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Statistics in Medicine

Rebecca Andridge, Anne-Michelle Noone, Nadia Howlader
Breast cancers are clinically heterogeneous based on tumor markers. The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program provides baseline data on these tumor markers for reporting cancer burden and trends over time in the US general population. These tumor markers, however, are often prone to missing observations. In particular, estrogen receptor (ER) status, a key biomarker in the study of breast cancer, has been collected since 1992 but historically was not well-reported, with missingness rates as high as 25% in early years...
December 5, 2016: Statistics in Medicine
Hongqian Wu, Ying Zhang, Jeffrey D Long
Longitudinal binomial data are frequently generated from multiple questionnaires and assessments in various scientific settings for which the binomial data are often overdispersed. The standard generalized linear mixed effects model may result in severe underestimation of standard errors of estimated regression parameters in such cases and hence potentially bias the statistical inference. In this paper, we propose a longitudinal beta-binomial model for overdispersed binomial data and estimate the regression parameters under a probit model using the generalized estimating equation method...
December 5, 2016: Statistics in Medicine
Ying Liu, Yuanjia Wang, Chaorui Huang, Donglin Zeng
There is an increasing demand for personalization of disease screening based on assessment of patient risk and other characteristics. For example, in breast cancer screening, advanced imaging technologies have made it possible to move away from 'one-size-fits-all' screening guidelines to targeted risk-based screening for those who are in need. Because diagnostic performance of various imaging modalities may vary across subjects, applying the most accurate modality to the patients who would benefit the most requires personalized strategy...
December 4, 2016: Statistics in Medicine
Hisashi Noma, Shiro Tanaka, Shigeyuki Matsui, Andrea Cipriani, Toshi A Furukawa
Network meta-analysis enables comprehensive synthesis of evidence concerning multiple treatments and their simultaneous comparisons based on both direct and indirect evidence. A fundamental pre-requisite of network meta-analysis is the consistency of evidence that is obtained from different sources, particularly whether direct and indirect evidence are in accordance with each other or not, and how they may influence the overall estimates. We have developed an efficient method to quantify indirect evidence, as well as a testing procedure to evaluate their inconsistency using Lindsay's composite likelihood method...
December 4, 2016: Statistics in Medicine
Hairui Hua, Danielle L Burke, Michael J Crowther, Joie Ensor, Catrin Tudur Smith, Richard D Riley
Stratified medicine utilizes individual-level covariates that are associated with a differential treatment effect, also known as treatment-covariate interactions. When multiple trials are available, meta-analysis is used to help detect true treatment-covariate interactions by combining their data. Meta-regression of trial-level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta-analyses are preferable to examine interactions utilizing individual-level information...
December 1, 2016: Statistics in Medicine
Rebecca Walwyn, Chris Roberts
In meta-analyses, where a continuous outcome is measured with different scales or standards, the summary statistic is the mean difference standardised to a common metric with a common variance. Where trial treatment is delivered by a person, nesting of patients within care providers leads to clustering that may interact with, or be limited to, one or more of the arms. Assuming a common standardising variance is less tenable and options for scaling the mean difference become numerous. Metrics suggested for cluster-randomised trials are within, between and total variances and for unequal variances, the control arm or pooled variances...
December 1, 2016: Statistics in Medicine
N Nazeri Rad, J F Lawless
In follow-up studies on chronic disease cohorts, individuals are often observed at irregular visit times that may be related to their previous disease history and other factors. This can produce bias in standard methods of estimation. Working in the context of multistate models, we consider a method of nonparametric estimation for state occupancy probabilities that adjusts for dependent follow-up through the use of inverse-intensity-of-visit weighted estimating functions and smoothing. The methodology is applied to the estimation of viral rebound probabilities in the Canadian Observational Cohort on HIV-positive persons...
November 28, 2016: Statistics in Medicine
Yvonne Vergouwe, Daan Nieboer, Rianne Oostenbrink, Thomas P A Debray, Gordon D Murray, Michael W Kattan, Hendrik Koffijberg, Karel G M Moons, Ewout W Steyerberg
Prediction models fitted with logistic regression often show poor performance when applied in populations other than the development population. Model updating may improve predictions. Previously suggested methods vary in their extensiveness of updating the model. We aim to define a strategy in selecting an appropriate update method that considers the balance between the amount of evidence for updating in the new patient sample and the danger of overfitting. We consider recalibration in the large (re-estimation of model intercept); recalibration (re-estimation of intercept and slope) and model revision (re-estimation of all coefficients) as update methods...
November 28, 2016: Statistics in Medicine
Mohamed Alosh, Mohammad F Huque, Frank Bretz, Ralph B D'Agostino
Clinical trials target patients who are expected to benefit from a new treatment under investigation. However, the magnitude of the treatment benefit, if it exists, often depends on the patient baseline characteristics. It is therefore important to investigate the consistency of the treatment effect across subgroups to ensure a proper interpretation of positive study findings in the overall population. Such assessments can provide guidance on how the treatment should be used. However, great care has to be taken when interpreting consistency results...
November 28, 2016: Statistics in Medicine
Alexina J Mason, Juan Gonzalez-Maffe, Killian Quinn, Nicki Doyle, Ken Legg, Peter Norsworthy, Roy Trevelion, Alan Winston, Deborah Ashby
The design of phase I studies is often challenging, because of limited evidence to inform study protocols. Adaptive designs are now well established in cancer but much less so in other clinical areas. A phase I study to assess the safety, pharmacokinetic profile and antiretroviral efficacy of C34-PEG4 -Chol, a novel peptide fusion inhibitor for the treatment of HIV infection, has been set up with Medical Research Council funding. During the study workup, Bayesian adaptive designs based on the continual reassessment method were compared with a more standard rule-based design, with the aim of choosing a design that would maximise the scientific information gained from the study...
