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Statistics in Medicine

Sarah C Conner, Lisa M Sullivan, Emelia J Benjamin, Michael P LaValley, Sandro Galea, Ludovic Trinquart
In observational studies with censored data, exposure-outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre-specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression-based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model-free method of taking the area under the survival function...
May 22, 2019: Statistics in Medicine
Yicheng Kang, Xiaodong Gong, Jiti Gao, Peihua Qiu
Errors-in-variables (EIV) regression is widely used in econometric models. The statistical analysis becomes challenging when the regression function is discontinuous and the distribution of measurement error is unknown. In the literature, most existing jump regression methods either assume that there is no measurement error involved or require that jumps are explicitly detected before the regression function can be estimated. In some applications, however, the ultimate goal is to estimate the regression function and to preserve the jumps in the process of estimation...
May 22, 2019: Statistics in Medicine
Tanja Högg, Yinshan Zhao, Paul Gustafson, John Petkau, John Fisk, Ruth Ann Marrie, Helen Tremlett
In epidemiological studies of secondary data sources, lack of accurate disease classifications often requires investigators to rely on diagnostic codes generated by physicians or hospital systems to identify case and control groups, resulting in a less-than-perfect assessment of the disease under investigation. Moreover, because of differences in coding practices by physicians, it is hard to determine the factors that affect the chance of an incorrectly assigned disease status. What results is a dilemma where assumptions of non-differential misclassification are questionable but, at the same time, necessary to proceed with statistical analyses...
May 21, 2019: Statistics in Medicine
Doug Morrison, Oliver Laeyendecker, Ron Brookmeyer
The cross-sectional approach to HIV incidence estimation overcomes some of the challenges with longitudinal cohort studies and has been successfully applied in many settings around the world. However, the cross-sectional approach does rely on an initial training data set to develop and calibrate the statistical methods to be used in cross-sectional surveys. The problem addressed in this paper is that the initial training data set may, over time, not reflect the current target population of interest because of evolution of the epidemic...
May 21, 2019: Statistics in Medicine
Kim May Lee, James Wason, Nigel Stallard
Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on-going clinical trials using a "platform" trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature...
May 21, 2019: Statistics in Medicine
C Baayen, C Volteau, C Flamant, P Blanche
Sequential designs and competing risks methodology are both well established. Their combined use has recently received some attention from a theoretical perspective, but their joint application in practice has been discussed less. The aim of this paper is to provide the applied statistician with a basic understanding of both sequential design theory and competing risks methodology and how to combine them in practice. Relevant references to more detailed theoretical discussions are provided, and all discussions are illustrated using a real case study...
May 17, 2019: Statistics in Medicine
John Lawrence
In multiple testing scenarios, one of the more popular definitions of the error rate is the familywise error rate (FWER). The per-family error rate (PFER) is often not considered. The PFER deserves more attention than it currently receives. Both of these concepts were formulated by Tukey. We highlight some of the good and bad qualities of the FWER and the PFER. We find the FWER and PFER for four commonly used multiple comparison procedures.
May 17, 2019: Statistics in Medicine
Kerrie P Nelson, Don Edwards
Agreement between experts' ratings is an important prerequisite for an effective screening procedure. In clinical settings, large-scale studies are often conducted to compare the agreement of experts' ratings between new and existing medical tests, for example, digital versus film mammography. Challenges arise in these studies where many experts rate the same sample of patients undergoing two medical tests, leading to a complex correlation structure between experts' ratings. Here, we propose a novel paired kappa measure to compare the agreement between the binary ratings of many experts across two medical tests...
May 17, 2019: Statistics in Medicine
Hayeon Park, Lawrence M Leemis
We propose two measures of performance for a confidence interval for a binomial proportion p: the root mean squared error and the mean absolute deviation. We also devise a confidence interval for p based on the actual coverage function that combines several existing approximate confidence intervals. This "Ensemble" confidence interval has improved statistical properties over the constituent confidence intervals. Software in an R package, which can be used in devising and assessing these confidence intervals, is available on CRAN...
May 17, 2019: Statistics in Medicine
Lubna Amro, Frank Konietschke, Markus Pauly
We consider statistical procedures for hypothesis testing of real valued functionals of matched pairs with missing values. In order to improve the accuracy of existing methods, we propose a novel multiplication combination procedure. Dividing the observed data into dependent (completely observed) pairs and independent (incompletely observed) components, it is based on combining separate results of adequate tests for the two sub data sets. Our methods can be applied for parametric as well as semiparametric and nonparametric models and make use of all available data...
May 17, 2019: Statistics in Medicine
Manisha Desai, Maria E Montez-Rath, Kristopher Kapphahn, Vilija R Joyce, Maya B Mathur, Ariadna Garcia, Natasha Purington, Douglas K Owens
The treatment of missing data in comparative effectiveness studies with right-censored outcomes and time-varying covariates is challenging because of the multilevel structure of the data. In particular, the performance of an accessible method like multiple imputation (MI) under an imputation model that ignores the multilevel structure is unknown and has not been compared to complete-case (CC) and single imputation methods that are most commonly applied in this context. Through an extensive simulation study, we compared statistical properties among CC analysis, last value carried forward, mean imputation, the use of missing indicators, and MI-based approaches with and without auxiliary variables under an extended Cox model when the interest lies in characterizing relationships between non-missing time-varying exposures and right-censored outcomes...
