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International Journal of Biostatistics

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https://www.readbyqxmd.com/read/28628480/kernel-based-measure-of-variable-importance-for-genetic-association-studies
#1
Vicente Gallego, M Luz Calle, Ramon Oller
The identification of genetic variants that are associated with disease risk is an important goal of genetic association studies. Standard approaches perform univariate analysis where each genetic variant, usually Single Nucleotide Polymorphisms (SNPs), is tested for association with disease status. Though many genetic variants have been identified and validated so far using this univariate approach, for most complex diseases a large part of their genetic component is still unknown, the so called missing heritability...
June 17, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28599385/big-data-small-sample
#2
Inna Gerlovina, Mark J van der Laan, Alan Hubbard
Multiple comparisons and small sample size, common characteristics of many types of "Big Data" including those that are produced by genomic studies, present specific challenges that affect reliability of inference. Use of multiple testing procedures necessitates calculation of very small tail probabilities of a test statistic distribution. Results based on large deviation theory provide a formal condition that is necessary to guarantee error rate control given practical sample sizes, linking the number of tests and the sample size; this condition, however, is rarely satisfied...
May 20, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28541924/characterizing-highly-benefited-patients-in-randomized-clinical-trials
#3
Vivek Charu, Paul B Rosenberg, Lon S Schneider, Lea T Drye, Lisa Rein, David Shade, Constantine G Lyketsos, Constantine E Frangakis
Physicians and patients may choose a certain treatment only if it is predicted to have a large effect for the profile of that patient. We consider randomized controlled trials in which the clinical goal is to identify as many patients as possible that can highly benefit from the treatment. This is challenging with large numbers of covariate profiles, first, because the theoretical, exact method is not feasible, and, second, because usual model-based methods typically give incorrect results. Better, more recent methods use a two-stage approach, where a first stage estimates a working model to produce a scalar predictor of the treatment effect for each covariate profile; and a second stage estimates empirically a high-benefit group based on the first-stage predictor...
May 20, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28541925/improvement-screening-for-ultra-high-dimensional-data-with-censored-survival-outcomes-and-varying-coefficients
#4
Mu Yue, Jialiang Li
Motivated by risk prediction studies with ultra-high dimensional bio markers, we propose a novel improvement screening methodology. Accurate risk prediction can be quite useful for patient treatment selection, prevention strategy or disease management in evidence-based medicine. The question of how to choose new markers in addition to the conventional ones is especially important. In the past decade, a number of new measures for quantifying the added value from the new markers were proposed, among which the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) stand out...
May 18, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28525350/comparing-four-methods-for-estimating-tree-based-treatment-regimes
#5
Aniek Sies, Iven Van Mechelen
When multiple treatment alternatives are available for a certain psychological or medical problem, an important challenge is to find an optimal treatment regime, which specifies for each patient the most effective treatment alternative given his or her pattern of pretreatment characteristics. The focus of this paper is on tree-based treatment regimes, which link an optimal treatment alternative to each leaf of a tree; as such they provide an insightful representation of the decision structure underlying the regime...
May 12, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28493818/group-tests-for-high-dimensional-failure-time-data-with-the-additive-hazards-models
#6
Dandan Jiang, Jianguo Sun
Statistical analysis of high-dimensional data has been attracting more and more attention due to the abundance of such data in various fields such as genetic studies or genomics and the existence of many interesting topics. Among them, one is the identification of a gene or genes that have significant effects on the occurrence of or are significantly related to a certain disease. In this paper, we will discuss such a problem that can be formulated as a group test or testing a group of variables or coefficients when one faces right-censored failure time response variable...
May 9, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28453440/median-analysis-of-repeated-measures-associated-with-recurrent-events-in-presence-of-terminal-event
#7
Rajeshwari Sundaram, Ling Ma, Subhashis Ghoshal
Recurrent events are often encountered in medical follow up studies. In addition, such recurrences have other quantities associated with them that are of considerable interest, for instance medical costs of the repeated hospitalizations and tumor size in cancer recurrences. These processes can be viewed as point processes, i.e. processes with arbitrary positive jump at each recurrence. An analysis of the mean function for such point processes have been proposed in the literature. However, such point processes are often skewed, leading to median as a more appropriate measure than the mean...
April 28, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28441139/empirical-likelihood-in-nonignorable-covariate-missing-data-problems
#8
Yanmei Xie, Biao Zhang
Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model...
April 20, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28361783/a-theorem-at-the-core-of-colliding-bias
#9
Doron J Shahar, Eyal Shahar
Conditioning on a shared outcome of two variables can alter the association between these variables, possibly adding a bias component when estimating effects. In particular, if two causes are marginally independent, they might be dependent in strata of their common effect. Explanations of the phenomenon, however, do not explicitly state when dependence will be created and have been largely informal. We prove that two, marginally independent, causes will be dependent in a particular stratum of their shared outcome if and only if they modify each other's effects, on a probability ratio scale, on that value of the outcome variable...
