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Statistics in Medicine

Birgit I Witte, Johannes Berkhof, Marianne A Jonker
In screening and surveillance studies, event times are interval censored. Besides, screening tests are imperfect so that the interval at which an event takes place may be uncertain. We describe an expectation-maximization algorithm to find the nonparametric maximum likelihood estimator of the cumulative incidence function of an event based on screening test data. Our algorithm has a closed-form solution for the combined expectation and maximization step and is computationally undemanding. A simulation study indicated that the bias of the estimator tends to zero for large sample size, and its mean squared error is in general lower than the mean squared error of the estimator that assumes the screening test is perfect...
June 20, 2017: Statistics in Medicine
Olga V Demler, Michael J Pencina, Nancy R Cook, Ralph B D'Agostino
The change in area under the curve (∆AUC), the integrated discrimination improvement (IDI), and net reclassification index (NRI) are commonly used measures of risk prediction model performance. Some authors have reported good validity of associated methods of estimating their standard errors (SE) and construction of confidence intervals, whereas others have questioned their performance. To address these issues, we unite the ∆AUC, IDI, and three versions of the NRI under the umbrella of the U-statistics family...
June 19, 2017: Statistics in Medicine
Roseanne McNamee
It is often assumed that randomisation will prevent bias in estimation of treatment effects from clinical trials, but this is not true of the semiparametric Proportional Hazards model for survival data when there is underlying risk heterogeneity. Here, a new formula is proposed for estimation of this bias, improving on a previous formula through ease of use and clarity regarding the role of the mid-study cumulative hazard rate, shown to be an important factor for the bias magnitude. Informative censoring (IC) is recognised as a source of bias...
June 15, 2017: Statistics in Medicine
Brian H Willis, Richard D Riley
An important question for clinicians appraising a meta-analysis is: are the findings likely to be valid in their own practice-does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity-where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple ('leave-one-out') cross-validation technique, we demonstrate how we may test meta-analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution...
June 15, 2017: Statistics in Medicine
Jiehuan Sun, Jose D Herazo-Maya, Naftali Kaminski, Hongyu Zhao, Joshua L Warren
Subgroup identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to define subgroups. Longitudinal gene expression profiles might provide additional information on disease progression than what is captured by baseline profiles alone. Therefore, subgroup identification could be more accurate and effective with the aid of longitudinal gene expression data. However, existing statistical methods are unable to fully utilize these data for patient clustering...
June 15, 2017: Statistics in Medicine
Matthias Brückner, Andrew Titman, Thomas Jaki
We consider estimation of treatment effects in two-stage adaptive multi-arm trials with a common control. The best treatment is selected at interim, and the primary endpoint is modeled via a Cox proportional hazards model. The maximum partial-likelihood estimator of the log hazard ratio of the selected treatment will overestimate the true treatment effect in this case. Several methods for reducing the selection bias have been proposed for normal endpoints, including an iterative method based on the estimated conditional selection biases and a shrinkage approach based on empirical Bayes theory...
June 13, 2017: Statistics in Medicine
Kerrie P Nelson, Aya A Mitani, Don Edwards
Widespread inconsistencies are commonly observed between physicians' ordinal classifications in screening tests results such as mammography. These discrepancies have motivated large-scale agreement studies where many raters contribute ratings. The primary goal of these studies is to identify factors related to physicians and patients' test results, which may lead to stronger consistency between raters' classifications. While ordered categorical scales are frequently used to classify screening test results, very few statistical approaches exist to model agreement between multiple raters...
June 13, 2017: Statistics in Medicine
Tobias Mütze, Tim Friede
In this article, we study blinded sample size re-estimation in the 'gold standard' design with internal pilot study for normally distributed outcomes. The 'gold standard' design is a three-arm clinical trial design that includes an active and a placebo control in addition to an experimental treatment. We focus on the absolute margin approach to hypothesis testing in three-arm trials at which the non-inferiority of the experimental treatment and the assay sensitivity are assessed by pairwise comparisons. We compare several blinded sample size re-estimation procedures in a simulation study assessing operating characteristics including power and type I error...
June 12, 2017: Statistics in Medicine
Chrisovalantis Malesios, Nikolaos Demiris, Konstantinos Kalogeropoulos, Ioannis Ntzoufras
Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein-Uhlenbeck process formulation...
June 12, 2017: Statistics in Medicine
Giorgos Bakoyannis, Menggang Yu, Constantin T Yiannoutsos
Many biomedical and clinical studies with time-to-event outcomes involve competing risks data. These data are frequently subject to interval censoring. This means that the failure time is not precisely observed but is only known to lie between two observation times such as clinical visits in a cohort study. Not taking into account the interval censoring may result in biased estimation of the cause-specific cumulative incidence function, an important quantity in the competing risks framework, used for evaluating interventions in populations, for studying the prognosis of various diseases, and for prediction and implementation science purposes...
June 12, 2017: Statistics in Medicine
Kengo Nagashima, Yasunori Sato
In the estimation of Cox regression models, maximum partial likelihood estimates might be infinite in a monotone likelihood setting, where partial likelihood converges to a finite value and parameter estimates converge to infinite values. To address monotone likelihood, previous studies have applied Firth's bias correction method to Cox regression models. However, while the model selection criteria for Firth's penalized partial likelihood approach have not yet been studied, a heuristic AIC-type information criterion can be used in a statistical package...
