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Statistical Methods in Medical Research

Patrick Taffé, Mingkai Peng, Victoria Stagg, Tyler Williamson
Bland and Altman's limits of agreement have been used in many clinical research settings to assess agreement between two methods of measuring a quantitative trait. However, when the variances of the measurement errors of the two methods are different, limits of agreement can be misleading. MethodCompare is an R package that implements a new statistical methodology, developed by Taffé in 2016. MethodCompare produces three new plots, the "bias plot", the "precision plot", and the "comparison plot" to visually evaluate the performance of the new measurement method against the reference method...
January 1, 2018: Statistical Methods in Medical Research
Chi-Chung Wen, Yi-Hau Chen
Semiparametric transformation models, which include the Cox proportional hazards and proportional odds models as special cases, are popular in current practice of survival analysis owing to that, in contrast to parametric models, no assumption on the baseline distribution is required. Although sample size calculations for semiparametric survival analysis with right-censored data are available, no such calculation exits in literature for semiparametric analysis with current status data, where only an examination time and whether the event occurs prior to the examination are observable...
January 1, 2018: Statistical Methods in Medical Research
Susan K Mikulich-Gilbertson, Brandie D Wagner, Gary K Grunwald, Paula D Riggs, Gary O Zerbe
Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them...
January 1, 2018: Statistical Methods in Medical Research
Elizabeth A Handorf, Daniel F Heitjan, Justin E Bekelman, Nandita Mitra
The analysis of observational data to determine the cost-effectiveness of medical treatments is complicated by the need to account for skewness, censoring, and the effects of measured and unmeasured confounders. We quantify cost-effectiveness as the Net Monetary Benefit (NMB), a linear combination of the treatment effects on cost and effectiveness that denominates utility in monetary terms. We propose a parametric estimation approach that describes cost with a Gamma generalized linear model and survival time (the canonical effectiveness variable) with a Weibull accelerated failure time model...
January 1, 2018: Statistical Methods in Medical Research
Johannes Hertel, Stefan Frenzel, Johanna König, Katharina Wittfeld, Georg Fuellen, Birte Holtfreter, Maik Pietzner, Nele Friedrich, Matthias Nauck, Henry Völzke, Thomas Kocher, Hans J Grabe
For the goal of individualized medicine, it is critical to have clinical phenotypes at hand which represent the individual pathophysiology. However, for most of the utilized phenotypes, two individuals with the same phenotype assignment may differ strongly in their underlying biological traits. In this paper, we propose a definition for individualization and a corresponding statistical operationalization, delivering thereby a statistical framework in which the usefulness of a variable in the meaningful differentiation of individuals with the same phenotype can be assessed...
January 1, 2018: Statistical Methods in Medical Research
K F Arnold, Gth Ellison, S C Gadd, J Textor, Pwg Tennant, A Heppenstall, M S Gilthorpe
'Unexplained residuals' models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework...
January 1, 2018: Statistical Methods in Medical Research
Zach Branson, Marie-Abèle Bind
We present a randomization-based inferential framework for experiments characterized by a strongly ignorable assignment mechanism where units have independent probabilities of receiving treatment. Previous works on randomization tests often assume these probabilities are equal within blocks of units. We consider the general case where they differ across units and show how to perform randomization tests and obtain point estimates and confidence intervals. Furthermore, we develop rejection-sampling and importance-sampling approaches for conducting randomization-based inference conditional on any statistic of interest, such as the number of treated units or forms of covariate balance...
January 1, 2018: Statistical Methods in Medical Research
Valerie A Smith, John S Preisser
Semicontinuous data, characterized by a point mass at zero followed by a positive, continuous distribution, arise frequently in medical research. These data are typically analyzed using two-part mixtures that separately model the probability of incurring a positive outcome and the distribution of positive values among those who incur them. In such a conditional specification, however, standard two-part models do not provide a marginal interpretation of covariate effects on the overall population. We have previously proposed a marginalized two-part model that yields more interpretable effect estimates by parameterizing the model in terms of the marginal mean...
January 1, 2018: Statistical Methods in Medical Research
Faisal M Zahid, Christian Heumann
Missing data is a common issue that can cause problems in estimation and inference in biomedical, epidemiological and social research. Multiple imputation is an increasingly popular approach for handling missing data. In case of a large number of covariates with missing data, existing multiple imputation software packages may not work properly and often produce errors. We propose a multiple imputation algorithm called mispr based on sequential penalized regression models. Each variable with missing values is assumed to have a different distributional form and is imputed with its own imputation model using the ridge penalty...
January 1, 2018: Statistical Methods in Medical Research
Rohana J Karunamuni, Linglong Kong, Wei Tu
We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function...
January 1, 2018: Statistical Methods in Medical Research
Jing Zhang, Chia-Wen Ko, Lei Nie, Yong Chen, Ram Tiwari
Meta-analysis of interventions usually relies on randomized controlled trials. However, when the dominant source of information comes from single-arm studies, or when the results from randomized controlled trials lack generalization due to strict inclusion and exclusion criteria, it is vital to synthesize both sources of evidence. One challenge of synthesizing both sources is that single-arm studies are usually less reliable than randomized controlled trials due to selection bias and confounding factors. In this paper, we propose a Bayesian hierarchical framework for the purpose of bias reduction and efficiency gain...