November 27, 2016: Statistics in Medicine
Shanshan Li
Prevalent sampling is frequently a convenient and economical sampling technique for the collection of time-to-event data and thus is commonly used in studies of the natural history of a disease. However, it is biased by design because it tends to recruit individuals with longer survival times. This paper considers estimation of time-dependent receiver operating characteristic curves when data are collected under prevalent sampling. To correct the sampling bias, we develop both nonparametric and semiparametric estimators using extended risk sets and the inverse probability weighting techniques...
November 27, 2016: Statistics in Medicine
Peter C Austin, Philippe Wagner, Juan Merlo
Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster-specific random effects which allow one to partition the total individual variance into between-cluster variation and between-individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients...
November 25, 2016: Statistics in Medicine
George Nicholson, Chris Holmes
Characterizing the technical precision of measurements is a necessary stage in the planning of experiments and in the formal sample size calculation for optimal design. Instruments that measure multiple analytes simultaneously, such as in high-throughput assays arising in biomedical research, pose particular challenges from a statistical perspective. The current most popular method for assessing precision of high-throughput assays is by scatterplotting data from technical replicates. Here, we question the statistical rationale of this approach from both an empirical and theoretical perspective, illustrating our discussion using four example data sets from different genomic platforms...
November 24, 2016: Statistics in Medicine
Stefan Wellek
In current practice, the most frequently applied approach to the handling of ties in the Mann-Whitney-Wilcoxon (MWW) test is based on the conditional distribution of the sum of mid-ranks, given the observed pattern of ties. Starting from this conditional version of the testing procedure, a sample size formula was derived and investigated by Zhao et al. (Stat Med 2008). In contrast, the approach we pursue here is a nonconditional one exploiting explicit representations for the variances of and the covariance between the two U-statistics estimators involved in the Mann-Whitney form of the test statistic...
November 22, 2016: Statistics in Medicine
Junho Lee, Ronald E Gangnon, Jun Zhu
Popular approaches to spatial cluster detection, such as the spatial scan statistic, are defined in terms of the responses. Here, we consider a varying-coefficient regression and spatial clusters in the regression coefficients. For varying-coefficient regression, such as the geographically weighted regression, different regression coefficients are obtained for different spatial units. It is often of interest to the practitioners to identify clusters of spatial units with distinct patterns in a regression coefficient, but there is no formal statistical methodology for that...
November 22, 2016: Statistics in Medicine
Songshan Yang, James A Cranford, Jennifer M Jester, Runze Li, Robert A Zucker, Anne Buu
This study proposes a time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes. The motivating example demonstrates that this zero-inflated Poisson model allows investigators to study group differences in different aspects of substance use (e.g., the probability of abstinence and the quantity of alcohol use) simultaneously. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size increases; the accuracy under equal group sizes is only higher when the sample size is small (100)...
November 21, 2016: Statistics in Medicine
Fei Chen, Gang Li, K K Gordon Lan
Even though consistency is an important issue in multi-regional clinical trials and inconsistency is often anticipated, solutions for handling inconsistency are rare. If a region's treatment effects are inconsistent with that of the other regions, pooling all the regions to estimate the overall treatment effect may not be reasonable. Unlike the multiple center clinical trials conducted in the USA and Europe, in multi-regional clinical trials, different regional regulatory agencies may have their own ways to interpret data and approve new drugs...
November 21, 2016: Statistics in Medicine
Fei Wan
The proportional hazard model is one of the most important statistical models used in medical research involving time-to-event data. Simulation studies are routinely used to evaluate the performance and properties of the model and other alternative statistical models for time-to-event outcomes under a variety of situations. Complex simulations that examine multiple situations with different censoring rates demand approaches that can accommodate this variety. In this paper, we propose a general framework for simulating right-censored survival data for proportional hazards models by simultaneously incorporating a baseline hazard function from a known survival distribution, a known censoring time distribution, and a set of baseline covariates...
November 21, 2016: Statistics in Medicine
Björn Holzhauer
Meta-analyses of clinical trials often treat the number of patients experiencing a medical event as binomially distributed when individual patient data for fitting standard time-to-event models are unavailable. Assuming identical drop-out time distributions across arms, random censorship, and low proportions of patients with an event, a binomial approach results in a valid test of the null hypothesis of no treatment effect with minimal loss in efficiency compared with time-to-event methods. To deal with differences in follow-up-at the cost of assuming specific distributions for event and drop-out times-we propose a hierarchical multivariate meta-analysis model using the aggregate data likelihood based on the number of cases, fatal cases, and discontinuations in each group, as well as the planned trial duration and groups sizes...
November 18, 2016: Statistics in Medicine
Haijin He, Jingheng Cai, Xinyuan Song, Liuquan Sun
End-stage renal disease (ESRD) is one of the most serious diabetes complications. Numerous studies have been devoted to revealing the risk factors of the onset time of ESRD. In this article, we propose a proportional mean residual life (MRL) model with latent variables to assess the effects of observed and latent risk factors on the MRL function of ESRD in a cohort of Chinese type 2 diabetic patients. The proposed model generalizes the conventional proportional MRL model to accommodate the latent risk factor that cannot be measured by a single observed variable...
November 18, 2016: Statistics in Medicine
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