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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 35)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Biometrics     Hybrid Journal   (Followers: 49)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Computational Statistics     Hybrid Journal   (Followers: 16)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 14)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 18)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
International Statistical Review     Hybrid Journal   (Followers: 10)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 84)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 34)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Journal of Statistical Software     Open Access   (Followers: 18, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 76, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal  
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 30)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 42)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 17)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 6)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 9)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Economics and Statistics     Hybrid Journal   (Followers: 275)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 16)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Significance     Hybrid Journal   (Followers: 6)
Sociological Methods & Research     Hybrid Journal   (Followers: 48)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Statistical Modelling     Hybrid Journal   (Followers: 18)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 141)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 25)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Similar Journals
Journal Cover
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]
  • 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
       
  • 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
       
  • 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
       
  • 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
       
  • 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
       
  • 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
       
  • Targeted maximum likelihood estimation for causal inference in survival
           and competing risks analysis

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      Abstract: Abstract Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in presence of high-dimensional nuisance parameters. Generally, TMLE consists of a two-step procedure that combines data-adaptive nuisance parameter estimation with semiparametric efficiency and rigorous statistical inference obtained via a targeted update step. In this paper, we demonstrate the practical applicability of TMLE based causal inference in survival and competing risks settings where event times are not confined to take place on a discrete and finite grid. We focus on estimation of causal effects of time-fixed treatment decisions on survival and absolute risk probabilities, considering different univariate and multidimensional parameters. Besides providing a general guidance to using TMLE for survival and competing risks analysis, we further describe how the previous work can be extended with the use of loss-based cross-validated estimation, also known as super learning, of the conditional hazards. We illustrate the usage of the considered methods using publicly available data from a trial on adjuvant chemotherapy for colon cancer. R software code to implement all considered algorithms and to reproduce all analyses is available in an accompanying online appendix on Github.
      PubDate: 2024-01-01
       
  • 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
       
  • Assessing model prediction performance for the expected cumulative number
           of recurrent events

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      Abstract: Abstract In a recurrent event setting, we introduce a new score designed to evaluate the prediction ability, for a given model, of the expected cumulative number of recurrent events. This score can be seen as an extension of the Brier Score for single time to event data but works for recurrent events with or without a terminal event. Theoretical results are provided that show that under standard assumptions in a recurrent event context, our score can be asymptotically decomposed as the sum of the theoretical mean squared error between the model and the true expected cumulative number of recurrent events and an inseparability term that does not depend on the model. This decomposition is further illustrated on simulations studies. It is also shown that this score should be used in comparison with a reference model, such as a nonparametric estimator that does not include the covariates. Finally, the score is applied for the prediction of hospitalisations on a dataset of patients suffering from atrial fibrillation and a comparison of the prediction performances of different models, such as the Cox model, the Aalen Model or the Ghosh and Lin model, is investigated.
      PubDate: 2023-11-17
      DOI: 10.1007/s10985-023-09610-x
       
  • 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
       
  • Improving marginal hazard ratio estimation using quadratic inference
           functions

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      Abstract: Abstract Clustered and multivariate failure time data are commonly encountered in biomedical studies and a marginal regression approach is often employed to identify the potential risk factors of a failure. We consider a semiparametric marginal Cox proportional hazards model for right-censored survival data with potential correlation. We propose to use a quadratic inference function method based on the generalized method of moments to obtain the optimal hazard ratio estimators. The inverse of the working correlation matrix is represented by the linear combination of basis matrices in the context of the estimating equation. We investigate the asymptotic properties of the regression estimators from the proposed method. The optimality of the hazard ratio estimators is discussed. Our simulation study shows that the estimator from the quadratic inference approach is more efficient than those from existing estimating equation methods whether the working correlation structure is correctly specified or not. Finally, we apply the model and the proposed estimation method to analyze a study of tooth loss and have uncovered new insights that were previously inaccessible using existing methods.
      PubDate: 2023-10-01
      DOI: 10.1007/s10985-023-09598-4
       
  • Estimation and testing for clustered interval-censored bivariate survival
           data with application using the semi-parametric version of the
           Clayton–Oakes model

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      Abstract: Abstract The Kaplan–Meier estimator is ubiquitously used to estimate survival probabilities for time-to-event data. It is nonparametric, and thus does not require specification of a survival distribution, but it does assume that the risk set at any time t consists of independent observations. This assumption does not hold for data from paired organ systems such as occur in ophthalmology (eyes) or otolaryngology (ears), or for other types of clustered data. In this article, we estimate marginal survival probabilities in the setting of clustered data, and provide confidence limits for these estimates with intra-cluster correlation accounted for by an interval-censored version of the Clayton–Oakes model. We develop a goodness-of-fit test for general bivariate interval-censored data and apply it to the proposed interval-censored version of the Clayton–Oakes model. We also propose a likelihood ratio test for the comparison of survival distributions between two groups in the setting of clustered data under the assumption of a constant between-group hazard ratio. This methodology can be used both for balanced and unbalanced cluster sizes, and also when the cluster size is informative. We compare our test to the ordinary log rank test and the Lin-Wei (LW) test based on the marginal Cox proportional Hazards model with robust standard errors obtained from the sandwich estimator. Simulation results indicate that the ordinary log rank test over-inflates type I error, while the proposed unconditional likelihood ratio test has appropriate type I error and higher power than the LW test. The method is demonstrated in real examples from the Sorbinil Retinopathy Trial, and the Age-Related Macular Degeneration Study. Raw data from these two trials are provided.
      PubDate: 2023-10-01
      DOI: 10.1007/s10985-022-09588-y
       
