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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 277)
Statistics in Medicine     Hybrid Journal   (Followers: 143)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 77, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 49)
Sociological Methods & Research     Hybrid Journal   (Followers: 49)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 42)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 36)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 34)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 30)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 25)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 18)
Journal of Statistical Software     Open Access   (Followers: 18, SJR: 13.802, CiteScore: 16)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
International Statistical Review     Hybrid Journal   (Followers: 10)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Current Research in Biostatistics     Open Access   (Followers: 9)
Stata Journal     Full-text available via subscription   (Followers: 9)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Lifetime Data Analysis     Hybrid Journal   (Followers: 6)
Significance     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
TEST     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  

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Lifetime Data Analysis
Journal Prestige (SJR): 0.985
Citation Impact (citeScore): 1
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1572-9249 - ISSN (Online) 1380-7870
Published by Springer-Verlag Homepage  [2468 journals]
  • Regression analysis of doubly censored failure time data with ancillary
           information

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      Abstract: Abstract Doubly censored failure time data occur in many areas and for the situation, the failure time of interest usually represents the elapsed time between two related events such as an infection and the resulting disease onset. Although many methods have been proposed for regression analysis of such data, most of them are conditional on the occurrence time of the initial event and ignore the relationship between the two events or the ancillary information contained in the initial event. Corresponding to this, a new sieve maximum likelihood approach is proposed that makes use of the ancillary information, and in the method, the logistic model and Cox proportional hazards model are employed to model the initial event and the failure time of interest, respectively. A simulation study is conducted and suggests that the proposed method works well in practice and is more efficient than the existing methods as expected. The approach is applied to an AIDS study that motivated this investigation.
      PubDate: 2024-04-20
       
  • Partial-linear single-index transformation models with censored data

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      Abstract: Abstract In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients’ mortality and a Veteran’s Administration lung cancer trial.
      PubDate: 2024-04-16
       
  • Cox model inference for relative hazard and pure risk from stratified
           weight-calibrated case-cohort data

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      Abstract: Abstract The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log-relative hazards, and there has been some work estimating pure risk. Yet there are few examples of these options in the medical literature, and we could not find programs currently online to analyze these various options. We therefore present a unified approach and R software to facilitate such analyses. We used influence functions adapted to the various design and analysis options together with variance calculations that take the two-phase sampling into account. This work clarifies when the widely used “robust” variance estimate of Barlow (Biometrics 50:1064–1072, 1994) is appropriate. The corresponding R software, CaseCohortCoxSurvival, facilitates analysis with and without stratification and/or weight calibration, for subcohort sampling with or without replacement. We also allow for phase-two data to be missing at random for stratified designs. We provide inference not only for log-relative hazards in the Cox model, but also for cumulative baseline hazards and covariate-specific pure risks. We hope these calculations and software will promote wider use of more efficient and principled design and analysis options for case-cohort studies.
      PubDate: 2024-04-02
       
  • On the role of Volterra integral equations in self-consistent,
           product-limit, inverse probability of censoring weighted, and
           redistribution-to-the-right estimators for the survival function

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      Abstract: Abstract This paper reconsiders several results of historical and current importance to nonparametric estimation of the survival distribution for failure in the presence of right-censored observation times, demonstrating in particular how Volterra integral equations help inter-connect the resulting estimators. The paper begins by considering Efron’s self-consistency equation, introduced in a seminal 1967 Berkeley symposium paper. Novel insights provided in the current work include the observations that (i) the self-consistency equation leads directly to an anticipating Volterra integral equation whose solution is given by a product-limit estimator for the censoring survival function; (ii) a definition used in this argument immediately establishes the familiar product-limit estimator for the failure survival function; (iii) the usual Volterra integral equation for the product-limit estimator of the failure survival function leads to an immediate and simple proof that it can be represented as an inverse probability of censoring weighted estimator; (iv) a simple identity characterizes the relationship between natural inverse probability of censoring weighted estimators for the survival and distribution functions of failure; (v) the resulting inverse probability of censoring weighted estimators, attributed to a highly influential 1992 paper of Robins and Rotnitzky, were implicitly introduced in Efron’s 1967 paper in its development of the redistribution-to-the-right algorithm. All results developed herein allow for ties between failure and/or censored observations.
      PubDate: 2024-03-21
       
