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Statistical Methods in Medical Research

Mehmet C Mert, Peter Filzmoser, Gottfried Endel, Ingrid Wilbacher
Compositional data analysis refers to analyzing relative information, based on ratios between the variables in a data set. Data from epidemiology are usually treated as absolute information in an analysis. We outline the differences in both approaches for univariate and multivariate statistical analyses, using illustrative data sets from Austrian districts. Not only the results of the analyses can differ, but in particular the interpretation differs. It is demonstrated that the compositional data analysis approach leads to new and interesting insights...
June 2018: Statistical Methods in Medical Research
Ruwanthi Kolamunnage-Dona, Paula R Williamson
Joint modelling of longitudinal biomarker and event-time processes has gained its popularity in recent years as they yield more accurate and precise estimates. Considering this modelling framework, a new methodology for evaluating the time-dependent efficacy of a longitudinal biomarker for clinical endpoint is proposed in this article. In particular, the proposed model assesses how well longitudinally repeated measurements of a biomarker over various time periods (0,t) distinguish between individuals who developed the disease by time t and individuals who remain disease-free beyond time t...
June 2018: Statistical Methods in Medical Research
Pablo Martínez-Camblor, Sonia Pérez-Fernández, Norberto Corral
Receiver operating-characteristic curve is a popular graphical method frequently used in order to study the diagnostic capacity of continuous (bio)markers. In spite of the existence of a huge number of papers devoted to both theoretical and practical aspects of this topic, the construction of confidence bands has had little impact in the specialized literature. As far as the authors know, in the CRAN there are only three R packages providing receiver operating-characteristic curve confidence regions: plotROC, pROC and fbroc...
June 2018: Statistical Methods in Medical Research
Shaun R Seaman, Rachael A Hughes
Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model...
June 2018: Statistical Methods in Medical Research
Joseph S Koopmeiners, Brian P Hobbs
Randomized, placebo-controlled clinical trials are the gold standard for evaluating a novel therapeutic agent. In some instances, it may not be considered ethical or desirable to complete a placebo-controlled clinical trial and, instead, the placebo is replaced by an active comparator with the objective of showing either superiority or non-inferiority to the active comparator. In a non-inferiority trial, the experimental treatment is considered non-inferior if it retains a pre-specified proportion of the effect of the active comparator as represented by the non-inferiority margin...
May 2018: Statistical Methods in Medical Research
Weijie Chen, Berkman Sahiner, Frank Samuelson, Aria Pezeshk, Nicholas Petrick
Scores produced by statistical classifiers in many clinical decision support systems and other medical diagnostic devices are generally on an arbitrary scale, so the clinical meaning of these scores is unclear. Calibration of classifier scores to a meaningful scale such as the probability of disease is potentially useful when such scores are used by a physician. In this work, we investigated three methods (parametric, semi-parametric, and non-parametric) for calibrating classifier scores to the probability of disease scale and developed uncertainty estimation techniques for these methods...
May 2018: Statistical Methods in Medical Research
Simon R White, Graciela Muniz-Terrera, Fiona E Matthews
Many medical (and ecological) processes involve the change of shape, whereby one trajectory changes into another trajectory at a specific time point. There has been little investigation into the study design needed to investigate these models. We consider the class of fixed effect change-point models with an underlying shape comprised two joined linear segments, also known as broken-stick models. We extend this model to include two sub-groups with different trajectories at the change-point, a change and no change class, and also include a missingness model to account for individuals with incomplete follow-up...
May 2018: Statistical Methods in Medical Research
Zhen Zhang, Samiran Sinha, Tapabrata Maiti, Eva Shipp
Accelerated failure time model is a popular model to analyze censored time-to-event data. Analysis of this model without assuming any parametric distribution for the model error is challenging, and the model complexity is enhanced in the presence of large number of covariates. We developed a nonparametric Bayesian method for regularized estimation of the regression parameters in a flexible accelerated failure time model. The novelties of our method lie in modeling the error distribution of the accelerated failure time nonparametrically, modeling the variance as a function of the mean, and adopting a variable selection technique in modeling the mean...
April 2018: Statistical Methods in Medical Research
Carine Bellera, Cécile Proust-Lima, Lawrence Joseph, Pierre Richaud, Jeremy Taylor, Howard Sandler, James Hanley, Simone Mathoulin-Pélissier
Background Biomarker series can indicate disease progression and predict clinical endpoints. When a treatment is prescribed depending on the biomarker, confounding by indication might be introduced if the treatment modifies the marker profile and risk of failure. Objective Our aim was to highlight the flexibility of a two-stage model fitted within a Bayesian Markov Chain Monte Carlo framework. For this purpose, we monitored the prostate-specific antigens in prostate cancer patients treated with external beam radiation therapy...
April 2018: Statistical Methods in Medical Research
Julien Péron, Marc Buyse, Brice Ozenne, Laurent Roche, Pascal Roy
Generalized pairwise comparisons have been proposed to permit a comprehensive assessment of several prioritized outcomes between two groups of observations. This procedure estimates Δ, the net chance of a better outcome with treatment than with control by comparing the patients outcomes among all possible pairs taking one patient from the treatment group and one patient from the control group. For time to event outcomes, the standard procedure of generalized pairwise comparisons is analogous to the Gehan's modification of the Mann-Whitney test which is biased in presence of censored observation and less powerful than Efron's modification of this test...