May 17, 2019: Statistics in Medicine
Igor Akushevich, Julia Kravchenko, Arseniy P Yashkin, Fang Fang, Anatoliy I Yashin
BACKGROUND: Time trends of lung cancer prevalence and mortality are the result of three competing processes: changes in the incidence rate, stage-specific survival, and ascertainment at early stages. Improvements in these measures act concordantly to improve disease-related mortality, but push the prevalence rate in opposite directions making a qualitative interpretation difficult. The goal of this paper is to evaluate the relative contributions of these components to changes in lung cancer prevalence and mortality...
May 13, 2019: Statistics in Medicine
Lu Mao
Rodent survival-sacrifice experiments are routinely conducted to assess the tumor-inducing potential of a certain exposure or drug. Because most tumors under study are impalpable, animals are examined at death for evidence of tumor formation. In some studies, the cause of death is ascertained by a pathologist to account for possible correlation between tumor development and death. Existing methods for survival-sacrifice data with cause-of-death information have been restricted to multi-group testing or one-sample estimation of tumor onset distribution and thus do not provide a natural way to quantify treatment effect or dose-response relationship...
May 9, 2019: Statistics in Medicine
Vera Djordjilović, Christian M Page, Jon Michael Gran, Therese H Nøst, Torkjel M Sandanger, Marit B Veierød, Magne Thoresen
We address the problem of testing whether a possibly high-dimensional vector may act as a mediator between some exposure variable and the outcome of interest. We propose a global test for mediation, which combines a global test with the intersection-union principle. We discuss theoretical properties of our approach and conduct simulation studies that demonstrate that it performs equally well or better than its competitor. We also propose a multiple testing procedure, ScreenMin, that provides asymptotic control of either familywise error rate or false discovery rate when multiple groups of potential mediators are tested simultaneously...
May 9, 2019: Statistics in Medicine
Xiangyu Liu, Jing Ning, Yu Cheng, Xuelin Huang, Ruosha Li
When analyzing bivariate outcome data, it is often of scientific interest to measure and estimate the association between the bivariate outcomes. In the presence of influential covariates for one or both of the outcomes, conditional association measures can quantify the strength of association without the disturbance of the marginal covariate effects, to provide cleaner and less-confounded insights into the bivariate association. In this work, we propose estimation and inferential procedures for assessing the conditional Kendall's tau coefficient given the covariates, by adopting the quantile regression and quantile copula framework to handle marginal covariate effects...
May 9, 2019: Statistics in Medicine
Svenja E Seide, Katrin Jensen, Meinhard Kieser
The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data-generating models (DGMs) are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case, we propose a framework for the simulation of random-effect network meta-analyses including multiarm trials with binary outcome. The only one of the common DGMs used in the pairwise case, which is directly applicable to a random-effects network setting uses strongly restrictive assumptions...
May 9, 2019: Statistics in Medicine
Byron J Gajewski, Caitlyn Meinzer, Scott M Berry, Gaylan L Rockswold, William G Barsan, Frederick K Korley, Renee' H Martin
A primary goal of a phase II dose-ranging trial is to identify a correct dose before moving forward to a phase III confirmatory trial. A correct dose is one that is actually better than control. A popular model in phase II is an independent model that puts no structure on the dose-response relationship. Unfortunately, the independent model does not efficiently use information from related doses. One very successful alternate model improves power using a pre-specified dose-response structure. Past research indicates that EMAX models are broadly successful and therefore attractive for designing dose-response trials...
May 9, 2019: Statistics in Medicine
Thu-Lan Kelly, Nicole Pratt
Girardeau, Ravaud and Donner in 2008 presented a formula for sample size calculations for cluster randomised crossover trials, when the intracluster correlation coefficient, interperiod correlation coefficient and mean cluster size are specified in advance. However, in many randomised trials, the number of clusters is constrained in some way, but the mean cluster size is not. We present a version of the Girardeau formula for sample size calculations for cluster randomised crossover trials when the number of clusters is fixed...
May 8, 2019: Statistics in Medicine
Jialiang Li, Mu Yue, Wenyang Zhang
In the clinical trial community, it is usually not easy to find a treatment that benefits all patients since the reaction to treatment may differ substantially across different patient subgroups. The heterogeneity of treatment effect plays an essential role in personalized medicine. To facilitate the development of tailored therapies and improve the treatment efficacy, it is important to identify subgroups that exhibit different treatment effects. We consider a very general framework for subgroup identification via the homogeneity pursuit methods usually employed in econometric time series analysis...
May 7, 2019: Statistics in Medicine
Marius Placzek, Tim Friede
Adaptive enrichment designs offer an efficient and flexible way to demonstrate the efficacy of a treatment in a clinically defined full population or in, eg, biomarker-defined subpopulations while controlling the family-wise Type I error rate in the strong sense. Frequently used testing strategies in designs with two or more stages include the combination test and the conditional error function approach. Here, we focus on the latter and present some extensions. In contrast to previous work, we allow for multiple subgroups rather than one subgroup only...
May 7, 2019: Statistics in Medicine
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