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Statistical Methods in Medical Research

Jaya M Satagopan, Sara H Olson, Robert C Elston
This paper is concerned with the estimation of the logarithm of disease odds (log odds) when evaluating two risk factors, whether or not interactions are present. Statisticians define interaction as a departure from an additive model on a certain scale of measurement of the outcome. Certain interactions, known as removable interactions, may be eliminated by fitting an additive model under an invertible transformation of the outcome. This can potentially provide more precise estimates of log odds than fitting a model with interaction terms...
April 2017: Statistical Methods in Medical Research
Marcelo Azevedo Costa, Thiago de Souza Rodrigues, André Gabriel Fc da Costa, René Natowicz, Antônio Pádua Braga
This work proposes a sequential methodology for selecting variables in classification problems in which the number of predictors is much larger than the sample size. The methodology includes a Monte Carlo permutation procedure that conditionally tests the null hypothesis of no association among the outcomes and the available predictors. In order to improve computing aspects, we propose a new parametric distribution, the Truncated and Zero Inflated Gumbel Distribution. The final application is to find compact classification models with improved performance for genomic data...
April 2017: Statistical Methods in Medical Research
Wei Liu, Bo Zhang, Hui Zhang, Zhiwei Zhang
There is growing interest in assessing immune biomarkers, which are quick to measure and potentially predictive of long-term efficacy, as surrogate endpoints in randomized, placebo-controlled vaccine trials. This can be done under a principal stratification approach, with principal strata defined using a subject's potential immune responses to vaccine and placebo (the latter may be assumed to be zero). In this context, principal surrogacy refers to the extent to which vaccine efficacy varies across principal strata...
April 2017: Statistical Methods in Medical Research
Achmad Efendi, Reza Drikvandi, Geert Verbeke, Geert Molenberghs
In this paper, we develop a simple diagnostic test for the random-effects distribution in mixed models. The test is based on the gradient function, a graphical tool proposed by Verbeke and Molenberghs to check the impact of assumptions about the random-effects distribution in mixed models on inferences. Inference is conducted through the bootstrap. The proposed test is easy to implement and applicable in a general class of mixed models. The operating characteristics of the test are evaluated in a simulation study, and the method is further illustrated using two real data analyses...
April 2017: Statistical Methods in Medical Research
Guo-Liang Tian, Man-Lai Tang, Qin Wu, Yin Liu
Although the item count technique is useful in surveys with sensitive questions, privacy of those respondents who possess the sensitive characteristic of interest may not be well protected due to a defect in its original design. In this article, we propose two new survey designs (namely the Poisson item count technique and negative binomial item count technique) which replace several independent Bernoulli random variables required by the original item count technique with a single Poisson or negative binomial random variable, respectively...
April 2017: Statistical Methods in Medical Research
Arvid Sjölander, Stijn Vansteelandt
The attributable fraction is a commonly used measure that quantifies the public health impact of an exposure on an outcome. It was originally defined for binary outcomes, but an extension has recently been proposed for right-censored survival time outcomes; the so-called attributable fraction function. A maximum likelihood estimator of the attributable fraction function has been developed, which requires a model for the outcome. In this paper, we derive a doubly robust estimator of the attributable fraction function...
April 2017: Statistical Methods in Medical Research
Yong Chen, Yulun Liu, Jing Ning, Lei Nie, Hongjian Zhu, Haitao Chu
Diagnostic systematic review is a vital step in the evaluation of diagnostic technologies. In many applications, it involves pooling pairs of sensitivity and specificity of a dichotomized diagnostic test from multiple studies. We propose a composite likelihood (CL) method for bivariate meta-analysis in diagnostic systematic reviews. This method provides an alternative way to make inference on diagnostic measures such as sensitivity, specificity, likelihood ratios, and diagnostic odds ratio. Its main advantages over the standard likelihood method are the avoidance of the nonconvergence problem, which is nontrivial when the number of studies is relatively small, the computational simplicity, and some robustness to model misspecifications...
April 2017: Statistical Methods in Medical Research
Tanzy Mt Love, Sally W Thurston, Philip W Davidson
The Seychelles Child Development Study is a research project with the objective of examining associations between prenatal exposure to low doses of methylmercury from maternal fish consumption and children's developmental outcomes. Whether methylmercury has neurotoxic effects at low doses remains unclear and recommendations for pregnant women and children to reduce fish intake may prevent a substantial number of people from receiving sufficient nutrients that are abundant in fish. The primary findings of the Seychelles Child Development Study are inconsistent with adverse associations between methylmercury from fish consumption and neurodevelopmental outcomes...
April 2017: Statistical Methods in Medical Research
Dipankar Bandyopadhyay, Diana M Galvis, Victor H Lachos
Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones...
April 2017: Statistical Methods in Medical Research
Zhong Guan, Jing Qin
The receiver operating characteristic curve is commonly used for assessing diagnostic test accuracy and for discriminatory ability of a medical diagnostic test in distinguishing between diseases and non-diseased individuals. With the advance of technology, many genetic variables and biomarker variables are easily collected. The most challenging problem is how to combine clinical, genetic, and biomarker variables together to predict disease status. If one is interested in predicting t-year survival, however, the status of "case" (death) and "control" (survival) at the given t-year is unknown for those individuals who were censored before t-year...
