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Statistical Methods in Medical Research

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No abstract text is available yet for this article.
October 2017: Statistical Methods in Medical Research
Narayanaswamy Balakrishnan
No abstract text is available yet for this article.
October 2017: Statistical Methods in Medical Research
Yolanda Hagar, James J Dignam, Vanja Dukic
The effects of predictors on time to failure may be difficult to assess in cancer studies with longer follow-up, as the commonly used assumption of proportionality of hazards holding over an extended period is often questionable. Motivated by a long-term prostate cancer clinical trial, we contrast and compare four powerful methods for estimation of the hazard rate. These four methods allow for varying degrees of smoothness as well as covariates with effects that vary over time. We pay particular attention to an extended multiresolution hazard estimator, which is a flexible, semi-parametric, Bayesian method for joint estimation of predictor effects and the hazard rate...
October 2017: Statistical Methods in Medical Research
Stephen Burgess, Dylan S Small, Simon G Thompson
Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure...
October 2017: Statistical Methods in Medical Research
Sylwia Bujkiewicz, John R Thompson, Enti Spata, Keith R Abrams
We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing-remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival...
October 2017: Statistical Methods in Medical Research
Jack Bowden, Lorenzo Trippa
Bayesian adaptive trials have the defining feature that the probability of randomization to a particular treatment arm can change as information becomes available as to its true worth. However, there is still a general reluctance to implement such designs in many clinical settings. One area of concern is that their frequentist operating characteristics are poor or, at least, poorly understood. We investigate the bias induced in the maximum likelihood estimate of a response probability parameter, p, for binary outcome by the process of adaptive randomization...
October 2017: Statistical Methods in Medical Research
Georgiana Onicescu, Andrew Lawson, Jiajia Zhang, Mulugeta Gebregziabher, Kristin Wallace, Jan M Eberth
In this paper, we extend the spatially explicit survival model for small area cancer data by allowing dependency between space and time and using accelerated failure time models. Spatial dependency is modeled directly in the definition of the survival, density, and hazard functions. The models are developed in the context of county level aggregated data. Two cases are considered: the first assumes that the spatial and temporal distributions are independent; the second allows for dependency between the spatial and temporal components...
October 2017: Statistical Methods in Medical Research
Jing Zhang, Haitao Chu, Hwanhee Hong, Beth A Virnig, Bradley P Carlin
Network meta-analysis expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, synthesizing both direct and indirect information and thus strengthening inference. Since most of trials only compare two treatments, a typical data set in a network meta-analysis managed as a trial-by-treatment matrix is extremely sparse, like an incomplete block structure with significant missing data. Zhang et al. proposed an arm-based method accounting for correlations among different treatments within the same trial and assuming that absent arms are missing at random...
October 2017: Statistical Methods in Medical Research
(no author information available yet)
No abstract text is available yet for this article.
August 2017: Statistical Methods in Medical Research
Timothy NeCamp, Amy Kilbourne, Daniel Almirall
Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens...
August 2017: Statistical Methods in Medical Research
Eric B Laber, Marie Davidian
We asked three leading researchers in the area of dynamic treatment regimes to share their stories on how they became interested in this topic and their perspectives on the most important opportunities and challenges for the future.
August 2017: Statistical Methods in Medical Research
Christopher E Davies, Gary Fv Glonek, Lynne C Giles
One purpose of a longitudinal study is to gain a better understanding of how an outcome of interest changes among a given population over time. In what follows, a trajectory will be taken to mean the series of measurements of the outcome variable for an individual. Group-based trajectory modelling methods seek to identify subgroups of trajectories within a population, such that trajectories that are grouped together are more similar to each other than to trajectories in distinct groups. Group-based trajectory models generally assume a certain structure in the covariances between measurements, for example conditional independence, homogeneous variance between groups or stationary variance over time...
August 2017: Statistical Methods in Medical Research
Jing Ning, Mohammad H Rahbar, Sangbum Choi, Jin Piao, Chuan Hong, Deborah J Del Junco, Elaheh Rahbar, Erin E Fox, John B Holcomb, Mei-Cheng Wang
In comparative effectiveness studies of multicomponent, sequential interventions like blood product transfusion (plasma, platelets, red blood cells) for trauma and critical care patients, the timing and dynamics of treatment relative to the fragility of a patient's condition is often overlooked and underappreciated. While many hospitals have established massive transfusion protocols to ensure that physiologically optimal combinations of blood products are rapidly available, the period of time required to achieve a specified massive transfusion standard (e...
