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Journals Communications in Statistics: ...

Communications in Statistics: Theory and Methods

https://read.qxmd.com/read/37840573/inference-for-sparse-linear-regression-based-on-the-leave-one-covariate-out-solution-path
#1
JOURNAL ARTICLE
Xiangyang Cao, Karl Gregory, Dewei Wang
We propose a new measure of variable importance in high-dimensional regression based on the change in the LASSO solution path when one covariate is left out. The proposed procedure provides a novel way to calculate variable importance and conduct variable screening. In addition, our procedure allows for the construction of p -values for testing whether each coe cient is equal to zero as well as for testing hypotheses involving multiple regression coefficients simultaneously; bootstrap techniques are used to construct the null distribution...
2023: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/37588769/estimating-time-varying-treatment-switching-effect-using-accelerated-failure-time-model-with-application-to-vascular-access-for-hemodialysis
#2
JOURNAL ARTICLE
Fang-I Chu, Yuedong Wang
Vascular access for hemodialysis is of paramount importance. Although studies have found that central venous catheter (CVC) is often associated with poor outcomes and switching to arteriovenous fistula (AVF) and arteriovenous grafts (AVG) is beneficial, it has not been fully elucidated how the effect of switching of access on outcomes changes over time and whether the effect depends on switching time. In this paper we propose to relate the observed survival time for patients without access change and the counterfactual time for patients with access change using an AFT model with time-varying effects...
2023: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/37484707/a-note-on-semiparametric-efficient-generalization-of-causal-effects-from-randomized-trials-to-target-populations
#3
JOURNAL ARTICLE
Fan Li, Hwanhee Hong, Elizabeth A Stuart
When effect modifiers influence the decision to participate in randomized trials, generalizing causal effect estimates to an external target population requires the knowledge of two scores - the propensity score for receiving treatment and the sampling score for trial participation. While the former score is known due to randomization, the latter score is usually unknown and estimated from data. Under unconfounded trial participation, we characterize the asymptotic efficiency bounds for estimating two causal estimands - the population average treatment effect and the average treatment effect among the non-participants - and examine the role of the scores...
2023: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/36743328/power-for-balanced-linear-mixed-models-with-complex-missing-data-processes
#4
JOURNAL ARTICLE
Kevin P Josey, Brandy M Ringham, Anna E Barón, Margaret Schenkman, Katherine A Sauder, Keith E Muller, Dana Dabelea, Deborah H Glueck
When designing repeated measures studies, both the amount and the pattern of missing outcome data can affect power. The chance that an observation is missing may vary across measurements, and missingness may be correlated across measurements. For example, in a physiotherapy study of patients with Parkinson's disease, increasing intermittent dropout over time yielded missing measurements of physical function. In this example, we assume data are missing completely at random, since the chance that a data point was missing appears to be unrelated to either outcomes or covariates...
2023: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/36353187/semiparametric-copula-based-regression-modeling-of-semi-competing-risks-data
#5
JOURNAL ARTICLE
Hong Zhu, Yu Lan, Jing Ning, Yu Shen
Semi-competing risks data often arise in medical studies where the terminal event (e.g., death) censors the non-terminal event (e.g., cancer recurrence), but the non-terminal event does not prevent the subsequent occurrence of the terminal event. This article considers regression modeling of semi-competing risks data to assess the covariate effects on the respective non-terminal and terminal event times. We propose a copula-based framework for semi-competing risks regression with time-varying coefficients, where the dependence between the non-terminal and terminal event times is characterized by a copula and the time-varying covariate effects are imposed on two marginal regression models...
2022: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/35399822/confidence-interval-estimation-of-the-youden-index-and-corresponding-cut-point-for-a-combination-of-biomarkers-under-normality
#6
JOURNAL ARTICLE
Kristopher Attwood, Lili Tian
In prognostic/diagnostic medical research, it is often the goal to identify a biomarker that differentiates between patients with and without a condition, or patients that will have good or poor response to a given treatment. The statistical literature is abundant with methods for evaluating single biomarkers for these purposes. However, in practice, a single biomarker rarely captures all aspects of a disease process; therefore, it is often the case that using a combination of biomarkers will improve discriminatory ability...
