journal
MENU ▼
Read by QxMD icon Read
search

International Journal of Biostatistics

journal
https://www.readbyqxmd.com/read/27930367/multiple-comparisons-using-composite-likelihood-in-clustered-data
#1
Mahdis Azadbakhsh, Xin Gao, Hanna Jankowski
We study the problem of multiple hypothesis testing for correlated clustered data. As the existing multiple comparison procedures based on maximum likelihood estimation could be computationally intensive, we propose to construct multiple comparison procedures based on composite likelihood method. The new test statistics account for the correlation structure within the clusters and are computationally convenient to compute. Simulation studies show that the composite likelihood based procedures maintain good control of the familywise type I error rate in the presence of intra-cluster correlation, whereas ignoring the correlation leads to erratic performance...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27889706/using-relative-statistics-and-approximate-disease-prevalence-to-compare-screening-tests
#2
Frank Samuelson, Craig Abbey
Schatzkin et al. and other authors demonstrated that the ratios of some conditional statistics such as the true positive fraction are equal to the ratios of unconditional statistics, such as disease detection rates, and therefore we can calculate these ratios between two screening tests on the same population even if negative test patients are not followed with a reference procedure and the true and false negative rates are unknown. We demonstrate that this same property applies to an expected utility metric...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27889705/effect-estimation-in-point-exposure-studies-with-binary-outcomes-and-high-dimensional-covariate-data-a-comparison-of-targeted-maximum-likelihood-estimation-and-inverse-probability-of-treatment-weighting
#3
Menglan Pang, Tibor Schuster, Kristian B Filion, Mireille E Schnitzer, Maria Eberg, Robert W Platt
Inverse probability of treatment weighting (IPW) and targeted maximum likelihood estimation (TMLE) are relatively new methods proposed for estimating marginal causal effects. TMLE is doubly robust, yielding consistent estimators even under misspecification of either the treatment or the outcome model. While IPW methods are known to be sensitive to near violations of the practical positivity assumption (e. g., in the case of data sparsity), the consequences of this violation in the TMLE framework for binary outcomes have been less widely investigated...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27838682/sample-size-for-assessing-agreement-between-two-methods-of-measurement-by-bland-altman-method
#4
Meng-Jie Lu, Wei-Hua Zhong, Yu-Xiu Liu, Hua-Zhang Miao, Yong-Chang Li, Mu-Huo Ji
The Bland-Altman method has been widely used for assessing agreement between two methods of measurement. However, it remains unsolved about sample size estimation. We propose a new method of sample size estimation for Bland-Altman agreement assessment. According to the Bland-Altman method, the conclusion on agreement is made based on the width of the confidence interval for LOAs (limits of agreement) in comparison to predefined clinical agreement limit. Under the theory of statistical inference, the formulae of sample size estimation are derived, which depended on the pre-determined level of α, β, the mean and the standard deviation of differences between two measurements, and the predefined limits...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27831920/joint-model-for-mortality-and-hospitalization
#5
Yuqi Chen, Wensheng Guo, Peter Kotanko, Len Usvyat, Yuedong Wang
Modeling hospitalization is complicated because the follow-up time can be censored due to death. In this paper, we propose a shared frailty joint model for survival time and hospitalization. A random effect semi-parametric proportional hazard model is assumed for the survival time and conditional on the follow-up time, hospital admissions or total length of stay is modeled by a generalized linear model with a nonparametric offset function of the follow-up time. We assume that the hospitalization and the survival time are correlated through a latent subject-specific random frailty...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27497870/semiparametric-regression-estimation-for-recurrent-event-data-with-errors-in-covariates-under-informative-censoring
#6
Hsiang Yu, Yu-Jen Cheng, Ching-Yun Wang
Recurrent event data arise frequently in many longitudinal follow-up studies. Hence, evaluating covariate effects on the rates of occurrence of such events is commonly of interest. Examples include repeated hospitalizations, recurrent infections of HIV, and tumor recurrences. In this article, we consider semiparametric regression methods for the occurrence rate function of recurrent events when the covariates may be measured with errors. In contrast to the existing works, in our case the conventional assumption of independent censoring is violated since the recurrent event process is interrupted by some correlated events, which is called informative drop-out...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27232635/multi-locus-test-and-correction-for-confounding-effects-in-genome-wide-association-studies
#7
Donglai Chen, Chuanhai Liu, Jun Xie
Genome-wide association studies (GWAS) examine a large number of genetic variants, e. g., single nucleotide polymorphisms (SNP), and associate them with a disease of interest. Traditional statistical methods for GWASs can produce spurious associations, due to limited information from individual SNPs and confounding effects. This paper develops two statistical methods to enhance data analysis of GWASs. The first is a multiple-SNP association test, which is a weighted chi-square test derived for big contingency tables...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27092657/mendelian-randomization-using-public-data-from-genetic-consortia
