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Statistics in Medicine

Giuliana Cortese, Stine A Holmboe, Thomas H Scheike
The hazard ratios resulting from a Cox's regression hazards model are hard to interpret and to be converted into prolonged survival time. As the main goal is often to study survival functions, there is increasing interest in summary measures based on the survival function that are easier to interpret than the hazard ratio; the residual mean time is an important example of those measures. However, because of the presence of right censoring, the tail of the survival distribution is often difficult to estimate correctly...
January 20, 2017: Statistics in Medicine
Luis G León Novelo, Andrew Womack, Hongxiao Zhu, Xiaowei Wu
This paper addresses model-based Bayesian inference in the analysis of data arising from bioassay experiments. In such experiments, increasing doses of a chemical substance are given to treatment groups (usually rats or mice) for a fixed period of time (usually 2 years). The goal of such an experiment is to determine whether an increased dosage of the chemical is associated with increased probability of an adverse effect (usually presence of adenoma or carcinoma). The data consists of dosage, survival time, and the occurrence of the adverse event for each unit in the study...
January 20, 2017: Statistics in Medicine
Miao-Yu Tsai
The concordance correlation coefficient (CCC) is a commonly accepted measure of agreement between two observers for continuous responses. This paper proposes a generalized estimating equations (GEE) approach allowing dependency between repeated measurements over time to assess intra-agreement for each observer and inter- and total agreement among multiple observers simultaneously. Furthermore, the indices of intra-, inter-, and total agreement through variance components (VC) from an extended three-way linear mixed model (LMM) are also developed with consideration of the correlation structure of longitudinal repeated measurements...
January 20, 2017: Statistics in Medicine
Peter C Austin, Jason P Fine
In studies with survival or time-to-event outcomes, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Specialized statistical methods must be used to analyze survival data in the presence of competing risks. We conducted a review of randomized controlled trials with survival outcomes that were published in high-impact general medical journals. Of 40 studies that we identified, 31 (77.5%) were potentially susceptible to competing risks. However, in the majority of these studies, the potential presence of competing risks was not accounted for in the statistical analyses that were described...
January 19, 2017: Statistics in Medicine
Jenna R Krall, Amber J Hackstadt, Roger D Peng
Exposure to particulate matter (PM) air pollution has been associated with a range of adverse health outcomes, including cardiovascular disease hospitalizations and other clinical parameters. Determining which sources of PM, such as traffic or industry, are most associated with adverse health outcomes could help guide future recommendations aimed at reducing harmful pollution exposure for susceptible individuals. Information obtained from multisite studies, which is generally more precise than information from a single location, is critical to understanding how PM impacts health and to informing local strategies for reducing individual-level PM exposure...
January 18, 2017: Statistics in Medicine
Kristen M Cunanan, Alexia Iasonos, Ronglai Shen, Colin B Begg, Mithat Gönen
The landscape for early phase cancer clinical trials is changing dramatically because of the advent of targeted therapy. Increasingly, new drugs are designed to work against a target such as the presence of a specific tumor mutation. Because typically only a small proportion of cancer patients will possess the mutational target, but the mutation is present in many different cancers, a new class of basket trials is emerging, whereby the drug is tested simultaneously in different baskets, that is, subgroups of different tumor types...
January 18, 2017: Statistics in Medicine
Guanglei Yu, Liang Zhu, Yang Li, Jianguo Sun, Leslie L Robison
Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest...
January 18, 2017: Statistics in Medicine
Gabriela B Cybis, Janet S Sinsheimer, Trevor Bedford, Andrew Rambaut, Philippe Lemey, Marc A Suchard
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods...
January 18, 2017: Statistics in Medicine
Layla Parast, Tianxi Cai, Lu Tian
Given the long follow-up periods that are often required for treatment or intervention studies, the potential to use surrogate markers to decrease the required follow-up time is a very attractive goal. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Currently available methods for identifying and validating surrogate markers tend to rely on restrictive model assumptions and/or focus on uncensored outcomes...
January 15, 2017: Statistics in Medicine
Marvin N Wright, Theresa Dankowski, Andreas Ziegler
The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables...
January 15, 2017: Statistics in Medicine
Lisa L Henn, John Hughes, Eleena Iisakka, Jutta Ellermann, Shabnam Mortazavi, Connor Ziegler, Mikko J Nissi, Patrick Morgan
Femoroacetabular impingement (FAI) is a condition in which subtle deformities of the femoral head and acetabulum (hip socket) result in pathological abutment during hip motion. FAI is a common cause of hip pain and can lead to acetabular cartilage damage and osteoarthritis. For some patients with FAI, surgical intervention is indicated, and it can improve quality of life and potentially delay the onset of osteoarthritis. For other patients, however, surgery is contraindicated because significant cartilage damage has already occurred...
