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Sherri Rose, Sharon-Lise Normand
Postmarket comparative effectiveness and safety analyses of therapeutic treatments typically involve large observational cohorts. We propose double robust machine learning estimation techniques for implantable medical device evaluations where there are more than two unordered treatments and patients are clustered in hospitals. This flexible approach also accommodates high-dimensional covariates drawn from clinical databases. The Massachusetts Data Analysis Center percutaneous coronary intervention cohort is used to assess the composite outcome of 10 drug-eluting stents among adults implanted with at least one drug-eluting stent in Massachusetts...
July 13, 2018: Biometrics
Chih-Yuan Hsu, Chen-Hsin Chen, Ken-Ning Hsu, Ya-Hung Lu
Lan and DeMets () proposed the alpha spending function for group sequential trials to permit the use of unspecified frequencies and timings of interim analyses in the trial design. Regarding a trial with censored time to endpoint, Lan and DeMets () later defined information time at an interim analysis in a maximum duration trial. To compare two survival curves utilizing such a design, information times for group sequential logrank and Wilcoxon-type statistics have been developed by assuming that the survival time follows an exponential distribution or a Weibull distribution without considering the censoring distribution...
July 13, 2018: Biometrics
Lucia Babino, Andrea Rotnitzky, James Robins
We consider estimation, from longitudinal observational data, of the parameters of marginal structural mean models for unconstrained outcomes. Current proposals include inverse probability of treatment weighted and double robust (DR) estimators. A difficulty with DR estimation is that it requires postulating a sequence of models, one for the each mean of the counterfactual outcome given covariate and treatment history up to each exposure time point. Most natural models for such means are often incompatible...
July 13, 2018: Biometrics
Giovanni Nattino, Bo Lu
In medical and health sciences, observational studies are a major data source for inferring causal relationships. Unlike randomized experiments, observational studies are vulnerable to the hidden bias introduced by unmeasured confounders. The impact of unmeasured covariates on the causal effect can be assessed by conducting a sensitivity analysis. A comprehensive framework of sensitivity analyses has been developed for matching designs. Sensitivity parameters are introduced to capture the association between the missing covariates and the exposure or the outcome...
July 10, 2018: Biometrics
Soyoung Kim, Donglin Zeng, Jianwen Cai
Generalized case-cohort design has been proposed to assess the effects of exposures on survival outcomes when measuring exposures is expensive and events are not rare in the cohort. In such design, expensive exposure information is collected from both a (stratified) randomly selected subcohort and a subset of individuals with events. In this article, we consider extension of such design to study multiple types of survival events by selecting a proportion of cases for each type of event. We propose a general weighting scheme to analyze data...
July 10, 2018: Biometrics
Ting Ye, Menggang Yu
Immunotherapies are taking the center stage for cancer drug development and research. Many of these therapies, for example, immune checkpoint inhibitors, are known to have possible lag periods to achieve their full effects. Therefore, the proportional hazard assumption is violated when comparing survival curves in randomized clinical trials evaluating such therapies. Limited work exists in determining sample size to account for the lag period which is usually unknown. Assuming that the lag period is within some reasonable range, this article presents an approach to calculate sample size based on a maximin efficiency robust test...
July 10, 2018: Biometrics
Jeffrey A Boatman, David M Vock
Patients awaiting cadaveric organ transplantation face a difficult decision if offered a low-quality organ: accept the organ or remain on the waiting list and hope a better organ is offered in the future. A dynamic treatment regime (DTR) for transplantation is a rule that determines whether a patient should decline an offered organ. Existing methods can estimate the effect of DTRs on survival outcomes, but these were developed for applications where treatment is abundantly available. For transplantation, organ availability is limited, and existing methods can only estimate the effect of a DTR assuming a single patient follows the DTR...
July 10, 2018: Biometrics
Minggen Lu, Christopher S McMahan
For analyzing current status data, a flexible partially linear proportional hazards model is proposed. Modeling flexibility is attained through using monotone splines to approximate the baseline cumulative hazard function, as well as B-splines to accommodate nonlinear covariate effects. To facilitate model fitting, a computationally efficient and easy to implement expectation-maximization algorithm is developed through a two-stage data augmentation process involving carefully structured latent Poisson random variables...