#8
John R Thompson, Cosetta Minelli, Fabiola Del Greco M
Mendelian randomization (MR) is a technique that seeks to establish causation between an exposure and an outcome using observational data. It is an instrumental variable analysis in which genetic variants are used as the instruments. Many consortia have meta-analysed genome-wide associations between variants and specific traits and made their results publicly available. Using such data, it is possible to derive genetic risk scores for one trait and to deduce the association of that same risk score with a second trait...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/26882561/tree-based-method-for-aggregate-survival-data-modeling
#9
Asanao Shimokawa, Yoshitaka Narita, Soichiro Shibui, Etsuo Miyaoka
In many scenarios, a patient in medical research is treated as a statistical unit. However, in some scenarios, we are interested in treating aggregate data as a statistical unit. In such situations, each set of aggregated data is considered to be a concept in a symbolic representation, and each concept has a hyperrectangle or multiple points in the variable space. To construct a tree-structured model from these aggregate survival data, we propose a new approach, where a datum can be included in several terminal nodes in a tree...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/26812804/testing-equality-in-ordinal-data-with-repeated-measurements-a-model-free-approach
#10
Kung-Jong Lui
In randomized clinical trials, we often encounter ordinal categorical responses with repeated measurements. We propose a model-free approach with using the generalized odds ratio (GOR) to measure the relative treatment effect. We develop procedures for testing equality of treatment effects and derive interval estimators for the GOR. We further develop a simple procedure for testing the treatment-by-period interaction. To illustrate the use of test procedures and interval estimators developed here, we consider two real-life data sets, one studying the gender effect on pain scores on an ordinal scale after hip joint resurfacing surgeries, and the other investigating the effect of an active hypnotic drug in insomnia patients on ordinal categories of time to falling asleep...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/26656800/adaptive-design-for-staggered-start-clinical-trial
#11
Ao Yuan, Qizhai Li, Ming Xiong, Ming T Tan
In phase II and/or III clinical trial study, there are several competing treatments, the goal is to assess the performances of the treatments at the end of the study, the trial design aims to minimize risks to the patients in the trial, according to some given allocation optimality criterion. Recently, a new type of clinical trial, the staggered-start trial has been proposed in some studies, in which different treatments enter the same trial at different times. Some basic questions for this trial are whether optimality can still be kept? under what conditions? and if so how to allocate the the coming patients to treatments to achieve such optimality? Here we propose and study a class of adaptive designs of staggered-start clinical trials, in which for given optimality criterion object, we show that as long as the initial sizes at the beginning of the successive trials are not too large relative to the total sample size, the proposed design can still achieve optimality criterion asymptotically for the allocation proportions as the ordinary trials; if these initial sample sizes have about the same magnitude as the total sample size, full optimality cannot be achieved...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/26641973/a-binomial-integer-valued-arch-model
#12
Miroslav M Ristić, Christian H Weiß, Ana D Janjić
We present an integer-valued ARCH model which can be used for modeling time series of counts with under-, equi-, or overdispersion. The introduced model has a conditional binomial distribution, and it is shown to be strictly stationary and ergodic. The unknown parameters are estimated by three methods: conditional maximum likelihood, conditional least squares and maximum likelihood type penalty function estimation. The asymptotic distributions of the estimators are derived. A real application of the novel model to epidemic surveillance is briefly discussed...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/26636415/effect-of-smoothing-in-generalized-linear-mixed-models-on-the-estimation-of-covariance-parameters-for-longitudinal-data
#13
Muhammad Abu Shadeque Mullah, Andrea Benedetti
Besides being mainly used for analyzing clustered or longitudinal data, generalized linear mixed models can also be used for smoothing via restricting changes in the fit at the knots in regression splines. The resulting models are usually called semiparametric mixed models (SPMMs). We investigate the effect of smoothing using SPMMs on the correlation and variance parameter estimates for serially correlated longitudinal normal, Poisson and binary data. Through simulations, we compare the performance of SPMMs to other simpler methods for estimating the nonlinear association such as fractional polynomials, and using a parametric nonlinear function...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/26569139/a-comparison-of-some-approximate-confidence-intervals-for-a-single-proportion-for-clustered-binary-outcome-data
#14
Krishna K Saha, Daniel Miller, Suojin Wang
Interval estimation of the proportion parameter in the analysis of binary outcome data arising in cluster studies is often an important problem in many biomedical applications. In this paper, we propose two approaches based on the profile likelihood and Wilson score. We compare them with two existing methods recommended for complex survey data and some other methods that are simple extensions of well-known methods such as the likelihood, the generalized estimating equation of Zeger and Liang and the ratio estimator approach of Rao and Scott...
