64 research outputs found
A Quantile Regression Model for Failure-Time Data with Time-Dependent Covariates
Since survival data occur over time, often important covariates that we wish
to consider also change over time. Such covariates are referred as
time-dependent covariates. Quantile regression offers flexible modeling of
survival data by allowing the covariates to vary with quantiles. This paper
provides a novel quantile regression model accommodating time-dependent
covariates, for analyzing survival data subject to right censoring. Our simple
estimation technique assumes the existence of instrumental variables. In
addition, we present a doubly-robust estimator in the sense of Robins and
Rotnitzky (1992). The asymptotic properties of the estimators are rigorously
studied. Finite-sample properties are demonstrated by a simulation study. The
utility of the proposed methodology is demonstrated using the Stanford heart
transplant dataset
General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv
The R package frailtySurv for simulating and fitting semi-parametric shared
frailty models is introduced. Package frailtySurv implements semi-parametric
consistent estimators for a variety of frailty distributions, including gamma,
log-normal, inverse Gaussian and power variance function, and provides
consistent estimators of the standard errors of the parameters' estimators. The
parameters' estimators are asymptotically normally distributed, and therefore
statistical inference based on the results of this package, such as hypothesis
testing and confidence intervals, can be performed using the normal
distribution. Extensive simulations demonstrate the flexibility and correct
implementation of the estimator. Two case studies performed with publicly
available datasets demonstrate applicability of the package. In the Diabetic
Retinopathy Study, the onset of blindness is clustered by patient, and in a
large hard drive failure dataset, failure times are thought to be clustered by
the hard drive manufacturer and model
Discrete-time Competing-Risks Regression with or without Penalization
Many studies employ the analysis of time-to-event data that incorporates
competing risks and right censoring. Most methods and software packages are
geared towards analyzing data that comes from a continuous failure time
distribution. However, failure-time data may sometimes be discrete either
because time is inherently discrete or due to imprecise measurement. This paper
introduces a novel estimation procedure for discrete-time survival analysis
with competing events. The proposed approach offers two key advantages over
existing procedures: first, it expedites the estimation process for a large
number of unique failure time points; second, it allows for straightforward
integration and application of widely used regularized regression and screening
methods. We illustrate the benefits of our proposed approach by conducting a
comprehensive simulation study. Additionally, we showcase the utility of our
procedure by estimating a survival model for the length of stay of patients
hospitalized in the intensive care unit, considering three competing events:
discharge to home, transfer to another medical facility, and in-hospital death
Unlocking Retrospective Prevalent Information in EHRs -- a Pairwise Pseudolikelihood Approach
Typically, electronic health record data are not collected towards a specific
research question. Instead, they comprise numerous observations recruited at
different ages, whose medical, environmental and oftentimes also genetic data
are being collected. Some phenotypes, such as disease-onset ages, may be
reported retrospectively if the event preceded recruitment, and such
observations are termed ``prevalent". The standard method to accommodate this
``delayed entry" conditions on the entire history up to recruitment, hence the
retrospective prevalent failure times are conditioned upon and cannot
participate in estimating the disease-onset age distribution. An alternative
approach conditions just on survival up to recruitment age, plus the
recruitment age itself. This approach allows incorporating the prevalent
information but brings about numerical and computational difficulties. In this
work we develop consistent estimators of the coefficients in a regression model
for the age-at-onset, while utilizing the prevalent data. Asymptotic results
are provided, and simulations are conducted to showcase the substantial
efficiency gain that may be obtained by the proposed approach. In particular,
the method is highly useful in leveraging large-scale repositories for
replicability analysis of genetic variants. Indeed, analysis of urinary bladder
cancer data reveals that the proposed approach yields about twice as many
replicated discoveries compared to the popular approach
Optimal Cox Regression Subsampling Procedure with Rare Events
Massive sized survival datasets are becoming increasingly prevalent with the
development of the healthcare industry. Such datasets pose computational
challenges unprecedented in traditional survival analysis use-cases. A popular
way for coping with massive datasets is downsampling them to a more manageable
size, such that the computational resources can be afforded by the researcher.
Cox proportional hazards regression has remained one of the most popular
statistical models for the analysis of survival data to-date. This work
addresses the settings of right censored and possibly left truncated data with
rare events, such that the observed failure times constitute only a small
portion of the overall sample. We propose Cox regression subsampling-based
estimators that approximate their full-data partial-likelihood-based
counterparts, by assigning optimal sampling probabilities to censored
observations, and including all observed failures in the analysis. Asymptotic
properties of the proposed estimators are established under suitable regularity
conditions, and simulation studies are carried out to evaluate the finite
sample performance of the estimators. We further apply our procedure on
UK-biobank colorectal cancer genetic and environmental risk factors
- …