July 5, 2018: Biometrics
Jing Qian, Sy Han Chiou, Jacqueline E Maye, Folefac Atem, Keith A Johnson, Rebecca A Betensky
In several common study designs, regression modeling is complicated by the presence of censored covariates. Examples of such covariates include maternal age of onset of dementia that may be right censored in an Alzheimer's amyloid imaging study of healthy subjects, metabolite measurements that are subject to limit of detection censoring in a case-control study of cardiovascular disease, and progressive biomarkers whose baseline values are of interest, but are measured post-baseline in longitudinal neuropsychological studies of Alzheimer's disease...
June 22, 2018: Biometrics
Evan L Ray, Jeffer E Sasaki, Patty S Freedson, John Staudenmayer
A person's physical activity has important health implications, so it is important to be able to measure aspects of physical activity objectively. One approach to doing that is to use data from an accelerometer to classify physical activity according to activity type (e.g., lying down, sitting, standing, or walking) or intensity (e.g., sedentary, light, moderate, or vigorous). This can be formulated as a labeled classification problem, where the model relates a feature vector summarizing the accelerometer signal in a window of time to the activity type or intensity in that window...
June 19, 2018: Biometrics
Fan Li, Elizabeth L Turner, John S Preisser
In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Setting the latter two correlation parameters to be equal accommodates cross-sectional designs...
June 19, 2018: Biometrics
Yichen Cheng, James Y Dai, Xiaoyu Wang, Charles Kooperberg
Copy number variation (CNV) of DNA plays an important role in the development of many diseases. However, due to the irregularity and sparsity of the CNVs, studying the association between CNVs and a disease outcome or a trait can be challenging. Up to now, not many methods have been proposed in the literature for this problem. Most of the current researchers reply on an ad hoc two-stage procedure by first identifying CNVs in each individual genome and then performing an association test using these identified CNVs...
June 12, 2018: Biometrics
Antonio Canale, Daniele Durante, David B Dunson
There is wide interest in studying how the distribution of a continuous response changes with a predictor. We are motivated by environmental applications in which the predictor is the dose of an exposure and the response is a health outcome. A main focus in these studies is inference on dose levels associated with a given increase in risk relative to a baseline. In addressing this goal, popular methods either dichotomize the continuous response or focus on modeling changes with the dose in the expectation of the outcome...
June 12, 2018: Biometrics
Ben Brintz, Claudio Fuentes, Lisa Madsen
N-mixture models are probability models that estimate abundance using replicate observed counts while accounting for imperfect detection. In this article, we propose an asymptotic approximation to the N-mixture model which efficiently estimates large abundances without the computational limitations of the generalized N-mixture model introduced by Dail and Madsen in . It has been suggested in the literature that N-mixture models do not perform well when counts from the same sites show weak patterns of population dynamics...
June 5, 2018: Biometrics
Ruitao Lin
Most phase I dose-finding trials are conducted based on a single binary toxicity outcome to investigate the safety of new drugs. In many situations, however, it is important to distinguish between various toxicity types and different toxicity grades. By minimizing the maximum joint probability of incorrect decisions, we extend the Bayesian optimal interval (BOIN) design to control multiple toxicity outcomes at prespecified levels. The developed multiple-toxicity BOIN design can handle equally important, unequally important as well as nested toxicity outcomes...
June 5, 2018: Biometrics
Fei Gao, Donglin Zeng, Dan-Yu Lin
Interval-censored data arise when the event time of interest can only be ascertained through periodic examinations. In medical studies, subjects may not complete the examination schedule for reasons related to the event of interest. In this article, we develop a semiparametric approach to adjust for such informative dropout in regression analysis of interval-censored data. Specifically, we propose a broad class of joint models, under which the event time of interest follows a transformation model with a random effect and the dropout time follows a different transformation model but with the same random effect...
June 5, 2018: Biometrics
Jason P Estes, Danh V Nguyen, Yanjun Chen, Lorien S Dalrymple, Connie M Rhee, Kamyar Kalantar-Zadeh, Damla Şentürk
No abstract text is available yet for this article.
June 5, 2018: Biometrics
Jason P Estes, Danh V Nguyen, Yanjun Chen, Lorien S Dalrymple, Connie M Rhee, Kamyar Kalantar-Zadeh, Damla Şentürk
Standard profiling analysis aims to evaluate medical providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. The outcome, for instance, may be mortality, medical complications, or 30-day (unplanned) hospital readmission. Profiling analysis involves regression modeling of a patient outcome, adjusting for patient health status at baseline, and comparing each provider's outcome rate (e.g., 30-day readmission rate) to a normative standard (e.g., national "average")...
June 5, 2018: Biometrics
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