June 12, 2017: Statistics in Medicine
Stefan Wellek
In clinical trials using lifetime as primary outcome variable, it is more the rule than the exception that even for patients who are failing in the course of the study, survival time does not become known exactly since follow-up takes place according to a restricted schedule with fixed, possibly long intervals between successive visits. In practice, the discreteness of the data obtained under such circumstances is plainly ignored both in data analysis and in sample size planning of survival time studies. As a framework for analyzing the impact of making no difference between continuous and discrete recording of failure times, we use a scenario in which the partially observed times are assigned to the points of the grid of inspection times in the natural way...
June 12, 2017: Statistics in Medicine
Nathan Minois, Valérie Lauwers-Cances, Stéphanie Savy, Michel Attal, Sandrine Andrieu, Vladimir Anisimov, Nicolas Savy
At the design of clinical trial operation, a question of a paramount interest is how long it takes to recruit a given number of patients. Modelling the recruitment dynamics is the necessary step to answer this question. Poisson-gamma model provides very convenient, flexible and realistic approach. This model allows predicting the trial duration using data collected at an interim time with very good accuracy. A natural question arises: how to evaluate the parameters of recruitment model before the trial begins? The question is harder to handle as there are no recruitment data available for this trial...
June 12, 2017: Statistics in Medicine
Jin Yue, Xin Lai, Liu Liu, Paul B S Lai
The timely detection of surgical quality changes is becoming increasingly important. Variable life-adjusted display (VLAD) has a wide range of applications in the medical field. However, the control limits of VLAD are not defined; thus, the charts alone cannot reveal whether surgical quality underwent a significant change. This paper proposes a new risk-adjusted exponentially weighted moving average VLAD (RAEV) chart and provides a control limit that can be used with the VLAD. The RAEV chart is designed to detect shifts in the odds ratios of patients' surgical risk...
June 7, 2017: Statistics in Medicine
Heng Zhou, J Jack Lee, Ying Yuan
We propose a flexible Bayesian optimal phase II (BOP2) design that is capable of handling simple (e.g., binary) and complicated (e.g., ordinal, nested, and co-primary) endpoints under a unified framework. We use a Dirichlet-multinomial model to accommodate different types of endpoints. At each interim, the go/no-go decision is made by evaluating a set of posterior probabilities of the events of interest, which is optimized to maximize power or minimize the number of patients under the null hypothesis. Unlike other existing Bayesian designs, the BOP2 design explicitly controls the type I error rate, thereby bridging the gap between Bayesian designs and frequentist designs...
June 7, 2017: Statistics in Medicine
Philippe Flandre, Reena Deutsch, John O'Quigley
One aspect of an analysis of survival data based on the proportional hazards model that has been receiving increasing attention is that of the predictive ability or explained variation of the model. A number of contending measures have been suggested, including one measure, R(2) (β), which has been proposed given its several desirable properties, including its capacity to accommodate time-dependent covariates, a major feature of the model and one that gives rise to great generality. A thorough study of the properties of available measures, including the aforementioned measure, has been carried out recently...
June 7, 2017: Statistics in Medicine
Chyong-Mei Chen, Pao-Sheng Shen
Interval-censored failure-time data arise when subjects are examined or observed periodically such that the failure time of interest is not examined exactly but only known to be bracketed between two adjacent observation times. The commonly used approaches assume that the examination times and the failure time are independent or conditionally independent given covariates. In many practical applications, patients who are already in poor health or have a weak immune system before treatment usually tend to visit physicians more often after treatment than those with better health or immune system...
June 5, 2017: Statistics in Medicine
James E Barrett
Selective recruitment designs preferentially recruit individuals who are estimated to be statistically informative onto a clinical trial. Individuals who are expected to contribute less information have a lower probability of recruitment. Furthermore, in an information-adaptive design, recruits are allocated to treatment arms in a manner that maximises information gain. The informativeness of an individual depends on their covariate (or biomarker) values, and how information is defined is a critical element of information-adaptive designs...
June 5, 2017: Statistics in Medicine
Per Kragh Andersen
A number of suggested measures of life years lost among patients with a given disease are reviewed, and some new ones are proposed. The methods are all phrased in the framework of a (Markov or non-Markov) illness-death model in combination with a population life table. The methods are illustrated using data on Danish male patients with bipolar disorder, and some recommendations are given. Copyright © 2017 John Wiley & Sons, Ltd.
June 5, 2017: Statistics in Medicine
Hyune-Ju Kim, Jun Luo, Huann-Sheng Chen, Don Green, Dennis Buckman, Jeffrey Byrne, Eric J Feuer
This paper considers an improved confidence interval for the average annual percent change in trend analysis, which is based on a weighted average of the regression slopes in the segmented line regression model with unknown change points. The performance of the improved confidence interval proposed by Muggeo is examined for various distribution settings, and two new methods are proposed for further improvement. The first method is practically equivalent to the one proposed by Muggeo, but its construction is simpler, and it is modified to use the t-distribution instead of the standard normal distribution...
June 5, 2017: Statistics in Medicine
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