Read by QxMD icon Read

Statistics in Medicine

Rui Huang, Liming Xiang, Il Do Ha
Frailty models are widely used to model clustered survival data arising in multicenter clinical studies. In the literature, most existing frailty models are proportional hazards, additive hazards, or accelerated failure time model based. In this paper, we propose a frailty model framework based on mean residual life regression to accommodate intracluster correlation and in the meantime provide easily understand and straightforward interpretation for the effects of prognostic factors on the expectation of the remaining lifetime...
August 16, 2019: Statistics in Medicine
Kirk M Wolter, N Ganesh, Kennon R Copeland, James A Singleton, Meena Khare
We discuss alternative estimators of the population total given a dual-frame random-digit-dial (RDD) telephone survey in which samples are selected from landline and cell phone sampling frames. The estimators are subject to sampling and nonsampling errors. To reduce sampling variability when an optimum balance of landline and cell phone samples is not feasible, we develop an application of shrinkage estimation. We demonstrate the implications for survey weighting of a differential nonresponse mechanism by telephone status...
August 16, 2019: Statistics in Medicine
Stijn Vansteelandt, Martin Linder, Sjouke Vandenberghe, Johan Steen, Jesper Madsen
In this article, we will present statistical methods to assess to what extent the effect of a randomised treatment (versus control) on a time-to-event endpoint might be explained by the effect of treatment on a mediator of interest, a variable that is measured longitudinally at planned visits throughout the trial. In particular, we will show how to identify and infer the path-specific effect of treatment on the event time via the repeatedly measured mediator levels. The considered proposal addresses complications due to patients dying before the mediator is assessed, due to the mediator being repeatedly measured, and due to posttreatment confounding of the effect of the mediator by other mediators...
August 14, 2019: Statistics in Medicine
Yongqiang Tang, Ronan Fitzpatrick
Recurrent events arise frequently in biomedical research, where the subject may experience the same type of events more than once. The Andersen-Gill (AG) model has become increasingly popular in the analysis of recurrent events particularly when the event rate is not constant over time. We propose a procedure for calculating the power and sample size for the robust Wald test from the AG model in superiority, noninferiority, and equivalence clinical trials. Its performance is demonstrated by numerical examples...
August 9, 2019: Statistics in Medicine
Jung In Kim, Feng-Chang Lin, Jason P Fine
Recurrent event data frequently occur in longitudinal studies when subjects experience more than one event during the observation period. Often, the occurrence of subsequent events is associated with the experience of previous events. Such dependence is commonly ignored in the application of standard recurrent event methodology. In this paper, we utilize a Cox-type regression model with time-varying triggering effect depending on the number and timing of previous events to enhance both model fit and prediction...
August 8, 2019: Statistics in Medicine
Stuart G Baker
Many clinical or prevention studies involve missing or censored outcomes. Maximum likelihood (ML) methods provide a conceptually straightforward approach to estimation when the outcome is partially missing. Methods of implementing ML methods range from the simple to the complex, depending on the type of data and the missing-data mechanism. Simple ML methods for ignorable missing-data mechanisms (when data are missing at random) include complete-case analysis, complete-case analysis with covariate adjustment, survival analysis with covariate adjustment, and analysis via propensity-to-be-missing scores...
August 8, 2019: Statistics in Medicine
Andreas Gleiss, Michael Schemper
We suggest measures to quantify the degrees of necessity and of sufficiency of prognostic factors for dichotomous and for survival outcomes. A cause, represented by certain values of prognostic factors, is considered necessary for an event if, without the cause, the event cannot develop. It is considered sufficient for an event if the event is unavoidable in the presence of the cause. Necessity and sufficiency can be seen as the two faces of causation, and this symmetry and equal relevance are reflected by the suggested measures...
August 6, 2019: Statistics in Medicine
Cécile Proust-Lima, Viviane Philipps, Jean-François Dartigues
As other neurodegenerative diseases, Alzheimer's disease, the most frequent dementia in the elderly, is characterized by multiple progressive impairments in the brain structure and in clinical functions such as cognitive functioning and functional disability. Until recently, these components were mostly studied independently because no joint model for multivariate longitudinal data and time to event was available in the statistical community. Yet, these components are fundamentally interrelated in the degradation process toward dementia and should be analyzed together...
August 6, 2019: Statistics in Medicine
Baldur P Magnusson, Heinz Schmidli, Nicolas Rouyrre, Daniel O Scharfstein
The treatment effect in subgroups of patients is often of interest in randomized controlled clinical trials, as this may provide useful information on how to treat which patients best. When a specific subgroup is characterized by the absence of certain events that happen postrandomization, a naive analysis on the subset of patients without these events may be misleading. The principal stratification framework allows one to define an appropriate causal estimand in such settings. Statistical inference for the principal stratum estimand hinges on scientifically justified assumptions, which can be included with Bayesian methods through prior distributions...
August 6, 2019: Statistics in Medicine
Kan Li, Sheng Luo
This paper is motivated by combining serial neurocognitive assessments and other clinical variables for monitoring the progression of Alzheimer's disease (AD). We propose a novel framework for the use of multiple longitudinal neurocognitive markers to predict the progression of AD. The conventional joint modeling longitudinal and survival data approach is not applicable when there is a large number of longitudinal outcomes. We introduce various approaches based on functional principal component for dimension reduction and feature extraction from multiple longitudinal outcomes...
