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Statistical Methods in Medical Research

Takeshi Emura, Masahiro Nakatochi, Shigeyuki Matsui, Hirofumi Michimae, Virginie Rondeau
Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data...
January 1, 2017: Statistical Methods in Medical Research
Wei Jiang, Weichuan Yu
In genome-wide association studies, we normally discover associations between genetic variants and diseases/traits in primary studies, and validate the findings in replication studies. We consider the associations identified in both primary and replication studies as true findings. An important question under this two-stage setting is how to determine significance levels in both studies. In traditional methods, significance levels of the primary and replication studies are determined separately. We argue that the separate determination strategy reduces the power in the overall two-stage study...
January 1, 2017: Statistical Methods in Medical Research
Liang Li, Tom Greene, Bo Hu
The time-dependent receiver operating characteristic curve is often used to study the diagnostic accuracy of a single continuous biomarker, measured at baseline, on the onset of a disease condition when the disease onset may occur at different times during the follow-up and hence may be right censored. Due to right censoring, the true disease onset status prior to the pre-specified time horizon may be unknown for some patients, which causes difficulty in calculating the time-dependent sensitivity and specificity...
November 27, 2016: Statistical Methods in Medical Research
Tyson H Holmes, Shou-Hua Li, David J McCann
The design of pharmacological trials for management of substance use disorders is shifting toward outcomes of successful individual-level behavior (abstinence or no heavy use). While binary success/failure analyses are common, McCann and Li (CNS Neurosci Ther 2012; 18: 414-418) introduced "number of beyond-threshold weeks of success" (NOBWOS) scores to avoid dichotomized outcomes. NOBWOS scoring employs an efficacy "hurdle" with values reflecting duration of success. Here, we evaluate NOBWOS scores rigorously...
November 23, 2016: Statistical Methods in Medical Research
Briana Cameron, Denise A Esserman
The two-stage (or doubly) randomized preference trial design is an important tool for researchers seeking to disentangle the role of patient treatment preference on treatment response through estimation of selection and preference effects. Up until now, these designs have been limited by their assumption of equal preference rates and effect sizes across the entire study population. We propose a stratified two-stage randomized trial design that addresses this limitation. We begin by deriving stratified test statistics for the treatment, preference, and selection effects...
November 21, 2016: Statistical Methods in Medical Research
Abdullah Masud, Wanzhu Tu, Zhangsheng Yu
Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods...
November 16, 2016: Statistical Methods in Medical Research
Masahiko Gosho, Kazushi Maruo, Ryota Ishii, Akihiro Hirakawa
The total score, which is calculated as the sum of scores in multiple items or questions, is repeatedly measured in longitudinal clinical studies. A mixed effects model for repeated measures method is often used to analyze these data; however, if one or more individual items are not measured, the method cannot be directly applied to the total score. We develop two simple and interpretable procedures that infer fixed effects for a longitudinal continuous composite variable. These procedures consider that the items that compose the total score are multivariate longitudinal continuous data and, simultaneously, handle subject-level and item-level missing data...
November 16, 2016: Statistical Methods in Medical Research
Chongyang Duan, Yingshu Cao, Lizhi Zhou, Ming T Tan, Pingyan Chen
Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango's score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice...
November 16, 2016: Statistical Methods in Medical Research
Wenle Zhao, Vance W Berger, Zhenning Yu
The maximal procedure is a restricted randomization method that maximizes the number of feasible allocation sequences under the constraints of the maximum tolerated imbalance and the allocation sequence length. It assigns an equal probability to all feasible sequences. However, its implementation is not easy due to the lack of the Markovian property of the conditional allocation probabilities. In this paper, we propose the asymptotic maximal procedure, which replaces the sequence-length-dependent conditional allocation probabilities with their asymptotic values...
November 16, 2016: Statistical Methods in Medical Research
Jeevanantham Rajeswaran, Eugene H Blackstone, John Barnard
In many longitudinal follow-up studies, we observe more than one longitudinal outcome. Impaired renal and liver functions are indicators of poor clinical outcomes for patients who are on mechanical circulatory support and awaiting heart transplant. Hence, monitoring organ functions while waiting for heart transplant is an integral part of patient management. Longitudinal measurements of bilirubin can be used as a marker for liver function and glomerular filtration rate for renal function. We derive an approximation to evolution of association between these two organ functions using a bivariate nonlinear mixed effects model for continuous longitudinal measurements, where the two submodels are linked by a common distribution of time-dependent latent variables and a common distribution of measurement errors...
