Population ‐calibrated multiple imputation for a binary/categorical covariate in categorical regression models
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 17, 2018 Category: Statistics Authors: Tra My Pham,
James R Carpenter,
Tim P Morris,
Angela M Wood,
Irene Petersen Source Type: research
A weighted kernel machine regression approach to environmental pollutants and infertility
In epidemiological studies of environmental pollutants in relation to human infertility, it is common that concentrations of a large number of exposures are collected in both male and female partners. Such a couple ‐based study poses some new challenges in statistical analysis, especially when the effect of the totality of these chemical mixtures is of interest, because these exposures may have complex nonlinear and nonadditive relationships with the infertility outcome. Kernel machine regression, as a nonpa rametric regression method, can be applied to model such effects, while accounting for the highly correlated struc...
Source: Statistics in Medicine - October 16, 2018 Category: Statistics Authors: Wei Zhang,
Zhen Chen,
Aiyi Liu,
Germaine M. Buck Louis Tags: RESEARCH ARTICLE Source Type: research
Population ‐calibrated multiple imputation for a binary/categorical covariate in categorical regression models
We describe the derivation of this offset from the population distribution of the incomplete variable and show how, in applications, it can be used to closely (and often exactly) match the post‐imputation distribution to the population level. Through analytic and simulation studies, we show th at our proposed calibrated‐δ adjustment MI method can give the same inference as standard MI when data are MAR, and can produce more accurate inference under two general missing not at random missingness mechanisms. The method is used to impute missing ethnicity data in a type 2 diabetes prevalence case study using UK primary ca...
Source: Statistics in Medicine - October 16, 2018 Category: Statistics Authors: Tra My Pham,
James R Carpenter,
Tim P Morris,
Angela M Wood,
Irene Petersen Tags: RESEARCH ARTICLE Source Type: research
A shared ‐parameter continuous‐time hidden Markov and survival model for longitudinal data with informative dropout
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 16, 2018 Category: Statistics Authors: Francesco Bartolucci,
Alessio Farcomeni Source Type: research
Issue Information
Statistics in Medicine,Volume 37, Issue 26, 20 November 2018. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 16, 2018 Category: Statistics Source Type: research
A shared ‐parameter continuous‐time hidden Markov and survival model for longitudinal data with informative dropout
A shared ‐parameter approach for jointly modeling longitudinal and survival data is proposed. With respect to available approaches, it allows for time‐varying random effects that affect both the longitudinal and the survival processes. The distribution of these random effects is modeled according to a co ntinuous‐time hidden Markov chain so that transitions may occur at any time point. For maximum likelihood estimation, we propose an algorithm based on a discretization of time until censoring in an arbitrary number of time windows. The observed information matrix is used to obtain standard errors. We illustrate the a...
Source: Statistics in Medicine - October 15, 2018 Category: Statistics Authors: Francesco Bartolucci,
Alessio Farcomeni Tags: RESEARCH ARTICLE Source Type: research
Latent trait shared ‐parameter mixed models for missing ecological momentary assessment data
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 15, 2018 Category: Statistics Authors: John F. Cursio,
Robin J. Mermelstein,
Donald Hedeker Source Type: research
Latent trait shared ‐parameter mixed models for missing ecological momentary assessment data
Latent trait shared ‐parameter mixed models for ecological momentary assessment (EMA) data containing missing values are developed in which data are collected in an intermittent manner. In such studies, data are often missing due to unanswered prompts. Using item response theory models, a latent trait is used to repr esent the missing prompts and modeled jointly with a mixed model for bivariate longitudinal outcomes. Both one‐ and two‐parameter latent trait shared‐parameter mixed models are presented. These new models offer a unique way to analyze missing EMA data with many response patterns. Here, the pro posed mo...
Source: Statistics in Medicine - October 14, 2018 Category: Statistics Authors: John F. Cursio,
Robin J. Mermelstein,
Donald Hedeker Tags: RESEARCH ARTICLE Source Type: research
Quantifying the association between progression ‐free survival and overall survival in oncology trials using Kendall's τ
This paper considers methods for estimating the association between progression ‐free and overall survival in oncology trials. Copula‐based, nonparametric, and illness‐death model–based methods are reviewed. In addition, the approach based on an underlying illness‐death model is generalized to allow general parametric models. The performance of these methods, in terms of bias and efficiency, is investigated through simulation and also illustrated using data from a clinical trial of treatments for colon cancer. The simulations suggest that the illness‐death model–based method provides good estimates of Kendall...
Source: Statistics in Medicine - October 12, 2018 Category: Statistics Authors: Enya M. Weber,
Andrew C. Titman Tags: RESEARCH ARTICLE Source Type: research
Quantifying the association between progression ‐free survival and overall survival in oncology trials using Kendall's τ
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 12, 2018 Category: Statistics Authors: Enya M. Weber,
Andrew C. Titman Source Type: research
Issue Information
Statistics in Medicine,Volume 37, Issue 25, 10 November 2018. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 12, 2018 Category: Statistics Source Type: research
Methods to improve the estimation of time ‐to‐event outcomes when data is de‐identified
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 12, 2018 Category: Statistics Authors: Samantha ‐Jo Caetano,
David Dawe,
Peter Ellis,
Craig C. Earle,
Gregory R. Pond Source Type: research
Constrained empirical ‐likelihood confidence regions in nonignorable covariate‐missing data problems
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 12, 2018 Category: Statistics Authors: Yanmei Xie,
Biao Zhang Source Type: research
Methods to improve the estimation of time ‐to‐event outcomes when data is de‐identified
This study investigates five methods intended to reduce the bias of time‐to‐event estimates. A simulation s tudy was conducted to evaluate the effectiveness of each method in reducing bias. In situations where there was a large number of censored patients, the results of the simulation showed that Method 4 yielded the most accurate estimates. This method adjusted the survival times of censored patients by adding a random uniform component such that the modified survival time would occur within the final year of the study. Alternatively, when there was only a small number of censored patients, the method that did not al...
Source: Statistics in Medicine - October 11, 2018 Category: Statistics Authors: Samantha ‐Jo Caetano,
David Dawe,
Peter Ellis,
Craig C. Earle,
Gregory R. Pond Tags: RESEARCH ARTICLE Source Type: research
Constrained empirical ‐likelihood confidence regions in nonignorable covariate‐missing data problems
Missing covariates in regression analysis are a pervasive problem in medical, social, and economic researches. We study empirical ‐likelihood confidence regions for unconstrained and constrained regression parameters in a nonignorable covariate‐missing data problem. For an assumed conditional mean regression model, we assume that some covariates are fully observed but other covariates are missing for some subjects. By expl oitation of a probability model of missingness and a working conditional score model from a semiparametric perspective, we build a system of unbiased estimating equations, where the number of equatio...
Source: Statistics in Medicine - October 11, 2018 Category: Statistics Authors: Yanmei Xie,
Biao Zhang Tags: RESEARCH ARTICLE Source Type: research
