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Statistics in Medicine

Tim P Morris, Ian R White, Michael J Crowther
Simulation studies are computer experiments that involve creating data by pseudo-random sampling. A key strength of simulation studies is the ability to understand the behavior of statistical methods because some "truth" (usually some parameter/s of interest) is known from the process of generating the data. This allows us to consider properties of methods, such as bias. While widely used, simulation studies are often poorly designed, analyzed, and reported. This tutorial outlines the rationale for using simulation studies and offers guidance for design, execution, analysis, reporting, and presentation...
January 16, 2019: Statistics in Medicine
Tri-Long Nguyen, Thomas P A Debray
In nonrandomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. While both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Indeed, many treatments are not assigned in a binary fashion and require a certain extent of dosing...
January 16, 2019: Statistics in Medicine
Tianyi Cai, Alan M Zaslavsky
Each year, surveys are conducted to assess the quality of care for Medicare beneficiaries, using instruments from the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) program. Currently, survey measures presented for Fee-for-Service beneficiaries are either pooled at the state level or unpooled for smaller substate areas nested within the state; the choice in each state is based on statistical tests of measure heterogeneity across areas within state. We fit spatial-temporal Bayesian random-effects models using a flexible parameterization to estimate mean scores for each of the domains formed by 94 areas in 32 states measured over 5 years...
January 15, 2019: Statistics in Medicine
Ezra Gayawan, Samson B Adebayo, Elisabeth Waldmann
The average nutritional status of children in Nigeria is, just as in most developing countries, still in an alarmingly bad condition. Prior studies have shown that this status relies on a series of different influences and can be measured by three anthropometric variables for stunting, wasting, and underweight. Different regression modeling techniques have been adopted over the years to explain the determinants and spatial clustering. Those indicators, however, show patterns that are not necessarily full filling requirements for ordinary regression models for the mean and are correlated among each other, a fact that has until now been ignored by most studies...
January 15, 2019: Statistics in Medicine
Marta Bofill Roig, Guadalupe Gómez Melis
Composite binary endpoints are increasingly used as primary endpoints in clinical trials. When designing a trial, it is crucial to determine the appropriate sample size for testing the statistical differences between treatment groups for the primary endpoint. As shown in this work, when using a composite binary endpoint to size a trial, one needs to specify the event rates and the effect sizes of the composite components as well as the correlation between them. In practice, the marginal parameters of the components can be obtained from previous studies or pilot trials; however, the correlation is often not previously reported and thus usually unknown...
January 13, 2019: Statistics in Medicine
Xiaoyan Wang, Yonghong Xu, Shuangge Ma
For many complex diseases, gene-environment (G-E) interactions have independent contributions beyond the main G and E effects. Despite extensive effort, it still remains challenging to identify G-E interactions. With the long accumulation of experiments and data, for many biomedical problems of common interest, there are existing studies that can be relevant and informative for the identification of G-E interactions and/or main effects. In this study, our goal is to identify G-E interactions (as well as their corresponding main G effects) under a joint statistical modeling framework...
January 13, 2019: Statistics in Medicine
Sierra Pugh, Matthew J Heaton, Brian Hartman, Candace Berrett, Chantel Sloan, Amber M Evans, Tebeb Gebretsadik, Pingsheng Wu, Tina V Hartert, Rees L Lee
RSV bronchiolitis (an acute lower respiratory tract viral infection in infants) is the most common cause of infant hospitalizations in the United States (US). The only preventive intervention currently available is monthly injections of immunoprophylaxis. However, this treatment is expensive and needs to be administered simultaneously with seasonal bronchiolitis cycles in order to be effective. To increase our understanding of bronchiolitis timing, this research focuses on identifying seasonal bronchiolitis cycles (start times, peaks, and declinations) throughout the continental US using data on infant bronchiolitis cases from the US Military Health System Data Repository...
January 13, 2019: Statistics in Medicine
Hong Wang, Gang Li
Some interesting recent studies have shown that neural network models are useful alternatives in modeling survival data when the assumptions of a classical parametric or semiparametric survival model such as the Cox (1972) model are seriously violated. However, to the best of our knowledge, the plausibility of adapting the emerging extreme learning machine (ELM) algorithm for single-hidden-layer feedforward neural networks to survival analysis has not been explored. In this paper, we present a kernel ELM Cox model regularized by an L0 -based broken adaptive ridge (BAR) penalization method...
January 10, 2019: Statistics in Medicine
Lauren M Hall, Joshua P French
The cumulative sum (CUSUM) control chart is a method for detecting whether the mean of a time series process has shifted beyond some tolerance (ie, is out of control). Originally developed in an industrial process control setting, the CUSUM statistic is typically reset to zero once a process is discovered to be out of control since the industrial process is then recalibrated to be in control. The CUSUM method is also used to detect disease outbreaks in prospective disease surveillance, with a disease outbreak coinciding with an out-of-control process...
January 9, 2019: Statistics in Medicine
Maricela Cruz, Daniel L Gillen, Miriam Bender, Hernando Ombao
The delivery and assessment of quality health care is complex with many interacting and interdependent components. In terms of research design and statistical analysis, this complexity and interdependency makes it difficult to assess the true impact of interventions designed to improve patient health care outcomes. Interrupted time series (ITS) is a quasi-experimental design developed for inferring the effectiveness of a health policy intervention while accounting for temporal dependence within a single system or unit...