  • Model averaging for right censored data with measurement error

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      Abstract: Abstract This paper studies a novel model averaging estimation issue for linear regression models when the responses are right censored and the covariates are measured with error. A novel weighted Mallows-type criterion is proposed for the considered issue by introducing multiple candidate models. The weight vector for model averaging is selected by minimizing the proposed criterion. Under some regularity conditions, the asymptotic optimality of the selected weight vector is established in terms of its ability to achieve the lowest squared loss asymptotically. Simulation results show that the proposed method is superior to the other existing related methods. A real data example is provided to supplement the actual performance.
      PubDate: 2024-03-13
       
  • Bias of the additive hazard model in the presence of causal effect
           heterogeneity

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      Abstract: Abstract Hazard ratios are prone to selection bias, compromising their use as causal estimands. On the other hand, if Aalen’s additive hazard model applies, the hazard difference has been shown to remain unaffected by the selection of frailty factors over time. Then, in the absence of confounding, observed hazard differences are equal in expectation to the causal hazard differences. However, in the presence of effect (on the hazard) heterogeneity, the observed hazard difference is also affected by selection of survivors. In this work, we formalize how the observed hazard difference (from a randomized controlled trial) evolves by selecting favourable levels of effect modifiers in the exposed group and thus deviates from the causal effect of interest. Such selection may result in a non-linear integrated hazard difference curve even when the individual causal effects are time-invariant. Therefore, a homogeneous time-varying causal additive effect on the hazard cannot be distinguished from a time-invariant but heterogeneous causal effect. We illustrate this causal issue by studying the effect of chemotherapy on the survival time of patients suffering from carcinoma of the oropharynx using data from a clinical trial. The hazard difference can thus not be used as an appropriate measure of the causal effect without making untestable assumptions.
      PubDate: 2024-03-11
       
  • On variable selection in a semiparametric AFT mixture cure model

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      Abstract: Abstract In clinical studies, one often encounters time-to-event data that are subject to right censoring and for which a fraction of the patients under study never experience the event of interest. Such data can be modeled using cure models in survival analysis. In the presence of cure fraction, the mixture cure model is popular, since it allows to model probability to be cured (called the incidence) and the survival function of the uncured individuals (called the latency). In this paper, we develop a variable selection procedure for the incidence and latency parts of a mixture cure model, consisting of a logistic model for the incidence and a semiparametric accelerated failure time model for the latency. We use a penalized likelihood approach, based on adaptive LASSO penalties for each part of the model, and we consider two algorithms for optimizing the criterion function. Extensive simulations are carried out to assess the accuracy of the proposed selection procedure. Finally, we employ the proposed method to a real dataset regarding heart failure patients with left ventricular systolic dysfunction.
      PubDate: 2024-03-04
       
  • A Bayesian quantile joint modeling of multivariate longitudinal and
           time-to-event data

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      Abstract: Abstract Linear mixed models are traditionally used for jointly modeling (multivariate) longitudinal outcomes and event-time(s). However, when the outcomes are non-Gaussian a quantile regression model is more appropriate. In addition, in the presence of some time-varying covariates, it might be of interest to see how the effects of different covariates vary from one quantile level (of outcomes) to the other, and consequently how the event-time changes across different quantiles. For such analyses linear quantile mixed models can be used, and an efficient computational algorithm can be developed. We analyze a dataset from the Acute Lymphocytic Leukemia (ALL) maintenance study conducted by Tata Medical Center, Kolkata. In this study, the patients suffering from ALL were treated with two standard drugs (6MP and MTx) for the first two years, and three biomarkers (e.g. lymphocyte count, neutrophil count and platelet count) were longitudinally measured. After treatment the patients were followed nearly for the next three years, and the relapse-time (if any) for each patient was recorded. For this dataset we develop a Bayesian quantile joint model for the three longitudinal biomarkers and time-to-relapse. We consider an Asymmetric Laplace Distribution (ALD) for each outcome, and exploit the mixture representation of the ALD for developing a Gibbs sampler algorithm to estimate the regression coefficients. Our proposed model allows different quantile levels for different biomarkers, but still simultaneously estimates the regression coefficients corresponding to a particular quantile combination. We infer that a higher lymphocyte count accelerates the chance of a relapse while a higher neutrophil count and a higher platelet count (jointly) reduce it. Also, we infer that across (almost) all quantiles 6MP reduces the lymphocyte count, while MTx increases the neutrophil count. Simulation studies are performed to assess the effectiveness of the proposed approach.
      PubDate: 2024-03-01
       