November 16, 2016: Statistical Methods in Medical Research
Guogen Shan
In an agreement test between two raters with binary endpoints, existing methods for sample size calculation are always based on asymptotic approaches that use limiting distributions of a test statistic under null and alternative hypotheses. These calculated sample sizes may be not reliable due to the unsatisfactory type I error control of asymptotic approaches. We propose a new sample size calculation based on exact approaches which control for the type I error rate. The two exact approaches are considered: one approach based on maximization and the other based on estimation and maximization...
November 16, 2016: Statistical Methods in Medical Research
Sylvie Scolas, Catherine Legrand, Abderrahim Oulhaj, Anouar El Ghouch
Models for interval-censored survival data presenting a fraction of "cure" or "immune" patients have recently been proposed in the literature, particularly extending the mixture cure model to interval-censored data. However, little is known about the goodness-of-fit of such models. In a mixture cure model, the survival distribution of the entire population is improper and expressed in terms of the survival distribution of uncured individuals, i.e. the latency part of the model, and the probability to experience the event of interest, i...
November 4, 2016: Statistical Methods in Medical Research
Junsheng Ma, Brian P Hobbs, Francesco C Stingo
Over the past decade, a tremendous amount of resources have been dedicated to the pursuit of developing genomic signatures that effectively match patients with targeted therapies. Although dozens of therapies that target DNA mutations have been developed, the practice of studying single candidate genes has limited our understanding of cancer. Moreover, many studies of multiple-gene signatures have been conducted for the purpose of identifying prognostic risk cohorts, and thus are limited for selecting personalized treatments...
November 1, 2016: Statistical Methods in Medical Research
David M Hughes, Arnošt Komárek, Gabriela Czanner, Marta Garcia-Fiñana
There is an emerging need in clinical research to accurately predict patients' disease status and disease progression by optimally integrating multivariate clinical information. Clinical data are often collected over time for multiple biomarkers of different types (e.g. continuous, binary and counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model...
October 26, 2016: Statistical Methods in Medical Research
Geert Verbeke, Steffen Fieuws, Geert Molenberghs, Marie Davidian
No abstract text is available yet for this article.
February 2017: Statistical Methods in Medical Research
Moonseong Heo, Alain H Litwin, Oni Blackstock, Namhee Kim, Julia H Arnsten
We derived sample size formulae for detecting main effects in group-based randomized clinical trials with different levels of data hierarchy between experimental and control arms. Such designs are necessary when experimental interventions need to be administered to groups of subjects whereas control conditions need to be administered to individual subjects. This type of trial, often referred to as a partially nested or partially clustered design, has been implemented for management of chronic diseases such as diabetes and is beginning to emerge more commonly in wider clinical settings...
February 2017: Statistical Methods in Medical Research
Hao Liu, Yu Shen, Jing Ning, Jing Qin
Cross-sectional prevalent cohort design has drawn considerable interests in the studies of association between risk factors and time-to-event outcome. The sampling scheme in such design gives rise to length-biased data that require specialized analysis strategy but can improve study efficiency. The power and sample size calculation methods are however lacking for studies with prevalent cohort design, and using the formula developed for traditional survival data may overestimate sample size. We derive the sample size formulas that are appropriate for the design of cross-sectional prevalent cohort studies, under the assumptions of exponentially distributed event time and uniform follow-up for cross-sectional prevalent cohort design...
February 2017: Statistical Methods in Medical Research
Asc Conlon, Jmg Taylor, M R Elliott
In clinical trials, a surrogate outcome ( S) can be measured before the outcome of interest ( T) and may provide early information regarding the treatment ( Z) effect on T. Many methods of surrogacy validation rely on models for the conditional distribution of T given Z and S. However, S is a post-randomization variable, and unobserved, simultaneous predictors of S and T may exist, resulting in a non-causal interpretation. Frangakis and Rubin developed the concept of principal surrogacy, stratifying on the joint distribution of the surrogate marker under treatment and control to assess the association between the causal effects of treatment on the marker and the causal effects of treatment on the clinical outcome...
February 2017: Statistical Methods in Medical Research
Jeevanantham Rajeswaran, Eugene H Blackstone
In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software...
February 2017: Statistical Methods in Medical Research
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No abstract text is available yet for this article.
December 2016: Statistical Methods in Medical Research
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