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Statistical Methods in Medical Research

Ilona Wm Verburg, Rebecca Holman, Niels Peek, Ameen Abu-Hanna, Nicolette F de Keizer
Funnel plots are graphical tools to assess and compare clinical performance of a group of care professionals or care institutions on a quality indicator against a benchmark. Incorrect construction of funnel plots may lead to erroneous assessment and incorrect decisions potentially with severe consequences. We provide workflow-based guidance for data analysts on constructing funnel plots for the evaluation of binary quality indicators, expressed as proportions, risk-adjusted rates or standardised rates. Our guidelines assume the following steps: (1) defining policy level input; (2) checking the quality of models used for case-mix correction; (3) examining whether the number of observations per hospital is sufficient; (4) testing for overdispersion of the values of the quality indicator; (5) testing whether the values of quality indicators are associated with institutional characteristics; and (6) specifying how the funnel plot should be constructed...
January 1, 2017: Statistical Methods in Medical Research
Joost van Rosmalen, David Dejardin, Yvette van Norden, Bob Löwenberg, Emmanuel Lesaffre
Data of previous trials with a similar setting are often available in the analysis of clinical trials. Several Bayesian methods have been proposed for including historical data as prior information in the analysis of the current trial, such as the (modified) power prior, the (robust) meta-analytic-predictive prior, the commensurate prior and methods proposed by Pocock and Murray et al. We compared these methods and illustrated their use in a practical setting, including an assessment of the comparability of the current and the historical data...
January 1, 2017: Statistical Methods in Medical Research
Jialiang Li, Qunqiang Feng, Jason P Fine, Michael J Pencina, Ben Van Calster
Polytomous discrimination index is a novel and important diagnostic accuracy measure for multi-category classification. After reconstructing its probabilistic definition, we propose a nonparametric approach to the estimation of polytomous discrimination index based on an empirical sample of biomarker values. In this paper, we provide the finite-sample and asymptotic properties of the proposed estimators and such analytic results may facilitate the statistical inference. Simulation studies are performed to examine the performance of the nonparametric estimators...
January 1, 2017: Statistical Methods in Medical Research
Scott W Olesen, Thomas Gurry, Eric J Alm
Fecal microbiota transplantation is a highly effective intervention for patients suffering from recurrent Clostridium difficile, a common hospital-acquired infection. Fecal microbiota transplantation's success as a therapy for C. difficile has inspired interest in performing clinical trials that experiment with fecal microbiota transplantation as a therapy for other conditions like inflammatory bowel disease, obesity, diabetes, and Parkinson's disease. Results from clinical trials that use fecal microbiota transplantation to treat inflammatory bowel disease suggest that, for at least one condition beyond C...
January 1, 2017: Statistical Methods in Medical Research
Sarah C Emerson, Sushrut S Waikar, Claudio Fuentes, Joseph V Bonventre, Rebecca A Betensky
Motivated by the goal of evaluating a biomarker for acute kidney injury, we consider the problem of assessing operating characteristics for a new biomarker when a true gold standard for disease status is unavailable. In this case, the biomarker is typically compared to another imperfect reference test, and this comparison is used to estimate the performance of the new biomarker. However, errors made by the reference test can bias assessment of the new test. Analysis methods like latent class analysis have been proposed to address this issue, generally employing some strong and unverifiable assumptions regarding the relationship between the new biomarker and the reference test...
January 1, 2017: Statistical Methods in Medical Research
Zhuokai Li, Hai Liu, Wanzhu Tu
Variable selection in semiparametric mixed models for longitudinal data remains a challenge, especially in the presence of multiple correlated outcomes. In this paper, we propose a model selection procedure that simultaneously selects fixed and random effects using a maximum penalized likelihood method with the adaptive least absolute shrinkage and selection operator penalty. Through random effects selection, we determine the correlation structure among multiple outcomes and therefore address whether a joint model is necessary...
January 1, 2017: Statistical Methods in Medical Research
Richard D Riley, Joie Ensor, Dan Jackson, Danielle L Burke
Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest...
January 1, 2017: Statistical Methods in Medical Research
Yolanda Hagar, James J Dignam, Vanja Dukic
The effects of predictors on time to failure may be difficult to assess in cancer studies with longer follow-up, as the commonly used assumption of proportionality of hazards holding over an extended period is often questionable. Motivated by a long-term prostate cancer clinical trial, we contrast and compare four powerful methods for estimation of the hazard rate. These four methods allow for varying degrees of smoothness as well as covariates with effects that vary over time. We pay particular attention to an extended multiresolution hazard estimator, which is a flexible, semi-parametric, Bayesian method for joint estimation of predictor effects and the hazard rate...
January 1, 2017: Statistical Methods in Medical Research
Bo Zhang, Wei Liu, Yingyao Hu
Conditional two-part random-effects models have been proposed for the analysis of healthcare cost panel data that contain both zero costs from the non-users of healthcare facilities and positive costs from the users. These models have been extended to accommodate more flexible data structures when using the generalized Gamma distribution to model the positive healthcare expenditures. However, a major drawback with the extended model, which is inherited from the conditional models, is that it is fairly difficult to make direct marginal inference with respect to overall healthcare costs that includes both zeros and non-zeros, or even on positive healthcare costs...