January 15, 2017: Statistics in Medicine
Orestis Efthimiou, Dimitris Mavridis, Thomas P A Debray, Myrto Samara, Mark Belger, George C M Siontis, Stefan Leucht, Georgia Salanti
Non-randomized studies aim to reveal whether or not interventions are effective in real-life clinical practice, and there is a growing interest in including such evidence in the decision-making process. We evaluate existing methodologies and present new approaches to using non-randomized evidence in a network meta-analysis of randomized controlled trials (RCTs) when the aim is to assess relative treatment effects. We first discuss how to assess compatibility between the two types of evidence. We then present and compare an array of alternative methods that allow the inclusion of non-randomized studies in a network meta-analysis of RCTs: the naïve data synthesis, the design-adjusted synthesis, the use of non-randomized evidence as prior information and the use of three-level hierarchical models...
January 12, 2017: Statistics in Medicine
Lisa N Yelland, Thomas R Sullivan, David J Price, Katherine J Lee
Randomised trials including a mixture of independent and paired data arise in many areas of health research, yet methods for determining the sample size for such trials are lacking. We derive design effects algebraically assuming clustering because of paired data will be taken into account in the analysis using generalised estimating equations with either an independence or exchangeable working correlation structure. Continuous and binary outcomes are considered, along with three different methods of randomisation: cluster randomisation, individual randomisation and randomisation to opposite treatment groups...
January 10, 2017: Statistics in Medicine
S Vandenberghe, S Vansteelandt, T Loeys
Analyses of randomised experiments frequently include attempts to decompose the intention-to-treat effect into a direct and indirect effect, mediated by given intermediaries, with the aim to shed light onto the treatment mechanism. Methods from causal mediation analysis have facilitated this by allowing for arbitrary models for the outcome and the mediator. They thereby generalise the traditional approach to direct and indirect effects, which is essentially limited to linear models. The default maximum likelihood methods make use of a model for the conditional distribution of the mediator, given treatment and baseline covariates, but are prone to bias when that model is misspecified...
January 9, 2017: Statistics in Medicine
Chieh Chiang, Chin-Fu Hsiao
In 1992, the US Food and Drug Administration declared that two drugs demonstrate average bioequivalence (ABE) if the log-transformed mean difference of pharmacokinetic responses lies in (-0.223, 0.223). The most widely used approach for assessing ABE is the two one-sided tests procedure. More specifically, ABE is concluded when a 100(1 - 2α) % confidence interval for mean difference falls within (-0.223, 0.223). As known, bioequivalent studies are usually conducted by crossover design. However, in the case that the half-life of a drug is long, a parallel design for the bioequivalent study may be preferred...
January 9, 2017: Statistics in Medicine
An-Min Tang, Nian-Sheng Tang, Hongtu Zhu
The normality assumption of measurement error is a widely used distribution in joint models of longitudinal and survival data, but it may lead to unreasonable or even misleading results when longitudinal data reveal skewness feature. This paper proposes a new joint model for multivariate longitudinal and multivariate survival data by incorporating a nonparametric function into the trajectory function and hazard function and assuming that measurement errors in longitudinal measurement models follow a skew-normal distribution...
January 9, 2017: Statistics in Medicine
Philip M Westgate, Woodrow W Burchett
The analysis of very small samples of Gaussian repeated measurements can be challenging. First, due to a very small number of independent subjects contributing outcomes over time, statistical power can be quite small. Second, nuisance covariance parameters must be appropriately accounted for in the analysis in order to maintain the nominal test size. However, available statistical strategies that ensure valid statistical inference may lack power, whereas more powerful methods may have the potential for inflated test sizes...
January 8, 2017: Statistics in Medicine
Cheng Zheng, Ran Dai, Parameswaran N Hari, Mei-Jie Zhang
In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with competing risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semiparametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for competing risk settings...
January 8, 2017: Statistics in Medicine
Vladimir V Anisimov, Wai Y Yeung, D Stephen Coad
Randomisation schemes are rules that assign patients to treatments in a clinical trial. Many of these schemes have the common aim of maintaining balance in the numbers of patients across treatment groups. The properties of imbalance that have been investigated in the literature are based on two treatment groups. In this paper, their properties for K > 2 treatments are studied for two randomisation schemes: centre-stratified permuted-block and complete randomisation. For both randomisation schemes, analytical approaches are investigated assuming that the patient recruitment process follows a Poisson-gamma model...
December 27, 2016: Statistics in Medicine
K Kunzmann, M Kieser
Clinical trials in phase II of drug development are frequently conducted as single-arm two-stage studies with a binary endpoint. Recently, adaptive designs have been proposed for this setting that enable a midcourse modification of the sample size. While these designs are elaborated with respect to hypothesis testing by assuring control of the type I error rate, the topic of point estimation has up to now not been addressed. For adaptive designs with a prespecified sample size recalculation rule, we propose a new point estimator that both assures compatibility of estimation and test decision and minimizes average mean squared error...
December 27, 2016: Statistics in Medicine
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