Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point
AbstractMedical research frequently involves comparing an event time of interest between treatment groups. Rather than comparing the entire survival or cumulative incidence curves, it is sometimes preferable to evaluate these probabilities at a fixed point in time. Performing a covariate adjusted analysis can improve efficiency, even in randomized clinical trials, but no currently available group sequential test for fixed point analysis provides this adjustment. This paper introduces covariate adjusted group sequential pointwise comparisons of survival and cumulative incidence probabilities. Their test statistics have an a...
Source: Lifetime Data Analysis - November 14, 2019 Category: Statistics Source Type: research
Tree-based modeling of time-varying coefficients in discrete time-to-event models
AbstractHazard models are popular tools for the modeling of discrete time-to-event data. In particular two approaches for modeling time dependent effects are in common use. The more traditional one assumes a linear predictor with effects of explanatory variables being constant over time. The more flexible approach uses the class of semiparametric models that allow the effects of the explanatory variables to vary smoothly over time. The approach considered here is in between these modeling strategies. It assumes that the effects of the explanatory variables are piecewise constant. It allows, in particular, to evaluate at wh...
Source: Lifetime Data Analysis - November 10, 2019 Category: Statistics Source Type: research
Varying coefficient transformation cure models for failure time data
This article discusses regression analysis of right-censored failure time data where there may exist a cured subgroup, and also covariate effects may be varying with time, a phenomena that often occurs in many medical studies. To address the problem, we discuss a class of varying coefficient transformation models along with a logistic model for the cured subgroup. For inference, a sieve maximum likelihood approach is developed with the use of spline functions, and the asymptotic properties of the proposed estimators are established. The proposed method can be easily implemented, and the conducted simulation study suggests ...
Source: Lifetime Data Analysis - October 8, 2019 Category: Statistics Source Type: research
Defining causal mediation with a longitudinal mediator and a survival outcome
AbstractIn the context of causal mediation analysis, prevailing notions of direct and indirect effects are based on nested counterfactuals. These can be problematic regarding interpretation and identifiability especially when the mediator is a time-dependent process and the outcome is survival or, more generally, a time-to-event outcome. We propose and discuss an alternative definition of mediated effects that does not suffer from these problems, and is more transparent than the current alternatives. Our proposal is based on the extended graphical approach of Robins and Richardson (in: Shrout (ed) Causality and psychopatho...
Source: Lifetime Data Analysis - September 30, 2019 Category: Statistics Source Type: research
Prognostic score matching methods for estimating the average effect of a non-reversible binary time-dependent treatment on the survival function
AbstractIn evaluating the benefit of a treatment on survival, it is often of interest to compare post-treatment survival with the survival function that would have been observed in the absence of treatment. In many practical settings, treatment is time-dependent in the sense that subjects typically begin follow-up untreated, with some going on to receive treatment at some later time point. In observational studies, treatment is not assigned at random and, therefore, may depend on various patient characteristics. We have developed semi-parametric matching methods to estimate the average treatment effect on the treated (ATT)...
Source: Lifetime Data Analysis - September 30, 2019 Category: Statistics Source Type: research
Bootstrap and permutation rank tests for proportional hazards under right censoring
AbstractWe address the testing problem of proportional hazards in the two-sample survival setting allowing right censoring, i.e., we check whether the famous Cox model is underlying. Although there are many test proposals for this problem, only a few papers suggest how to improve the performance for small sample sizes. In this paper, we do exactly this by carrying out our test as a permutation as well as a wild bootstrap test. The asymptotic properties of our test, namely asymptotic exactness under the null and consistency, can be transferred to both resampling versions. Various simulations for small sample sizes reveal an...
Source: Lifetime Data Analysis - September 24, 2019 Category: Statistics Source Type: research
A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events
AbstractRecurrent event data with a terminal event commonly arise in longitudinal follow-up studies. We use a weighted composite endpoint of all recurrent and terminal events to assess the overall effects of covariates on the two types of events. A semiparametric additive rates model is proposed to analyze the weighted composite event process and the dependence structure among recurrent and terminal events is left unspecified. An estimating equation approach is developed for inference, and the asymptotic properties of the resulting estimators are established. The finite-sample behavior of the proposed estimators is evaluat...