January 1, 2018: Statistical Methods in Medical Research
Peter C Austin
Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures (e.g., active treatment vs. control). The generalized propensity score is an extension of the propensity score for use with quantitative exposures (e.g., dose or quantity of medication, income, years of education). A crucial component of any propensity score analysis is that of balance assessment. This entails assessing the degree to which conditioning on the propensity score (via matching, weighting, or stratification) has balanced measured baseline covariates between exposure groups...
January 1, 2018: Statistical Methods in Medical Research
Xiao Lin, Ruosha Li, Fangrong Yan, Tao Lu, Xuelin Huang
Optimal therapeutic decisions can be made according to disease prognosis, where the residual lifetime is extensively used because of its straightforward interpretation and formula. To predict the residual lifetime in a dynamic manner, a longitudinal biomarker that is repeatedly measured during the post-baseline follow-up period should be included. In this article, we use functional principal component analysis, a powerful and flexible tool, to handle irregularly measured longitudinal data and extract the dominant features over a specific time interval...
January 1, 2018: Statistical Methods in Medical Research
Yu Shen, Wenli Dong, Roman Gulati, Marc D Ryser, Ruth Etzioni
Cancer screening can detect cancer that would not have been detected in a patient's lifetime without screening. Standard methods for analyzing screening data do not explicitly account for the possibility that a fraction of tumors may remain latent indefinitely. We extend these methods by representing cancers as a mixture of those that progress to symptoms (progressive) and those that remain latent (indolent). Given sensitivity of the screening test, we derive likelihood expressions to simultaneously estimate (1) the rate of onset of preclinical cancer, (2) the average preclinical duration of progressive cancers, and (3) the fraction of preclinical cancers that are indolent...
January 1, 2018: Statistical Methods in Medical Research
Yingdong Feng, Lili Tian
In the field of diagnostic studies for tree or umbrella ordering, under which the marker measurement for one class is lower or higher than those for the rest unordered classes, there exist a few diagnostic measures such as the naive AUC ( NAUC), the umbrella volume ( UV), and the recently proposed TAUC, i.e. area under a ROC curve for tree or umbrella ordering (TROC). However, an important characteristic about tree or umbrella ordering has been neglected. This paper mainly focuses on promoting the use of the integrated false negative rate under tree ordering ( ITFNR) as an additional diagnostic measure besides TAUC, and proposing the idea of using ( TAUC, ITFNR) instead of TAUC to evaluate the diagnostic accuracy of a biomarker under tree or umbrella ordering...
January 1, 2018: Statistical Methods in Medical Research
Hennadii Madan, Franjo Pernuš, Žiga Špiclin
We present a computational framework to select the most accurate and precise method of measurement of a certain quantity, when there is no access to the true value of the measurand. A typical use case is when several image analysis methods are applied to measure the value of a particular quantitative imaging biomarker from the same images. The accuracy of each measurement method is characterized by systematic error (bias), which is modeled as a polynomial in true values of measurand, and the precision as random error modeled with a Gaussian random variable...
January 1, 2018: Statistical Methods in Medical Research
Yoonsuh Jung, Hong Zhang, Jianhua Hu
High-dimensional data are often encountered in biomedical, environmental, and other studies. For example, in biomedical studies that involve high-throughput omic data, an important problem is to search for genetic variables that are predictive of a particular phenotype. A conventional solution is to characterize such relationships through regression models in which a phenotype is treated as the response variable and the variables are treated as covariates; this approach becomes particularly challenging when the number of variables exceeds the number of samples...
January 1, 2018: Statistical Methods in Medical Research
Laine E Thomas, Phillip J Schulte
Improving the quality of care that patients receive is a major focus of clinical research, particularly in the setting of cardiovascular hospitalization. Quality improvement studies seek to estimate and visualize the degree of variability in dichotomous treatment patterns and outcomes across different providers, whereby naive techniques either over-estimate or under-estimate the actual degree of variation. Various statistical methods have been proposed for similar applications including (1) the Gaussian hierarchical model, (2) the semi-parametric Bayesian hierarchical model with a Dirichlet process prior and (3) the non-parametric empirical Bayes approach of smoothing by roughening...
January 1, 2018: Statistical Methods in Medical Research
Chieh Chiang, Chin-Fu Hsiao
Multiregional clinical trials have been accepted in recent years as a useful means of accelerating the development of new drugs and abridging their approval time. The statistical properties of multiregional clinical trials are being widely discussed. In practice, variance of a continuous response may be different from region to region, but it leads to the assessment of the efficacy response falling into a Behrens-Fisher problem-there is no exact testing or interval estimator for mean difference with unequal variances...
January 1, 2018: Statistical Methods in Medical Research
Anqi Zhu, Donglin Zeng, Pengyue Zhang, Lang Li
One important goal in pharmaco-epidemiology studies is to understand the causal relationship between drug exposures and their clinical outcomes, including adverse drug events. In order to achieve this goal, however, we need to resolve several challenges. Most of pharmaco-epidemiology data are observational and confounding is largely present due to many co-medications. The pharmaco-epidemiology study data set is often sampled from large medical record databases using a matched case-control design, and it may not be representative of the original patient population in the medical record databases...
January 1, 2018: Statistical Methods in Medical Research
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