January 7, 2019: Statistics in Medicine
Valentijn M T de Jong, Marinus J C Eijkemans, Ben van Calster, Dirk Timmerman, Karel G M Moons, Ewout W Steyerberg, Maarten van Smeden
Multinomial Logistic Regression (MLR) has been advocated for developing clinical prediction models that distinguish between three or more unordered outcomes. We present a full-factorial simulation study to examine the predictive performance of MLR models in relation to the relative size of outcome categories, number of predictors and the number of events per variable. It is shown that MLR estimated by Maximum Likelihood yields overfitted prediction models in small to medium sized data. In most cases, the calibration and overall predictive performance of the multinomial prediction model is improved by using penalized MLR...
January 6, 2019: Statistics in Medicine
Xuelin Huang, Lei Liu, Jing Ning, Liang Li, Yu Shen
Most studies characterize longitudinal biomarker trajectories by looking forward at them from a commonly used time origin, such as the initial treatment time. For a better understanding of the relationship between biomarkers and disease progression, we propose to align all subjects by using their disease progression time as the origin and then looking backward at the biomarker distributions prior to that event. We demonstrate that such backward-looking plots are much more informative than forward-looking plots when the research goal is to understand the shape of the trajectory leading up to the event of interest...
January 6, 2019: Statistics in Medicine
Jan De Neve, Olivier Thas, Thomas A Gerds
Semiparametric linear transformation models form a versatile class of regression models with the Cox proportional hazards model being the most well-known member. These models are well studied for right censored outcomes and are typically used in survival analysis. We consider transformation models as a tool for situations with uncensored continuous outcomes where linear regression is not appropriate. We introduce the probabilistic index as a uniform effect measure for the class of transformation models. We discuss and compare three estimators using a working Cox regression model: the partial likelihood estimator, an estimator based on binary generalized linear models and one based on probabilistic index model estimating equations...
January 4, 2019: Statistics in Medicine
Kevin He, Valarie B Ashby, Douglas E Schaubel
Center-specific survival outcomes of kidney transplant recipients are an important quality measure, with several challenges. Existing methods based on restricted mean lifetime tend to focus on short- and medium-term clinical outcomes and may fail to capture long-term effects associated with quality of follow-up care. In this report, we propose methods that combine a lognormal frailty model and piecewise exponential baseline rates to compare the mean survival time across centers. The proposed methods allow for the consistent estimation of mean survival time as opposed to restricted mean lifetime and, within this context, permits more accurate profiling of long-term center-specific outcomes...
January 4, 2019: Statistics in Medicine
Di Shu, Grace Y Yi
Inverse probability weighting (IPW) estimation has been widely used in causal inference. Its validity relies on the important condition that the variables are precisely measured. This condition, however, is often violated, which distorts the IPW method and thus yields biased results. In this paper, we study the IPW estimation of average treatment effects for settings with mismeasured covariates and misclassified outcomes. We develop estimation methods to correct for measurement error and misclassification effects simultaneously...
January 4, 2019: Statistics in Medicine
Yizhen Xu, Tao Liu, Michael J Daniels, Rami Kantor, Ann Mwangi, Joseph W Hogan
Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. In some resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers...
January 4, 2019: Statistics in Medicine
Michele Santacatterina, Celia García-Pareja, Rino Bellocco, Anders Sönnerborg, Anna Mia Ekström, Matteo Bottai
Marginal structural Cox models have been used to estimate the causal effect of a time-varying treatment on a survival outcome in the presence of time-dependent confounders. These methods rely on the positivity assumption, which states that the propensity scores are bounded away from zero and one. Practical violations of this assumption are common in longitudinal studies, resulting in extreme weights that may yield erroneous inferences. Truncation, which consists of replacing outlying weights with less extreme ones, is the most common approach to control for extreme weights to date...
December 27, 2018: Statistics in Medicine
Hong Li, Constantine Gatsonis
In this paper, we develop methods to combine multiple biomarker trajectories into a composite diagnostic marker using functional data analysis (FDA) to achieve better diagnostic accuracy in monitoring disease recurrence in the setting of a prospective cohort study. In such studies, the disease status is usually verified only for patients with a positive test result in any biomarker and is missing in patients with negative test results in all biomarkers. Thus, the test result will affect disease verification, which leads to verification bias if the analysis is restricted only to the verified cases...
December 27, 2018: Statistics in Medicine
Naveen N Narisetty, Bhramar Mukherjee, Yin-Hsiu Chen, Richard Gonzalez, John D Meeker
In this paper, we propose a stepwise forward selection algorithm for detecting the effects of a set of correlated exposures and their interactions on a health outcome of interest when the underlying relationship could potentially be nonlinear. Though the proposed method is very general, our application in this paper remains to be on analysis of multiple pollutants and their interactions. Simultaneous exposure to multiple environmental pollutants could affect human health in a multitude of complex ways. For understanding the health effects of multiple environmental exposures, it is often important to identify and estimate complex interactions among exposures...
December 26, 2018: Statistics in Medicine
Shomoita Alam, Erica E M Moodie, David A Stephens
In investigations of the effect of treatment on outcome, the propensity score is a tool to eliminate imbalance in the distribution of confounding variables between treatment groups. Recent work has suggested that Super Learner, an ensemble method, outperforms logistic regression in nonlinear settings; however, experience with real-data analyses tends to show overfitting of the propensity score model using this approach. We investigated a wide range of simulated settings of varying complexities including simulations based on real data to compare the performances of logistic regression, generalized boosted models, and Super Learner in providing balance and for estimating the average treatment effect via propensity score regression, propensity score matching, and inverse probability of treatment weighting...
December 26, 2018: Statistics in Medicine
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