August 6, 2019: Statistics in Medicine
Ewout W Steyerberg, Daan Nieboer, Thomas P A Debray, Hans C van Houwelingen
Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta-analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6-month mortality based on individual patient data using meta-analytic techniques (15 studies, n = 11 022 patients)...
August 2, 2019: Statistics in Medicine
Johan Zetterqvist, Karel Vermeulen, Stijn Vansteelandt, Arvid Sjölander
Epidemiologic research often aims to estimate the association between a binary exposure and a binary outcome, while adjusting for a set of covariates (eg, confounders). When data are clustered, as in, for instance, matched case-control studies and co-twin-control studies, it is common to use conditional logistic regression. In this model, all cluster-constant covariates are absorbed into a cluster-specific intercept, whereas cluster-varying covariates are adjusted for by explicitly adding these as explanatory variables to the model...
August 2, 2019: Statistics in Medicine
Amanda Fernández-Fontelo, Alejandra Cabaña, Harry Joe, Pedro Puig, David Moriña
Underreporting in gender-based violence data is a worldwide problem leading to the underestimation of the magnitude of this social and public health concern. This problem deteriorates the data quality, providing poor and biased results that lead society to misunderstand the actual scope of this domestic violence issue. The present work proposes time series models for underreported counts based on a latent integer autoregressive of order 1 time series with Poisson distributed innovations and a latent underreporting binary state, that is, a first-order Markov chain...
July 29, 2019: Statistics in Medicine
Christopher Yarnell, Ruxandra Pinto, Rob Fowler
Investigating clustered data requires consideration of the variation across clusters, including consideration of the component of the total individual variance that is at the cluster level. The median odds ratio and analogues are useful intuitive measures available to communicate variability in outcomes across clusters using the variance of random intercepts from a multilevel regression model. However, the median odds ratio cannot describe variability across clusters for different patient subgroups because the random intercepts do not vary by subgroup...
July 29, 2019: Statistics in Medicine
Mengyun Wu, Shuangge Ma
For the pathogenesis of complex diseases, gene-environment (G-E) interactions have been shown to have important implications. G-E interaction analysis can be challenging with the need to jointly analyze a large number of main effects and interactions and to respect the "main effects, interactions" hierarchical constraint. Extensive methodological developments on G-E interaction analysis have been conducted in recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate long-tailed distributions/data contamination, make the restricted assumption of linear effects, and cannot effectively accommodate missingness in E variables...
July 29, 2019: Statistics in Medicine
C Rivera-Rodriguez, D Spiegelman, S Haneuse
In public health research, information that is readily available may be insufficient to address the primary question(s) of interest. One cost-efficient way forward, especially in resource-limited settings, is to conduct a two-phase study in which the population is initially stratified, at phase I, by the outcome and/or some categorical risk factor(s). At phase II detailed covariate data is ascertained on a subsample within each phase I strata. While analysis methods for two-phase designs are well established, they have focused exclusively on settings in which participants are assumed to be independent...
July 29, 2019: Statistics in Medicine
Erqian Li, Maozai Tian, Man-Lai Tang
The proportional subdistribution hazard regression model has been widely used by clinical researchers for analyzing competing risks data. It is well known that quantile regression provides a more comprehensive alternative to model how covariates influence not only the location but also the entire conditional distribution. In this paper, we develop variable selection procedures based on penalized estimating equations for competing risks quantile regression. Asymptotic properties of the proposed estimators including consistency and oracle properties are established...
July 29, 2019: Statistics in Medicine
Soutrik Mandal, Suojin Wang, Samiran Sinha
Among several semiparametric models, the Cox proportional hazard model is widely used to assess the association between covariates and the time-to-event when the observed time-to-event is interval-censored. Often, covariates are measured with error. To handle this covariate uncertainty in the Cox proportional hazard model with the interval-censored data, flexible approaches have been proposed. To fill a gap and broaden the scope of statistical applications to analyze time-to-event data with different models, in this paper, a general approach is proposed for fitting the semiparametric linear transformation model to interval-censored data when a covariate is measured with error...
July 25, 2019: Statistics in Medicine
Jerald F Lawless, Richard J Cook
A framework is proposed for the joint modeling of life history and loss to follow-up (LTF) processes in cohort studies. This framework provides a basis for discussing independence conditions for LTF and censoring and examining the implications of dependent LTF. We consider failure time and more general life history processes. The joint models are based on multistate processes with expanded state spaces encompassing both the life history and LTF processes. Tracing studies are discussed as a means of investigating the presence of dependent censoring and providing valid estimates of transition intensities and state occupancy probabilities...
July 24, 2019: Statistics in Medicine
Changyong Feng, Bokai Wang, Hongyue Wang
The relative risk, risk difference, and odds ratio are three major indices of differences in risks of diseases between different groups. Although widely used in research and practice in biomedical and epidemiologic research, misconceptions are not uncommon about their relationships. Many publications offer contradicting advices in how to use them in studies. Some biomedical researchers believe that these indices are related in a monotone fashion, and, thus, changes in one direction in one of the indices can be interpreted as same directional changes in the other two...
July 23, 2019: Statistics in Medicine
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"