  • Pseudo-value regression trees

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      Abstract: Abstract This paper presents a semi-parametric modeling technique for estimating the survival function from a set of right-censored time-to-event data. Our method, named pseudo-value regression trees (PRT), is based on the pseudo-value regression framework, modeling individual-specific survival probabilities by computing pseudo-values and relating them to a set of covariates. The standard approach to pseudo-value regression is to fit a main-effects model using generalized estimating equations (GEE). PRT extend this approach by building a multivariate regression tree with pseudo-value outcome and by successively fitting a set of regularized additive models to the data in the nodes of the tree. Due to the combination of tree learning and additive modeling, PRT are able to perform variable selection and to identify relevant interactions between the covariates, thereby addressing several limitations of the standard GEE approach. In addition, PRT include time-dependent effects in the node-wise models. Interpretability of the PRT fits is ensured by controlling the tree depth. Based on the results of two simulation studies, we investigate the properties of the PRT method and compare it to several alternative modeling techniques. Furthermore, we illustrate PRT by analyzing survival in 3,652 patients enrolled for a randomized study on primary invasive breast cancer.
      PubDate: 2024-02-25
       
  • The built-in selection bias of hazard ratios formalized using structural
           causal models

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      Abstract: Abstract It is known that the hazard ratio lacks a useful causal interpretation. Even for data from a randomized controlled trial, the hazard ratio suffers from so-called built-in selection bias as, over time, the individuals at risk among the exposed and unexposed are no longer exchangeable. In this paper, we formalize how the expectation of the observed hazard ratio evolves and deviates from the causal effect of interest in the presence of heterogeneity of the hazard rate of unexposed individuals (frailty) and heterogeneity in effect (individual modification). For the case of effect heterogeneity, we define the causal hazard ratio. We show that the expected observed hazard ratio equals the ratio of expectations of the latent variables (frailty and modifier) conditionally on survival in the world with and without exposure, respectively. Examples with gamma, inverse Gaussian and compound Poisson distributed frailty and categorical (harming, beneficial or neutral) distributed effect modifiers are presented for illustration. This set of examples shows that an observed hazard ratio with a particular value can arise for all values of the causal hazard ratio. Therefore, the hazard ratio cannot be used as a measure of the causal effect without making untestable assumptions, stressing the importance of using more appropriate estimands, such as contrasts of the survival probabilities.
      PubDate: 2024-02-15
      DOI: 10.1007/s10985-024-09617-y
       
  • Quantile difference estimation with censoring indicators missing at random

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      Abstract: Abstract In this paper, we define estimators of distribution functions when the data are right-censored and the censoring indicators are missing at random, and establish their strong representations and asymptotic normality. Besides, based on empirical likelihood method, we define maximum empirical likelihood estimators and smoothed log-empirical likelihood ratios of two-sample quantile difference in the presence and absence of auxiliary information, respectively, and prove their asymptotic distributions. Simulation study and real data analysis are conducted to investigate the finite sample behavior of the proposed methods.
      PubDate: 2024-01-18
      DOI: 10.1007/s10985-023-09614-7
       
  • Causal inference with recurrent and competing events

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      Abstract: Abstract Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports injuries in athletes. Competing events, such as death, complicate causal inference in studies of recurrent events because once a competing event occurs, an individual cannot have more recurrent events. Several statistical estimands have been studied in recurrent event settings, with and without competing events. However, the causal interpretations of these estimands, and the conditions that are required to identify these estimands from observed data, have yet to be formalized. Here we use a formal framework for causal inference to formulate several causal estimands in recurrent event settings, with and without competing events. When competing events exist, we clarify when commonly used classical statistical estimands can be interpreted as causal quantities from the causal mediation literature, such as (controlled) direct effects and total effects. Furthermore, we show that recent results on interventionist mediation estimands allow us to define new causal estimands with recurrent and competing events that may be of particular clinical relevance in many subject matter settings. We use causal directed acyclic graphs and single world intervention graphs to illustrate how to reason about identification conditions for the various causal estimands based on subject matter knowledge. Furthermore, using results on counting processes, we show that our causal estimands and their identification conditions, which are articulated in discrete time, converge to classical continuous time counterparts in the limit of fine discretizations of time. We propose estimators and establish their consistency for the various identifying functionals. Finally, we use the proposed estimators to compute the effect of blood pressure lowering treatment on the recurrence of acute kidney injury using data from the Systolic Blood Pressure Intervention Trial.
      PubDate: 2024-01-01
      DOI: 10.1007/s10985-023-09594-8
       