November 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27227728/one-step-targeted-minimum-loss-based-estimation-based-on-universal-least-favorable-one-dimensional-submodels
#15
Mark van der Laan, Susan Gruber
Consider a study in which one observes n independent and identically distributed random variables whose probability distribution is known to be an element of a particular statistical model, and one is concerned with estimation of a particular real valued pathwise differentiable target parameter of this data probability distribution. The targeted maximum likelihood estimator (TMLE) is an asymptotically efficient substitution estimator obtained by constructing a so called least favorable parametric submodel through an initial estimator with score, at zero fluctuation of the initial estimator, that spans the efficient influence curve, and iteratively maximizing the corresponding parametric likelihood till no more updates occur, at which point the updated initial estimator solves the so called efficient influence curve equation...
May 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27227727/second-order-inference-for-the-mean-of-a-variable-missing-at-random
#16
Iván Díaz, Marco Carone, Mark J van der Laan
We present a second-order estimator of the mean of a variable subject to missingness, under the missing at random assumption. The estimator improves upon existing methods by using an approximate second-order expansion of the parameter functional, in addition to the first-order expansion employed by standard doubly robust methods. This results in weaker assumptions about the convergence rates necessary to establish consistency, local efficiency, and asymptotic linearity. The general estimation strategy is developed under the targeted minimum loss-based estimation (TMLE) framework...
May 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27227726/super-learning-of-an-optimal-dynamic-treatment-rule
#17
Alexander R Luedtke, Mark J van der Laan
We consider the estimation of an optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric, beyond possible knowledge about the treatment and censoring mechanisms. We propose data adaptive estimators of this optimal dynamic regime which are defined by sequential loss-based learning under both the blip function and weighted classification frameworks...
May 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27227725/optimal-individualized-treatments-in-resource-limited-settings
#18
Alexander R Luedtke, Mark J van der Laan
An individualized treatment rule (ITR) is a treatment rule which assigns treatments to individuals based on (a subset of) their measured covariates. An optimal ITR is the ITR which maximizes the population mean outcome. Previous works in this area have assumed that treatment is an unlimited resource so that the entire population can be treated if this strategy maximizes the population mean outcome. We consider optimal ITRs in settings where the treatment resource is limited so that there is a maximum proportion of the population which can be treated...
May 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27227724/data-adaptive-bias-reduced-doubly-robust-estimation
#19
Karel Vermeulen, Stijn Vansteelandt
Doubly robust estimators have now been proposed for a variety of target parameters in the causal inference and missing data literature. These consistently estimate the parameter of interest under a semiparametric model when one of two nuisance working models is correctly specified, regardless of which. The recently proposed bias-reduced doubly robust estimation procedure aims to partially retain this robustness in more realistic settings where both working models are misspecified. These so-called bias-reduced doubly robust estimators make use of special (finite-dimensional) nuisance parameter estimators that are designed to locally minimize the squared asymptotic bias of the doubly robust estimator in certain directions of these finite-dimensional nuisance parameters under misspecification of both parametric working models...
May 1, 2016: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27227723/doubly-robust-and-efficient-estimation-of-marginal-structural-models-for-the-hazard-function
#20
Wenjing Zheng, Maya Petersen, Mark J van der Laan
In social and health sciences, many research questions involve understanding the causal effect of a longitudinal treatment on mortality (or time-to-event outcomes in general). Often, treatment status may change in response to past covariates that are risk factors for mortality, and in turn, treatment status may also affect such subsequent covariates. In these situations, Marginal Structural Models (MSMs), introduced by Robins (1997. Marginal structural models Proceedings of the American Statistical Association...
May 1, 2016: International Journal of Biostatistics
journal
journal
41717
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"