
Decisions about when to start or switch a therapy often depend on the frequency with which individuals are monitored or tested. For example, the optimal time to switch antiretroviral therapy depends on the frequency with which HIV-positive individuals have HIV RNA measured. This paper describes an approach to use observational data for the comparison of joint monitoring and treatment strategies and applies the method to a clinically relevant question in HIV research: when can monitoring frequency be decreased and when should individuals switch from a first-line treatment regimen to a new regimen? We outline the target trial that would compare the dynamic strategies of interest and then describe how to emulate it using data from HIV-positive individuals included in the HIV-CAUSAL Collaboration and the Centers for AIDS Research Network of Integrated Clinical Systems...
March 18, 2019: Statistics in Medicine
Nadim Ballout, Vivian Viallon
Graphical models are used in many applications such as medical diagnostics and computer security. Increasingly often, the estimation of such models has to be performed on several predefined strata of the whole population. For instance, in epidemiology and clinical research, strata are often defined according to age, gender, treatment, or disease type. In this article, we propose new approaches dedicated to the estimation of binary graphical models on such strata. These approaches are implemented by combining well-known methods that have been developed in the context of a single binary graphical model, with penalties encouraging structured sparsity, which have recently been shown to be appropriate when dealing with stratified data...
March 14, 2019: Statistics in Medicine
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