2022: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/33716388/covariate-adjustment-via-propensity-scores-for-recurrent-events-in-the-presence-of-dependent-censoring
#7
JOURNAL ARTICLE
Youngjoo Cho, Debashis Ghosh
Dependent censoring is common in many medical studies, especially when there are multiple occurrences of the event of interest. Ghosh and Lin (2003) and Hsieh, Ding and Wang (2011) proposed estimation procedures using an artificial censoring technique. However, if covariates are not bounded, then these methods can cause excessive artificial censoring. In this paper, we propose estimation procedures for the treatment effect based on a novel application of propensity scores. Simulation studies show that the proposed method provides good finite-sample properties...
2021: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/33408437/exact-group-sequential-designs-for-two-arm-experiments-with-poisson-distributed-outcome-variables
#8
JOURNAL ARTICLE
Michael J Grayling, James M S Wason, Adrian P Mander
We describe and compare two methods for the group sequential design of two-arm experiments with Poisson distributed data, which are based on a normal approximation and exact calculations respectively. A framework to determine near-optimal stopping boundaries is also presented. Using this framework, for a considered example, we demonstrate that a group sequential design could reduce the expected sample size under the null hypothesis by as much as 44% compared to a fixed sample approach. We conclude with a discussion of the advantages and disadvantages of the two presented procedures...
2021: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/33767526/a-class-of-additive-transformation-models-for-recurrent-gap-times
#9
JOURNAL ARTICLE
Ling Chen, Yanqin Feng, Jianguo Sun
The gap time between recurrent events is often of primary interest in many fields such as medical studies (Cook and Lawless 2007; Kang, Sun, and Zhao 2015; Schaubel and Cai 2004), and in this paper, we discuss regression analysis of the gap times arising from a general class of additive transformation models. For the problem, we propose two estimation procedures, the modified within-cluster resampling (MWCR) method and the weighted risk-set (WRS) method, and the proposed estimators are shown to be consistent and asymptotically follow the normal distribution...
2020: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/32913381/log-epsilon-skew-normal-a-generalization-of-the-log-normal-distribution
#10
JOURNAL ARTICLE
Alan D Hutson, Terry L Mashtare, Govind S Mudholkar
The log-normal distribution is widely used to model non-negative data in many areas of applied research. In this paper, we introduce and study a family of distributions with non-negative reals as support and termed the log-epsilon-skew normal (LESN) which includes the log-normal distributions as a special case. It is related to the epsilon-skew normal developed in Mudholkar and Hutson (2000) the way the log-normal is related to the normal distribution. We study its main properties, hazard function, moments, skewness and kurtosis coefficients, and discuss maximum likelihood estimation of model parameters...
2020: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/31768088/sample-size-calculation-for-count-outcomes-in-cluster-randomization-trials-with-varying-cluster-sizes
#11
JOURNAL ARTICLE
Jijia Wang, Song Zhang, Chul Ahn
In many cluster randomization studies, cluster sizes are not fixed and may be highly variable. For those studies, sample size estimation assuming a constant cluster size may lead to under-powered studies. Sample size formulas have been developed to incorporate the variability in cluster size for clinical trials with continuous and binary outcomes. Count outcomes frequently occur in cluster randomized studies. In this paper, we derive a closed-form sample size formula for count outcomes accounting for the variability in cluster size...
2020: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/33635297/a-robust-regression-methodology-via-m-estimation
#12
JOURNAL ARTICLE
Tao Yang, Colin M Gallagher, Christopher S McMahan
A robust regression methodology is proposed via M-estimation. The approach adapts to the tail behavior and skewness of the distribution of the random error terms, providing for a reliable analysis under a broad class of distributions. This is accomplished by allowing the objective function, used to determine the regression parameter estimates, to be selected in a data driven manner. The asymptotic properties of the proposed estimator are established and a numerical algorithm is provided to implement the methodology...
