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Statistical Methods in Medical Research

Min Yuan, Xu Steven Xu, Yaning Yang, Jinfeng Xu, Xiaohui Huang, Fangbiao Tao, Liang Zhao, Liping Zhang, Jose Pinheiro
Nonlinear mixed-effects modeling is a popular approach to describe the temporal trajectory of repeated measurements of clinical endpoints collected over time in clinical trials, to distinguish the within-subject and the between-subject variabilities, and to investigate clinically important risk factors (covariates) that may partly explain the between-subject variability. Due to the complex computing algorithms involved in nonlinear mixed-effects modeling, estimation of covariate effects is often time-consuming and error-prone owing to local convergence...
November 9, 2018: Statistical Methods in Medical Research
M H Elsensohn, E Dantony, J Iwaz, E Villar, C Couchoud, R Ecochard
BACKGROUND: With the increase of life expectancy, *On behalf of the REIN registry. end-stage renal disease (ESRD) is affecting a growing number of people. Simultaneously, renal replacement therapies (RRTs) have considerably improved patient survival. We investigated the way current RRT practices would affect patients' survival. METHODS: We used a multi-state model to represent the transitions between RRTs and the transition to death. The concept of "crude probability of death" combined with this model allowed estimating the proportions of ESRD-related and ESRD-unrelated deaths...
November 9, 2018: Statistical Methods in Medical Research
Mohammad Ehsanul Karim, John Petkau, Paul Gustafson, Robert W Platt
No abstract text is available yet for this article.
November 5, 2018: Statistical Methods in Medical Research
Jon M Gran, Odd O Aalen
No abstract text is available yet for this article.
November 5, 2018: Statistical Methods in Medical Research
Arman Alam Siddique, Mireille E Schnitzer, Asma Bahamyirou, Guanbo Wang, Timothy H Holtz, Giovanni B Migliori, Giovanni Sotgiu, Neel R Gandhi, Mario H Vargas, Dick Menzies, Andrea Benedetti
This paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest...
October 31, 2018: Statistical Methods in Medical Research
Lyvia Biagi, Arthur Bertachi, Marga Giménez, Ignacio Conget, Jorge Bondia, Josep Antoni Martín-Fernández, Josep Vehí
The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24 h, and the times spent in each of these different ranges are co-dependent and carry only relative information; therefore, CoDA is applied to these profiles...
October 31, 2018: Statistical Methods in Medical Research
Samuel L Brilleman, Michael J Crowther, Margarita Moreno-Betancur, Jacqueline Buros Novik, James Dunyak, Nidal Al-Huniti, Robert Fox, Jeff Hammerbacher, Rory Wolfe
Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient...
October 31, 2018: Statistical Methods in Medical Research
Yaoguo Xie, Zhengjun Zhang, Paul J Rathouz, Bruce P Barrett
Semi-continuous data, also known as zero-inflated continuous data, have a substantial portion of responses equal to a single value (typically 0) and a continuous, right-skewed distribution among the remaining positive values. For jointly modeling multivariate clustered semi-continuous responses, the covariate effects in the positive parts can be proportionally constrained to the covariate effects in the logistic part, yielding a multivariate two-part fixed effects model. It is shown that, both theoretically and experimentally, the proportionally constrained model is more efficient than the unconstrained model in terms of parameter estimation, and thus provides a deeper understanding of the data structure when the proportionality structure holds...
October 31, 2018: Statistical Methods in Medical Research
Xiaoyin F Fan, Paul Gallo, Guoqin Su, Ronald Menton, Florencia Segal
In the clinical development of some new infectious disease drugs, early clinical pharmacology trials may predict with high confidence that the efficacious doses are well below the range of the safety margin. In this case, a dose-ranging study may be unnecessary after a proof-of-concept (PoC) study testing the highest dose. A multi-stage adaptive design spanning both PoC and confirmatory stages is proposed in this context. The design incorporates two interim analyses allowing strategies for stopping, continuing, or expanding the study...
October 30, 2018: Statistical Methods in Medical Research
Monia Lupparelli
In linear regression modelling, the distortion of effects after marginalizing over variables of the conditioning set has been widely studied in several contexts. For Gaussian variables, the relationship between marginal and partial regression coefficients is well established and the issue is often addressed as a result of W. G. Cochran. Possible generalizations beyond the linear Gaussian case have been developed, nevertheless the case of discrete variables is still challenging, in particular in medical and social science settings...
October 24, 2018: Statistical Methods in Medical Research
(no author information available yet)
No abstract text is available yet for this article.