August 2017: Statistical Methods in Medical Research
Valerie A Smith, Brian Neelon, John S Preisser, Matthew L Maciejewski
In health services research, it is common to encounter semicontinuous data, characterized by a point mass at zero followed by a right-skewed continuous distribution with positive support. Examples include health expenditures, in which the zeros represent a subpopulation of patients who do not use health services, while the continuous distribution describes the level of expenditures among health services users. Longitudinal semicontinuous data are typically analyzed using two-part random-effect mixtures with one component that models the probability of health services use, and a second component that models the distribution of log-scale positive expenditures among users...
August 2017: Statistical Methods in Medical Research
Ilmari Ahonen, Denis Larocque, Jaakko Nevalainen
Outlier detection covers the wide range of methods aiming at identifying observations that are considered unusual. Novelty detection, on the other hand, seeks observations among newly generated test data that are exceptional compared with previously observed training data. In many applications, the general existence of novelty is of more interest than identifying the individual novel observations. For instance, in high-throughput cancer treatment screening experiments, it is meaningful to test whether any new treatment effects are seen compared with existing compounds...
August 2017: Statistical Methods in Medical Research
Keith A Marill, Yuchiao Chang, Kim F Wong, Ari B Friedman
Objectives Assessing high-sensitivity tests for mortal illness is crucial in emergency and critical care medicine. Estimating the 95% confidence interval (CI) of the likelihood ratio (LR) can be challenging when sample sensitivity is 100%. We aimed to develop, compare, and automate a bootstrapping method to estimate the negative LR CI when sample sensitivity is 100%. Methods The lowest population sensitivity that is most likely to yield sample sensitivity 100% is located using the binomial distribution. Random binomial samples generated using this population sensitivity are then used in the LR bootstrap...
August 2017: Statistical Methods in Medical Research
Airlane Pereira Alencar, Linda Lee Ho, Orlando Yesid Esparza Albarracin
To detect outbreaks of diseases in public health, several control charts have been proposed in the literature. In this context, the usual generalized linear model may be fitted for counts under a Negative Binomial distribution with a logarithm link function and the population size included as offset to model hospitalization rates. Different statistics are used to build CUSUM control charts to monitor daily hospitalizations and their performances are compared in simulation studies. The main contribution of the current paper is to consider different statistics based on transformations and the deviance residual to build control charts to monitor counts with seasonality effects and evaluate all the assumptions of the monitored statistics...
August 2017: Statistical Methods in Medical Research
Yemisi Takwoingi, Boliang Guo, Richard D Riley, Jonathan J Deeks
Hierarchical models such as the bivariate and hierarchical summary receiver operating characteristic (HSROC) models are recommended for meta-analysis of test accuracy studies. These models are challenging to fit when there are few studies and/or sparse data (for example zero cells in contingency tables due to studies reporting 100% sensitivity or specificity); the models may not converge, or give unreliable parameter estimates. Using simulation, we investigated the performance of seven hierarchical models incorporating increasing simplifications in scenarios designed to replicate realistic situations for meta-analysis of test accuracy studies...
August 2017: Statistical Methods in Medical Research
Nina Breinegaard, Sophia Rabe-Hesketh, Anders Skrondal
Generalized linear mixed models for longitudinal data assume that responses at different occasions are conditionally independent, given the random effects and covariates. Although this assumption is pivotal for consistent estimation, violation due to serial dependence is hard to assess by model elaboration. We therefore propose a targeted diagnostic test for serial dependence, called the transition model test (TMT), that is straightforward and computationally efficient to implement in standard software. The TMT is shown to have larger power than general misspecification tests...
August 2017: Statistical Methods in Medical Research
Bo Zhang, Wei Liu, Zhiwei Zhang, Yanping Qu, Zhen Chen, Paul S Albert
Joint modeling and within-cluster resampling are two approaches that are used for analyzing correlated data with informative cluster sizes. Motivated by a developmental toxicity study, we examined the performances and validity of these two approaches in testing covariate effects in generalized linear mixed-effects models. We show that the joint modeling approach is robust to the misspecification of cluster size models in terms of Type I and Type II errors when the corresponding covariates are not included in the random effects structure; otherwise, statistical tests may be affected...
August 2017: Statistical Methods in Medical Research
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