  • Estimation of separable direct and indirect effects in a continuous-time
           illness-death model

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      Abstract: Abstract In this article we study the effect of a baseline exposure on a terminal time-to-event outcome either directly or mediated by the illness state of a continuous-time illness-death process with baseline covariates. We propose a definition of the corresponding direct and indirect effects using the concept of separable (interventionist) effects (Robins and Richardson in Causality and psychopathology: finding the determinants of disorders and their cures, Oxford University Press, 2011; Robins et al. in arXiv:2008.06019, 2021; Stensrud et al. in J Am Stat Assoc 117:175–183, 2022). Our proposal generalizes Martinussen and Stensrud (Biometrics 79:127–139, 2023) who consider similar causal estimands for disentangling the causal treatment effects on the event of interest and competing events in the standard continuous-time competing risk model. Unlike natural direct and indirect effects (Robins and Greenland in Epidemiology 3:143–155, 1992; Pearl in Proceedings of the seventeenth conference on uncertainty in artificial intelligence, Morgan Kaufmann, 2001) which are usually defined through manipulations of the mediator independently of the exposure (so-called cross-world interventions), separable direct and indirect effects are defined through interventions on different components of the exposure that exert their effects through distinct causal mechanisms. This approach allows us to define meaningful mediation targets even though the mediating event is truncated by the terminal event. We present the conditions for identifiability, which include some arguably restrictive structural assumptions on the treatment mechanism, and discuss when such assumptions are valid. The identifying functionals are used to construct plug-in estimators for the separable direct and indirect effects. We also present multiply robust and asymptotically efficient estimators based on the efficient influence functions. We verify the theoretical properties of the estimators in a simulation study, and we demonstrate the use of the estimators using data from a Danish registry study.
      PubDate: 2024-01-01
      DOI: 10.1007/s10985-023-09601-y
       
  • RKHS-based covariate balancing for survival causal effect estimation

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      Abstract: Abstract Survival causal effect estimation based on right-censored data is of key interest in both survival analysis and causal inference. Propensity score weighting is one of the most popular methods in the literature. However, since it involves the inverse of propensity score estimates, its practical performance may be very unstable, especially when the covariate overlap is limited between treatment and control groups. To address this problem, a covariate balancing method is developed in this paper to estimate the counterfactual survival function. The proposed method is nonparametric and balances covariates in a reproducing kernel Hilbert space (RKHS) via weights that are counterparts of inverse propensity scores. The uniform rate of convergence for the proposed estimator is shown to be the same as that for the classical Kaplan–Meier estimator. The appealing practical performance of the proposed method is demonstrated by a simulation study as well as two real data applications to study the causal effect of smoking on survival time of stroke patients and that of endotoxin on survival time for female patients with lung cancer respectively.
      PubDate: 2024-01-01
      DOI: 10.1007/s10985-023-09590-y
       
  • Model-based hypothesis tests for the causal mediation of semi-competing
           risks

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      Abstract: Abstract Analyzing the causal mediation of semi-competing risks has become important in medical research. Semi-competing risks refers to a scenario wherein an intermediate event may be censored by a primary event but not vice versa. Causal mediation analyses decompose the effect of an exposure on the primary outcome into an indirect (mediation) effect: an effect mediated through a mediator, and a direct effect: an effect not through the mediator. Here we proposed a model-based testing procedure to examine the indirect effect of the exposure on the primary event through the intermediate event. Under the counterfactual outcome framework, we defined a causal mediation effect using counting process. To assess statistical evidence for the mediation effect, we proposed two tests: an intersection–union test (IUT) and a weighted log-rank test (WLR). The test statistic was developed from a semi-parametric estimator of the mediation effect using a Cox proportional hazards model for the primary event and a series of logistic regression models for the intermediate event. We built a connection between the IUT and WLR. Asymptotic properties of the two tests were derived, and the IUT was determined to be a size \(\alpha \) test and statistically more powerful than the WLR. In numerical simulations, both the model-based IUT and WLR can properly adjust for confounding covariates, and the Type I error rates of the proposed methods are well protected, with the IUT being more powerful than the WLR. Our methods demonstrate the strongly significant effects of hepatitis B or C on the risk of liver cancer mediated through liver cirrhosis incidence in a prospective cohort study. The proposed method is also applicable to surrogate endpoint analyses in clinical trials.
      PubDate: 2024-01-01
      DOI: 10.1007/s10985-023-09595-7
       