January 1, 2017: Statistical Methods in Medical Research
Xiaosun Lu, Yangxin Huang, Jiaqing Chen, Rong Zhou, Shuli Yu, Ping Yin
In medical studies, heterogeneous- and skewed-longitudinal data with mis-measured covariates are often observed together with a clinically important binary outcome. A finite mixture of joint models is currently used to fit heterogeneous-longitudinal data and binary outcome, in which these two parts are connected by the individual latent class membership. The skew distributions, such as skew-normal and skew-t, have shown beneficial in dealing with asymmetric data in various applications in literature. However, there has been relatively few studies concerning joint modeling of heterogeneous- and skewed-longitudinal data and a binary outcome...
January 1, 2017: Statistical Methods in Medical Research
Jian Wang, Sanjay Shete
A mediation model explores the direct and indirect effects of an initial variable ( X) on an outcome variable ( Y) by including a mediator ( M). In many realistic scenarios, investigators observe censored data instead of the complete data. Current research in mediation analysis for censored data focuses mainly on censored outcomes, but not censored mediators. In this study, we proposed a strategy based on the accelerated failure time model and a multiple imputation approach. We adapted a measure of the indirect effect for the mediation model with a censored mediator, which can assess the indirect effect at both the group and individual levels...
January 1, 2017: Statistical Methods in Medical Research
Edilberto Cepeda-Cuervo, Michel Córdoba, Vicente Núñez-Antón
This paper proposes alternative models for the analysis of count data featuring a given spatial structure, which corresponds to geographical areas. We assume that the overdispersion data structure partially results from the existing and well justified spatial correlation between geographical adjacent regions, so an extension of existing overdispersion models that include spatial neighborhood structures within a Bayesian framework is proposed. These models allow practitioners to quantify the association explained by the considered neighborhood structures and the one modelled by additional factors...
January 1, 2017: Statistical Methods in Medical Research
Zhuozhao Zhan, Geertruida H de Bock, Edwin R van den Heuvel
Clinical trials may apply or use a sequential introduction of a new treatment to determine its efficacy or effectiveness with respect to a control treatment. The reasons for choosing a particular switch design have different origins. For instance, they may be implemented for ethical or logistic reasons or for studying disease-modifying effects. Large-scale pragmatic trials with complex interventions often use stepped wedge designs (SWDs), where all participants start at the control group, and during the trial, the control treatment is switched to the new intervention at different moments...
January 1, 2017: Statistical Methods in Medical Research
Christopher E Davies, Lynne C Giles, Gary Fv Glonek
One purpose of a longitudinal study is to gain insight of how characteristics at earlier points in time can impact on subsequent outcomes. Typically, the outcome variable varies over time and the data for each individual can be used to form a discrete path of measurements, that is a trajectory. Group-based trajectory modelling methods seek to identify subgroups of individuals within a population with trajectories that are more similar to each other than to trajectories in distinct groups. An approach to modelling the influence of covariates measured at earlier time points in the group-based setting is to consider models wherein these covariates affect the group membership probabilities...
January 1, 2017: Statistical Methods in Medical Research
Orlando Yesid Esparza Albarracin, Airlane Pereira Alencar, Linda Lee Ho
Cumulative sum control charts have been used for health surveillance due to its efficiency to detect soon small shifts in the monitored series. However, these charts may fail when data are autocorrelated. An alternative procedure is to build a control chart based on the residuals after fitting autoregressive moving average models, but these models usually assume Gaussian distribution for the residuals. In practical health surveillance, count series can be modeled by Poisson or Negative Binomial regression, this last to control overdispersion...
January 1, 2017: Statistical Methods in Medical Research
Zhining Wang, Hua Jin, Hezhi Lu, Yaolan Jin
Non-inferiority of one treatment to another based on odds ratio for the matched-pair design is a common issue in the medical research. Liu et al. derived two asymptotic tests, delta method and score test, which can be applicable for large samples but may tend to be liberal for small sample sizes. Jin et al. proposed an IM-based method that can control the type I risk well but may be somewhat conservative. In this paper, we extend the IM-based method to RIM-based test using the randomized plausibility function...
January 1, 2017: Statistical Methods in Medical Research
Kanta Naito, Shouta Shimizu, Jun Udagawa, Hiroki Otani
A new nonlinear multivariate regression method called the LMSR method is proposed, by which a multidimensional understanding for the development process of human fetuses can be provided. Statistically important quantities such as median, skewness, coefficient of variation, and correlation of underlying structure can be described by corresponding smooth curves. Those curves can be obtained by a fine combination of a multivariate power transformation of data and penalized likelihood. It will be shown that the LMSR method and some associated tools are clearly efficient in analyzing development process of human fetuses...
January 1, 2017: Statistical Methods in Medical Research
Takeshi Emura, Masahiro Nakatochi, Shigeyuki Matsui, Hirofumi Michimae, Virginie Rondeau
Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data...
January 1, 2017: Statistical Methods in Medical Research
Wei Jiang, Weichuan Yu
In genome-wide association studies, we normally discover associations between genetic variants and diseases/traits in primary studies, and validate the findings in replication studies. We consider the associations identified in both primary and replication studies as true findings. An important question under this two-stage setting is how to determine significance levels in both studies. In traditional methods, significance levels of the primary and replication studies are determined separately. We argue that the separate determination strategy reduces the power in the overall two-stage study...
January 1, 2017: Statistical Methods in Medical Research
Geert Verbeke, Steffen Fieuws, Geert Molenberghs, Marie Davidian
No abstract text is available yet for this article.
February 2017: Statistical Methods in Medical Research
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