April 2017: Statistical Methods in Medical Research
Michael J Lopez, Roee Gutman
Propensity score methods are common for estimating a binary treatment effect when treatment assignment is not randomized. When exposure is measured on an ordinal scale (i.e. low-medium-high), however, propensity score inference requires extensions which have received limited attention. Estimands of possible interest with an ordinal exposure are the average treatment effects between each pair of exposure levels. Using these estimands, it is possible to determine an optimal exposure level. Traditional methods, including dichotomization of the exposure or a series of binary propensity score comparisons across exposure pairs, are generally inadequate for identification of optimal levels...
April 2017: Statistical Methods in Medical Research
Spencer Lourens, Ying Zhang, Jeffrey D Long, Jane S Paulsen
Executive dysfunction is a deficiency in skills of planning and problem solving that characterizes many neuropsychiatric disorders. The Towers Task is a commonly used measure of planning and problem solving for assessing executive function. Towers Task data are usually zero-inflated and right-censored, and ignoring these features can result in biased inference for the disease characterization of executive dysfunction. In this manuscript, a mixed-effects model for longitudinal censored semicontinuous data is developed for analyzing longitudinal Towers Task data from the PREDICT-HD study...
April 2017: Statistical Methods in Medical Research
Nicholas R Latimer, K R Abrams, P C Lambert, M J Crowther, A J Wailoo, J P Morden, R L Akehurst, M J Campbell
Estimates of the overall survival benefit of new cancer treatments are often confounded by treatment switching in randomised controlled trials (RCTs) - whereby patients randomised to the control group are permitted to switch onto the experimental treatment upon disease progression. In health technology assessment, estimates of the unconfounded overall survival benefit associated with the new treatment are needed. Several switching adjustment methods have been advocated in the literature, some of which have been used in health technology assessment...
April 2017: Statistical Methods in Medical Research
Ilaria Ardoino, Monica Lanzoni, Giuseppe Marano, Patrizia Boracchi, Elisabetta Sagrini, Alice Gianstefani, Fabio Piscaglia, Elia M Biganzoli
The interpretation of regression models results can often benefit from the generation of nomograms, 'user friendly' graphical devices especially useful for assisting the decision-making processes. However, in the case of multinomial regression models, whenever categorical responses with more than two classes are involved, nomograms cannot be drawn in the conventional way. Such a difficulty in managing and interpreting the outcome could often result in a limitation of the use of multinomial regression in decision-making support...
April 2017: Statistical Methods in Medical Research
Zhiwei Zhang, Kyeongmi Cheon
A common problem in randomized clinical trials is nonignorable missingness, namely that the clinical outcome(s) of interest can be missing in a way that is not fully explained by the observed quantities. This happens when the continued participation of patients depends on the current outcome after adjusting for the observed history. Standard methods for handling nonignorable missingness typically require specification of the response mechanism, which can be difficult in practice. This article proposes a reverse regression approach that does not require a model for the response mechanism...
April 2017: Statistical Methods in Medical Research
Edwin R Van den Heuvel, Renée J Zwanenburg, Conny Ma Van Ravenswaaij-Arts
This paper compares the power of the parallel group design, the matched-pairs design, and several options for the stepped wedge and delayed start designs for testing a possible effect of intranasal insulin with respect to placebo on developmental growth of children with a rare disorder like Phelan-McDermid syndrome. A subject-specific linear mixed effects model for the primary outcome developmental age in a longitudinal setting with five time points was assumed. Monte Carlo simulation studies with small sample sizes were applied since the rare disorder prohibits large trials...
April 2017: Statistical Methods in Medical Research
Peter C Austin, Ewout W Steyerberg
We conducted an extensive set of empirical analyses to examine the effect of the number of events per variable (EPV) on the relative performance of three different methods for assessing the predictive accuracy of a logistic regression model: apparent performance in the analysis sample, split-sample validation, and optimism correction using bootstrap methods. Using a single dataset of patients hospitalized with heart failure, we compared the estimates of discriminatory performance from these methods to those for a very large independent validation sample arising from the same population...
April 2017: Statistical Methods in Medical Research
Yi Deng, Xiaoxi Zhang, Qi Long
In multi-regional trials, the underlying overall and region-specific accrual rates often do not hold constant over time and different regions could have different start-up times, which combined with initial jump in accrual within each region often leads to a discontinuous overall accrual rate, and these issues associated with multi-regional trials have not been adequately investigated. In this paper, we clarify the implication of the multi-regional nature on modeling and prediction of accrual in clinical trials and investigate a Bayesian approach for accrual modeling and prediction, which models region-specific accrual using a nonhomogeneous Poisson process and allows the underlying Poisson rate in each region to vary over time...
April 2017: Statistical Methods in Medical Research
Kyoji Furukawa, Dale L Preston, Munechika Misumi, Harry M Cullings
While data are unavoidably missing or incomplete in most observational studies, consequences of mishandling such incompleteness in analysis are often overlooked. When time-varying information is collected irregularly and infrequently over a long period, even precisely obtained data may implicitly involve substantial incompleteness. Motivated by an analysis to quantitatively evaluate the effects of smoking and radiation on lung cancer risks among Japanese atomic-bomb survivors, we provide a unique application of multiple imputation to incompletely observed smoking histories under the assumption of missing at random...
April 2017: Statistical Methods in Medical Research
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