2019: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/32952273/parameter-estimation-for-semiparametric-ordinary-differential-equation-models
#13
JOURNAL ARTICLE
Hongqi Xue, Arun Kumar, Hulin Wu
We propose a new class of two-stage parameter estimation methods for semiparametric ordinary differential equation (ODE) models. In the first stage, state variables are estimated using a penalized spline approach; In the second stage, form of numerical discretization algorithms for an ODE solver is used to formulate estimating equations. Estimated state variables from the first stage are used to obtain more data points for the second stage. Asymptotic properties for the proposed estimators are established. Simulation studies show that the method performs well, especially for small sample...
2019: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/31649416/estimation-of-multi-state-models-with-missing-covariate-values-based-on-observed-data-likelihood
#14
JOURNAL ARTICLE
Wenjie Lou, Erin L Abner, Lijie Wan, David W Fardo, Richard Lipton, Mindy Katz, Richard J Kryscio
Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both 'missing completely at random' and 'missing at random' mechanisms...
2019: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/31571721/a-likelihood-based-approach-for-joint-modeling-of-longitudinal-trajectories-and-informative-censoring-process
#15
JOURNAL ARTICLE
Miran A Jaffa, Ayad A Jaffa
We propose a joint modeling likelihood-based approach for studies with repeated measures and informative right censoring. Joint modeling of longitudinal and survival data are common approaches but could result in biased estimates if proportionality of hazards is violated. To overcome this issue, and given that the exact time of dropout is typically unknown, we modeled the censoring time as the number of follow-up visits and extended it to be dependent on selected covariates. Longitudinal trajectories for each subject were modeled to provide insight into disease progression and incorporated with the number follow-up visits in one likelihood function...
2019: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/31548760/an-alternative-skew-exponential-power-distribution-formulation
#16
JOURNAL ARTICLE
Alan D Hutson
In this note we propose a newly formulated skew exponential power distribution that behaves substantially better than previously defined versions. This new model performs very well in terms of the large sample behavior of the maximum likelihood estimation procedure when compared to the classically defined four parameter model defined by Azzalini (1986). More recently, approaches to defining a skew exponential power distribution have used five or more parameters. Our approach improves upon previous attempts to extend the symmetric power exponential family to include skew alternatives by maintaining a minimum set of four parameters corresponding directly to location, scale, skewness and kurtosis...
2019: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/31467462/calculating-power-for-the-general-linear-multivariate-model-with-one-or-more-gaussian-covariates
#17
JOURNAL ARTICLE
S M Kreidler, B M Ringham, K E Muller, D H Glueck
We describe a noncentral ℱ power approximation for hypotheses about fixed predictors in general linear multivariate models with one or more Gaussian covariates. The results apply to both single and multiple parameter hypotheses. The approach extends power approximations for models with only fixed predictors, and for models with a single Gaussian covariate. The new method approximates the noncentrality parameter under the alternative hypothesis using a Taylor series expansion for the matrix-variate beta distribution of type I...
2019: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/31439981/on-the-distribution-of-summary-statistics-for-missing-data
#18
JOURNAL ARTICLE
B M Ringham, S M Kreidler, K E Muller, D H Glueck
Under an assumption that missing values occur randomly in a matrix, formulae are developed for the expected value and variance of six statistics that summarize the number and location of the missing values. For a seventh statistic, a regression model based on simulated data yields an estimate of the expected value. The results can be used in the development of methods to control the Type I error and approximate power and sample size for multilevel and longitudinal studies with missing data.
2019: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/31217751/conditionally-unbiased-estimation-in-the-normal-setting-with-unknown-variances
#19
JOURNAL ARTICLE
David S Robertson, Ekkehard Glimm
To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz ( Statistics & Probability Letters , 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common unknown variance...
2019: Communications in Statistics: Theory and Methods
https://read.qxmd.com/read/34305271/on-the-trace-of-a-wishart
#20
JOURNAL ARTICLE
Deborah H Glueck, Keith E Muller
A derivation based on spectral decomposition allows specifying the characteristic function of the trace of a singular or nonsingular, central or noncentral, true or pseudo-Wishart. The trace equals a weighted sum of noncentral chi-squared random variables and constants. We describe computational methods.
2018: Communications in Statistics: Theory and Methods
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