Source: Lifetime Data Analysis - September 22, 2019 Category: Statistics Source Type: research
Correction to: Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards
The R code used for the data analysis and simulations in our manuscript (D íaz et al. 2018) had two errors, which we have corrected. (Source: Lifetime Data Analysis)
Source: Lifetime Data Analysis - September 3, 2019 Category: Statistics Source Type: research
Special issue dedicated to Odd O. Aalen
(Source: Lifetime Data Analysis)
Source: Lifetime Data Analysis - August 27, 2019 Category: Statistics Source Type: research
Semiparametric methods for survival data with measurement error under additive hazards cure rate models
AbstractIt is well established that measurement error has drastically negative impact on data analysis. It can not only bias parameter estimates but may also cause loss of power for testing relationship between variables. Although survival analysis of error-contaminated data has attracted extensive interest, relatively little attention has been paid to dealing with survival data with error-contaminated covariates when the underlying population is characterized by a cured fraction. In this paper, we consider this problem for which lifetimes of the non-cured individuals are featured by the additive hazards model and the meas...
Source: Lifetime Data Analysis - August 19, 2019 Category: Statistics Source Type: research
Parametric modelling of prevalent cohort data with uncertainty in the measurement of the initial onset date
AbstractIn prevalent cohort studies with follow-up, if disease duration is the focus, the date of onset must be obtained retrospectively. For some diseases, such as Alzheimer ’s disease, the very notion of a date of onset is unclear, and it can be assumed that the reported date of onset acts only as a proxy for the unknown true date of onset. When adjusting for onset dates reported with error, the features of left-truncation and potential right-censoring of the failure times must be modeled appropriately. Under the assumptions of a classical measurement error model for the onset times and an underlying parametric failure...
Source: Lifetime Data Analysis - August 1, 2019 Category: Statistics Source Type: research
Parametric and semiparametric estimation methods for survival data under a flexible class of models
AbstractIn survival analysis, accelerated failure time models are useful in modeling the relationship between failure times and the associated covariates, where covariate effects are assumed to appear in a linear form in the model. Such an assumption of covariate effects is, however, quite restrictive for many practical problems. To incorporate flexible nonlinear relationship between covariates and transformed failure times, we propose partially linear single index models to facilitate complex relationship between transformed failure times and covariates. We develop two inference methods which handle the unknown nonlinear ...
Source: Lifetime Data Analysis - July 31, 2019 Category: Statistics Source Type: research
Prevalent cohort studies and unobserved heterogeneity
AbstractConsider lifetimes originating at a series of calendar times\( t_{1} ,t_{2} , \ldots \). At a certain time\( t_{0} \) a cross-sectional sample is taken, generating a sample ofcurrent durations (backward recurrence times) of survivors until\( t_{0} \) and aprevalent cohort study consisting of survival times left-truncated at the current durations. A Lexis diagram is helpful in visualizing this situation. Survival analysis based on current durations and prevalent cohort studies is now well-established as long as all covariates are observed. The general problems withunobserved covariates have been well understood for ...
Source: Lifetime Data Analysis - July 2, 2019 Category: Statistics Source Type: research
Quantile regression-based Bayesian joint modeling analysis of longitudinal –survival data, with application to an AIDS cohort study
AbstractIn longitudinal studies, it is of interest to investigate how repeatedly measured markers are associated with time to an event. Joint models have received increasing attention on analyzing such complex longitudinal –survival data with multiple data features, but most of them are mean regression-based models. This paper formulates a quantile regression (QR) based joint models in general forms that consider left-censoring due to the limit of detection, covariates with measurement errors and skewness. The joint models consist of three components: (i) QR-based nonlinear mixed-effects Tobit model using asymmetric Lapl...
Source: Lifetime Data Analysis - May 27, 2019 Category: Statistics Source Type: research
Semiparametric regression analysis of doubly censored failure time data from cohort studies
AbstractDoubly censored failure time data occur when the failure time of interest represents the elapsed time between two events, an initial event and a subsequent event, and the observations on both events may suffer censoring. A well-known example of such data is given by the acquired immune deficiency syndrome (AIDS) cohort study in which the two events are HIV infection and AIDS diagnosis, and several inference methods have been developed in the literature for their regression analysis. However, all of them only apply to limited situations or focus on a single model. In this paper, we propose a marginal likelihood appr...
Source: Lifetime Data Analysis - May 20, 2019 Category: Statistics Source Type: research