  • A Bayesian proportional hazards mixture cure model for interval-censored
           data

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      Abstract: Abstract The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval-censored, the estimation of this model is challenging due to its complex data structure. In this article, we propose a computationally efficient semiparametric Bayesian approach, facilitated by spline approximation and Poisson data augmentation, for model estimation and inference with interval-censored data and a cure rate. The spline approximation and Poisson data augmentation greatly simplify the MCMC algorithm and enhance the convergence of the MCMC chains. The empirical properties of the proposed method are examined through extensive simulation studies and also compared with the R package “GORCure”. The use of the proposed method is illustrated through analyzing a data set from the Aerobics Center Longitudinal Study.
      PubDate: 2023-11-28
      DOI: 10.1007/s10985-023-09613-8
       
  • Efficiency of the Breslow estimator in semiparametric transformation
           models

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      Abstract: Abstract Semiparametric transformation models for failure time data consist of a parametric regression component and an unspecified cumulative baseline hazard. The nonparametric maximum likelihood estimator (NPMLE) of the cumulative baseline hazard can be summarized in terms of weights introduced into a Breslow-type estimator (Weighted Breslow). At any given time point, the weights invoke an integral over the future of the cumulative baseline hazard, which presents theoretical and computational challenges. A simpler non-MLE Breslow-type estimator (Breslow) was derived earlier from a martingale estimating equation (MEE) setting observed and expected counts of failures equal, conditional on the past history. Despite much successful theoretical and computational development, the simpler Breslow estimator continues to be commonly used as a compromise between simplicity and perceived loss of full efficiency. In this paper we derive the relative efficiency of the Breslow estimator and consider the properties of the two estimators using simulations and real data on prostate cancer survival.
      PubDate: 2023-11-26
      DOI: 10.1007/s10985-023-09611-w
       
  • Bias reduction for semi-competing risks frailty model with rare events:
           application to a chronic kidney disease cohort study in South Korea

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      Abstract: Abstract In a semi-competing risks model in which a terminal event censors a non-terminal event but not vice versa, the conventional method can predict clinical outcomes by maximizing likelihood estimation. However, this method can produce unreliable or biased estimators when the number of events in the datasets is small. Specifically, parameter estimates may converge to infinity, or their standard errors can be very large. Moreover, terminal and non-terminal event times may be correlated, which can account for the frailty term. Here, we adapt the penalized likelihood with Firth’s correction method for gamma frailty models with semi-competing risks data to reduce the bias caused by rare events. The proposed method is evaluated in terms of relative bias, mean squared error, standard error, and standard deviation compared to the conventional methods through simulation studies. The results of the proposed method are stable and robust even when data contain only a few events with the misspecification of the baseline hazard function. We also illustrate a real example with a multi-centre, patient-based cohort study to identify risk factors for chronic kidney disease progression or adverse clinical outcomes. This study will provide a better understanding of semi-competing risk data in which the number of specific diseases or events of interest is rare.
      PubDate: 2023-11-13
      DOI: 10.1007/s10985-023-09612-9
       
  • Causal survival analysis under competing risks using longitudinal modified
           treatment policies

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      Abstract: Abstract Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, ordinal, or continuous treatments measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to a competing event that precludes observation of the event of interest. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as \(\sqrt{n}\) -consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.
      PubDate: 2023-08-24
      DOI: 10.1007/s10985-023-09606-7
       
 
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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 277)
Statistics in Medicine     Hybrid Journal   (Followers: 143)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 77, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 49)
Sociological Methods & Research     Hybrid Journal   (Followers: 49)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 42)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 36)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 34)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 30)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 25)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 18)
Journal of Statistical Software     Open Access   (Followers: 18, SJR: 13.802, CiteScore: 16)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
International Statistical Review     Hybrid Journal   (Followers: 10)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Current Research in Biostatistics     Open Access   (Followers: 9)
Stata Journal     Full-text available via subscription   (Followers: 9)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Lifetime Data Analysis     Hybrid Journal   (Followers: 6)
Significance     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
TEST     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  

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Heriot-Watt University
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Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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