  • Cox (1972): recollections and reflections

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      Abstract: Abstract I present some personal memories and thoughts on Cox’s 1972 paper “Regression Models and Life-Tables”.
      PubDate: 2023-09-15
      DOI: 10.1007/s10985-023-09609-4
       
  • Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for
           Censored Outcomes

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      Abstract: Abstract To achieve the goal of providing the best possible care to each individual under their care, physicians need to customize treatments for individuals with the same health state, especially when treating diseases that can progress further and require additional treatments, such as cancer. Making decisions at multiple stages as a disease progresses can be formalized as a dynamic treatment regime (DTR). Most of the existing optimization approaches for estimating dynamic treatment regimes including the popular method of Q-learning were developed in a frequentist context. Recently, a general Bayesian machine learning framework that facilitates using Bayesian regression modeling to optimize DTRs has been proposed. In this article, we adapt this approach to censored outcomes using Bayesian additive regression trees (BART) for each stage under the accelerated failure time modeling framework, along with simulation studies and a real data example that compare the proposed approach with Q-learning. We also develop an R wrapper function that utilizes a standard BART survival model to optimize DTRs for censored outcomes. The wrapper function can easily be extended to accommodate any type of Bayesian machine learning model.
      PubDate: 2023-09-02
      DOI: 10.1007/s10985-023-09605-8
       
  • 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
       
  • Sensitivity Analysis for Observational Studies with Recurrent Events

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      Abstract: Abstract We conduct an observational study of the effect of sickle cell trait Haemoglobin AS (HbAS) on the hazard rate of malaria fevers in children. Assuming no unmeasured confounding, there is strong evidence that HbAS reduces the rate of malarial fevers. Since this is an observational study, however, the no unmeasured confounding assumption is strong. A sensitivity analysis considers how robust a conclusion is to a potential unmeasured confounder. We propose a new sensitivity analysis method for recurrent event data and apply it to the malaria study. We find that for the causal conclusion that HbAS is protective against malarial fevers to be overturned, the hypothesized unmeasured confounder must be as influential as all but one of the measured confounders.
      PubDate: 2023-08-12
      DOI: 10.1007/s10985-023-09607-6
       
  • Regression analysis of general mixed recurrent event data

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      Abstract: Abstract In modern biomedical datasets, it is common for recurrent outcomes data to be collected in an incomplete manner. More specifically, information on recurrent events is routinely recorded as a mixture of recurrent event data, panel count data, and panel binary data; we refer to this structure as general mixed recurrent event data. Although the aforementioned data types are individually well-studied, there does not appear to exist an established approach for regression analysis of the three component combination. Often, ad-hoc measures such as imputation or discarding of data are used to homogenize records prior to the analysis, but such measures lead to obvious concerns regarding robustness, loss of efficiency, and other issues. This work proposes a maximum likelihood regression estimation procedure for the combination of general mixed recurrent event data and establishes the asymptotic properties of the proposed estimators. In addition, we generalize the approach to allow for the existence of terminal events, a common complicating feature in recurrent event analysis. Numerical studies and application to the Childhood Cancer Survivor Study suggest that the proposed procedures work well in practical situations.
      PubDate: 2023-07-12
      DOI: 10.1007/s10985-023-09604-9
       
  • Evaluation of the natural history of disease by combining incident and
           prevalent cohorts: application to the Nun Study

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      Abstract: Abstract The Nun study is a well-known longitudinal epidemiology study of aging and dementia that recruited elderly nuns who were not yet diagnosed with dementia (i.e., incident cohort) and who had dementia prior to entry (i.e., prevalent cohort). In such a natural history of disease study, multistate modeling of the combined data from both incident and prevalent cohorts is desirable to improve the efficiency of inference. While important, the multistate modeling approaches for the combined data have been scarcely used in practice because prevalent samples do not provide the exact date of disease onset and do not represent the target population due to left-truncation. In this paper, we demonstrate how to adequately combine both incident and prevalent cohorts to examine risk factors for every possible transition in studying the natural history of dementia. We adapt a four-state nonhomogeneous Markov model to characterize all transitions between different clinical stages, including plausible reversible transitions. The estimating procedure using the combined data leads to efficiency gains for every transition compared to those from the incident cohort data only.
      PubDate: 2023-05-20
      DOI: 10.1007/s10985-023-09602-x
       
 
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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 35)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Biometrics     Hybrid Journal   (Followers: 49)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Computational Statistics     Hybrid Journal   (Followers: 16)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 14)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 18)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
International Statistical Review     Hybrid Journal   (Followers: 10)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 84)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 34)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Journal of Statistical Software     Open Access   (Followers: 18, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 76, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal  
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 30)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 42)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 17)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 6)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 9)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Economics and Statistics     Hybrid Journal   (Followers: 275)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 16)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Significance     Hybrid Journal   (Followers: 6)
Sociological Methods & Research     Hybrid Journal   (Followers: 48)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Statistical Modelling     Hybrid Journal   (Followers: 18)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 141)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 25)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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