October 22, 2018: Statistical Methods in Medical Research
Cristina Boschini, Klaus K Andersen, Thomas H Scheike
We present an excess risk regression model for matched cohort data, where the occurrence of some events for individuals with a disease is compared to that of healthy controls that are matched at the onset-of-disease by various factors. By using the matched structure, we show how to estimate the excess risk and its dependence on covariates on both proportional and additive form. We remove the individual effects on background mortality related to matching factors by considering differences. The model handles two different time scales, namely attained age and follow-up time...
October 22, 2018: Statistical Methods in Medical Research
Adelino Martins, Marc Aerts, Niel Hens, Andreas Wienke, Steven Abrams
Frailty models have been developed to quantify both heterogeneity as well as association in multivariate time-to-event data. In recent years, numerous shared and correlated frailty models have been proposed in the survival literature allowing for different association structures and frailty distributions. A bivariate correlated gamma frailty model with an additive decomposition of the frailty variables into a sum of independent gamma components was introduced before. Although this model has a very convenient closed-form representation for the bivariate survival function, the correlation among event- or subject-specific frailties is bounded above which becomes a severe limitation when the values of the two frailty variances differ substantially...
October 15, 2018: Statistical Methods in Medical Research
Joseph G Ibrahim, Sungduk Kim, Ming-Hui Chen, Arvind K Shah, Jianxin Lin
We examine a class of multivariate meta-regression models in the presence of individual patient data. The methodology is well motivated from several studies of cholesterol-lowering drugs where the goal is to jointly analyze the multivariate outcomes, low density lipoprotein cholesterol, high density lipoprotein cholesterol, and triglycerides. These three continuous outcome measures are correlated and shed much light on a subject's lipid status. One of the main goals in lipid research is the joint analysis of these three outcome measures in a meta-regression setting...
October 12, 2018: Statistical Methods in Medical Research
Soyoung Kim, Kwang Woo Ahn
The case-cohort design is an economical approach to estimate the effect of risk factors on the survival outcome when collecting exposure information or covariates on all patients is expensive in a large cohort study. Variables often have group structure such as categorical variables and highly correlated continuous variables. The existing literature for case-cohort data is limited to identifying non-zero variables at individual level only. In this article, we propose a bi-level variable selection method to select non-zero group and within-group variables for case-cohort data when variables have group structure...
October 11, 2018: Statistical Methods in Medical Research
Jue Wang, Sheng Luo
Impairment caused by Amyotrophic lateral sclerosis (ALS) is multidimensional (e.g. bulbar, fine motor, gross motor) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of ALS use multiple longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements...
October 11, 2018: Statistical Methods in Medical Research
Philip S Rosenberg
We develop a new age-period-cohort model for cancer surveillance research; the theory and methods are broadly applicable. In the new model, cohort deviations are weighted to account for the variable number of periods that each cohort is observed. Weighting ensures that the fitted rates can be naturally expressed as a function of age × a function of period × a function of cohort. Furthermore, the age, period, and cohort deviations are split into orthogonal quadratic components plus higher-order terms...
October 11, 2018: Statistical Methods in Medical Research
Raphaël Porcher, Justine Jacot, Jay S Wunder, David J Biau
Individualizing treatment according to patients' characteristics is central for personalized or precision medicine. There has been considerable recent research in developing statistical methods to determine optimal personalized treatment strategies by modeling the outcome of patients according to relevant covariates under each of the alternative treatments, and then relying on so-called predicted individual treatment effects. In this paper, we use potential outcomes and principal stratification frameworks and develop a multinomial model for left and right-censored data to estimate the probability that a patient is a responder given a set of baseline covariates...
October 9, 2018: Statistical Methods in Medical Research
David B Wolfson, Ana F Best, Vittorio Addona, Julian Wolfson, Shahinaz M Gadalla
It is frequently of interest to estimate the time that individuals survive with a disease, that is, to estimate the time between disease onset and occurrence of a clinical endpoint such as death. Epidemiologic survival data are commonly collected from either an incident cohort, whose members' disease onset occurs after the study baseline date, or from a cohort with prevalent disease that is followed forward in time. Incident cohort survival data are limited by study termination, while prevalent cohort data provide biased (left-truncated) survival data...
October 8, 2018: Statistical Methods in Medical Research
Brett Hanscom, James P Hughes, Brian D Williamson, Deborah Donnell
A central assumption in the design and conduct of non-inferiority trials is that the active-control therapy will have the same degree of effectiveness in the planned non-inferiority trial as in the prior placebo-controlled trials used to define the non-inferiority margin. This is referred to as the 'constancy' assumption. If the constancy assumption fails, decisions based on the chosen non-inferiority margin may be incorrect, and the study runs the risk of approving an inferior product or failing to approve a beneficial product...
October 8, 2018: Statistical Methods in Medical Research
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