March 31, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28593874/parameter-estimation-of-a-two-colored-urn-model-class
#10
Line Chloé Le Goff, Philippe Soulier
Though widely used in applications, reinforced random walk on graphs have never been the subject of a valid statistical inference. We develop in this paper a statistical framework for a general two-colored urn model. The probability to draw a ball at each step depends on the number of balls of each color and on a multidimensional parameter through a function, called choice function. We introduce two estimators of the parameter: the maximum likelihood estimator and a weighted least squares estimator which is less efficient, but is closer to the calibration techniques used in the applied literature...
March 25, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28343164/a-quantitative-concordance-measure-for-comparing-and-combining-treatment-selection-markers
#11
Zhiwei Zhang, Shujie Ma, Lei Nie, Guoxing Soon
Motivated by an HIV example, we consider how to compare and combine treatment selection markers, which are essential to the notion of precision medicine. The current literature on precision medicine is focused on evaluating and optimizing treatment regimes, which can be obtained by dichotomizing treatment selection markers. In practice, treatment decisions are based not only on efficacy but also on safety, cost and individual preference, making it difficult to choose a single cutoff value for all patients in all settings...
March 25, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28328527/on-the-conditional-power-in-survival-time-analysis-considering-cure-fractions
#12
Andreas Kuehnapfel, Fabian Schwarzenberger, Markus Scholz
Conditional power of survival endpoints at interim analyses can support decisions on continuing a trial or stopping it for futility. When a cure fraction becomes apparent, conditional power cannot be calculated accurately using simple survival models, e.g. the exponential model. Non-mixture models consider such cure fractions. In this paper, we derive conditional power functions for non-mixture models, namely the non-mixture exponential, the non-mixture Weibull, and the non-mixture Gamma models. Formulae were implemented in the R package CP...
March 17, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28207413/combinatorial-mixtures-of-multiparameter-distributions-an-application-to-bivariate-data
#13
Valeria Edefonti, Giovanni Parmigiani
We introduce combinatorial mixtures - a flexible class of models for inference on mixture distributions whose components have multidimensional parameters. The key idea is to allow each element of the component-specific parameter vectors to be shared by a subset of other components. This approach allows for mixtures that range from very flexible to very parsimonious and unifies inference on component-specific parameters with inference on the number of components. We develop Bayesian inference and computational approaches for this class of distributions, and illustrate them in an application...
February 16, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28195544/on-stratified-adjusted-tests-by-binomial-trials
#14
Asanao Shimokawa, Etsuo Miyaoka
To estimate or test the treatment effect in randomized clinical trials, it is important to adjust for the potential influence of covariates that are likely to affect the association between the treatment or control group and the response. If these covariates are known at the start of the trial, random assignment of the treatment within each stratum would be considered. On the other hand, if these covariates are not clear at the start of the trial, or if it is difficult to allocate the treatment within each stratum, completely randomized assignment of the treatment would be performed...
February 14, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28160542/testing-equality-of-treatments-under-an-incomplete-block-crossover-design-with-ordinal-responses
#15
Kung-Jong Lui
The generalized odds ratio (GOR) for paired sample is considered to measure the relative treatment effect on patient responses in ordinal data. Under a three-treatment two-period incomplete block crossover design, both asymptotic and exact procedures are developed for testing equality between treatments with ordinal responses. Monte Carlo simulation is employed to evaluate and compare the finite-sample performance of these test procedures. A discussion on advantages and disadvantages of the proposed test procedures based on the GOR versus those based on Wald's tests under the normal random effects proportional odds model is provided...
February 3, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28157692/bayesian-variable-selection-methods-for-matched-case-control-studies
#16
Josephine Asafu-Adjei, G Tadesse Mahlet, Brent Coull, Raji Balasubramanian, Michael Lev, Lee Schwamm, Rebecca Betensky
Matched case-control designs are currently used in many biomedical applications. To ensure high efficiency and statistical power in identifying features that best discriminate cases from controls, it is important to account for the use of matched designs. However, in the setting of high dimensional data, few variable selection methods account for matching. Bayesian approaches to variable selection have several advantages, including the fact that such approaches visit a wider range of model subsets. In this paper, we propose a variable selection method to account for case-control matching in a Bayesian context and apply it using simulation studies, a matched brain imaging study conducted at Massachusetts General Hospital, and a matched cardiovascular biomarker study conducted by the High Risk Plaque Initiative...
January 31, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/27930367/multiple-comparisons-using-composite-likelihood-in-clustered-data
#17
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
#18
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
#19
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
#20
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
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