Bias reduction for semi-competing risks frailty model with rare events: application to a chronic kidney disease cohort study in South Korea
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. (Source: Lifetime Data Analysis)
Source: Lifetime Data Analysis - November 13, 2023 Category: Statistics Source Type: research
Cox (1972): recollections and reflections
AbstractI present some personal memories and thoughts on Cox ’s 1972 paper “Regression Models and Life-Tables”. (Source: Lifetime Data Analysis)
Source: Lifetime Data Analysis - September 15, 2023 Category: Statistics Source Type: research
Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for Censored Outcomes
AbstractTo 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 fra...
Source: Lifetime Data Analysis - September 2, 2023 Category: Statistics Source Type: research
Causal survival analysis under competing risks using longitudinal modified treatment policies
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. (Source: Lifetime Data Analysis)
Source: Lifetime Data Analysis - August 24, 2023 Category: Statistics Source Type: research
Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring
AbstractWe consider a novel class of semiparametric joint models for multivariate longitudinal and survival data with dependent censoring. In these models, unknown-fashion cumulative baseline hazard functions are fitted by a novel class of penalized-splines (P-splines) with linear constraints. The dependence between the failure time of interest and censoring time is accommodated by a normal transformation model, where both nonparametric marginal survival function and censoring function are transformed to standard normal random variables with bivariate normal joint distribution. Based on a hybrid algorithm together with the...
Source: Lifetime Data Analysis - August 15, 2023 Category: Statistics Source Type: research
Sensitivity Analysis for Observational Studies with Recurrent Events
AbstractWe 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 ...
Source: Lifetime Data Analysis - August 12, 2023 Category: Statistics Source Type: research
Regression analysis of general mixed recurrent event data
AbstractIn 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 th...
Source: Lifetime Data Analysis - July 12, 2023 Category: Statistics Source Type: research
Quantile forward regression for high-dimensional survival data
AbstractDespite the urgent need for an effective prediction model tailored to individual interests, existing models have mainly been developed for the mean outcome, targeting average people. Additionally, the direction and magnitude of covariates ’ effects on the mean outcome may not hold across different quantiles of the outcome distribution. To accommodate the heterogeneous characteristics of covariates and provide a flexible risk model, we propose a quantile forward regression model for high-dimensional survival data. Our method selects variables by maximizing the likelihood of the asymmetric Laplace distribution (ALD...
Source: Lifetime Data Analysis - July 2, 2023 Category: Statistics Source Type: research
Estimation of separable direct and indirect effects in a continuous-time illness-death model
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 sepa rable 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 r egistry study. (Source: Lifetime Data Analysis)
Source: Lifetime Data Analysis - June 4, 2023 Category: Statistics Source Type: research
Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun Study
AbstractThe 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 ...
Source: Lifetime Data Analysis - May 20, 2023 Category: Statistics Source Type: research
Causal inference with recurrent and competing events
AbstractMany 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 con...
Source: Lifetime Data Analysis - May 12, 2023 Category: Statistics Source Type: research
A nonparametric instrumental approach to confounding in competing risks models
AbstractThis paper discusses nonparametric identification and estimation of the causal effect of a treatment in the presence of confounding, competing risks and random right-censoring. Our identification strategy is based on an instrumental variable. We show that the competing risks model generates a nonparametric quantile instrumental regression problem. Quantile treatment effects on the subdistribution function can be recovered from the regression function. A distinguishing feature of the model is that censoring and competing risks prevent identification at some quantiles. We characterize the set of quantiles for which e...
Source: Lifetime Data Analysis - May 9, 2023 Category: Statistics Source Type: research
Volume under the ROC surface for high-dimensional independent screening with ordinal competing risk outcomes
AbstractWe propose a screening method for high-dimensional data with ordinal competing risk outcomes, which is time-dependent and model-free. Existing methods are designed for cause-specific variable screening and fail to evaluate how a biomarker is associated with multiple competing events simultaneously. The proposed method utilizes the Volume under the ROC surface (VUS), which measures the concordance between values of a biomarker and event status at certain time points and provides an overall evaluation of the discrimination capacity of a biomarker. We show that the VUS possesses the sure screening property, i.e., true...
Source: Lifetime Data Analysis - May 9, 2023 Category: Statistics Source Type: research
Regression models for censored time-to-event data using infinitesimal jack-knife pseudo-observations, with applications to left-truncation
We present a modification of the infinitesimal jack-knife pseudo-observations that provide unbiased estimates in a left-truncated cohort. The computational speed and medium and large sample properties of the jack-knife pseudo-observations and infini tesimal jack-knife pseudo-observation are compared and we present an application of the modified infinitesimal jack-knife pseudo-observations in a left-truncated cohort of Danish patients with diabetes. (Source: Lifetime Data Analysis)
Source: Lifetime Data Analysis - May 8, 2023 Category: Statistics Source Type: research
Improving marginal hazard ratio estimation using quadratic inference functions
AbstractClustered 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. ...
Source: Lifetime Data Analysis - May 7, 2023 Category: Statistics Source Type: research