April 2018: Statistical Methods in Medical Research
Huirong Zhu, Stacia M DeSantis, Sheng Luo
Longitudinal zero-inflated count data are encountered frequently in substance-use research when assessing the effects of covariates and risk factors on outcomes. Often, both the time to a terminal event such as death or dropout and repeated measure count responses are collected for each subject. In this setting, the longitudinal counts are censored by the terminal event, and the time to the terminal event may depend on the longitudinal outcomes. In the study described herein, we expand the class of joint models for longitudinal and survival data to accommodate zero-inflated counts and time-to-event data by using a Cox proportional hazards model with piecewise constant baseline hazard...
April 2018: Statistical Methods in Medical Research
Peter C Austin, Nathaniel Jembere, Maria Chiu
Researchers are increasingly using complex population-based sample surveys to estimate the effects of treatments, exposures and interventions. In such analyses, statistical methods are essential to minimize the effect of confounding due to measured covariates, as treated subjects frequently differ from control subjects. Methods based on the propensity score are increasingly popular. Minimal research has been conducted on how to implement propensity score matching when using data from complex sample surveys...
April 2018: Statistical Methods in Medical Research
Selene Yue Xu, Sandahl Nelson, Jacqueline Kerr, Suneeta Godbole, Ruth Patterson, Gina Merchant, Ian Abramson, John Staudenmayer, Loki Natarajan
Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns...
April 2018: Statistical Methods in Medical Research
Yirui Hu, Donald R Hoover
Randomized stepped-wedge (R-SW) designs are increasingly used to evaluate interventions targeting continuous longitudinal outcomes measured at T-fixed time points. Typically, all units start out untreated, and randomly chosen units switch to intervention at sequential time points until all receive intervention. As randomization is not always feasible, non-randomized stepped-wedge (NR-SW) designs (units switching to intervention are not randomly chosen) have attracted researchers. We develop an orthogonlized generalized least squares framework for both R-SW and NR-SW designs...
April 2018: Statistical Methods in Medical Research
Chunyan Cai, Jing Ning, Xuelin Huang
Much progress has been made in the area of adaptive designs for clinical trials. However, little has been done regarding adaptive designs to identify optimal treatment strategies in animal studies. Motivated by an animal study of a novel strategy for treating strokes, we propose a Bayesian multi-stage cost-effectiveness design to simultaneously identify the optimal dose and determine the therapeutic treatment window for administrating the experimental agent. We consider a non-monotonic pattern for the dose-schedule-efficacy relationship and develop an adaptive shrinkage algorithm to assign more cohorts to admissible strategies...
April 2018: Statistical Methods in Medical Research
Rolando de la Cruz, Claudio Fuentes, Cristian Meza, Vicente Núñez-Antón
Consider longitudinal observations across different subjects such that the underlying distribution is determined by a non-linear mixed-effects model. In this context, we look at the misclassification error rate for allocating future subjects using cross-validation, bootstrap algorithms (parametric bootstrap, leave-one-out, .632 and [Formula: see text]), and bootstrap cross-validation (which combines the first two approaches), and conduct a numerical study to compare the performance of the different methods...
April 2018: Statistical Methods in Medical Research
Marzieh Mahmoodi, Abbas Moghimbeigi, Kazem Mohammad, Javad Faradmal
This study proposes semiparametric models for analysis of hierarchical count data containing excess zeros and overdispersion simultaneously. The methods discussed in this paper handle nonlinear covariate effects through flexible semiparametric multilevel regression techniques. This is performed by providing a comprehensive comparison of semiparametric multilevel zero-inflated negative binomial and semiparametric multilevel zero-inflated generalized Poisson models under the real and simulated data. An EM algorithm based on Newton-Raphson equations for maximum penalized likelihood estimation approach is developed...
April 2018: Statistical Methods in Medical Research
Guogen Shan
Simon's two-stage design has been widely used in early phase clinical trials to assess the activity of a new investigated treatment. In practice, the actual sample sizes do not always follow the study design precisely, especially in the second stage. When over- or under-enrollment occurs in a study, the original critical values for the study design are no longer valid for making proper statistical inference in a clinical trial. The hypothesis for such studies is always one-sided, and the null hypothesis is rejected when only a few responses are observed...
April 2018: Statistical Methods in Medical Research
Chawarat Rotejanaprasert, Andrew Lawson
Early detection of unusual health events depends on the ability to rapidly detect any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties...
April 2018: Statistical Methods in Medical Research
John Ferguson, Alberto Alvarez-Iglesias, John Newell, John Hinde, Martin O'Donnell
Chronic diseases tend to depend on a large number of risk factors, both environmental and genetic. Average attributable fractions were introduced by Eide and Gefeller as a way of partitioning overall disease burden into contributions from individual risk factors; this may be useful in deciding which risk factors to target in disease interventions. Here, we introduce new estimation methods for average attributable fractions that are appropriate for both case-control designs and prospective studies. Confidence intervals, derived using Monte Carlo simulation, are also described...
April 2018: Statistical Methods in Medical Research
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