Competing‐risks model for predicting the prognosis of penile cancer based on the SEER database
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DOI: 10.1002/cam4.2649
Abstract: This study performed a competing‐risks analysis using data from the SEER database on penile cancer patients with the aim of identifying more accurate prognostic factors. read more here.
Keywords: cancer; penile cancer; competing risks; seer database ... See more keywords
Analyzing semi-competing risks data with missing cause of informative terminal event.
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DOI: 10.1002/sim.7161
Abstract: Cancer studies frequently yield multiple event times that correspond to landmarks in disease progression, including non-terminal events (i.e., cancer recurrence) and an informative terminal event (i.e., cancer-related death). Hence, we often observe semi-competing risks data.… read more here.
Keywords: informative terminal; cause; semi competing; competing risks ... See more keywords
Propensity‐score matching with competing risks in survival analysis
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DOI: 10.1002/sim.8008
Abstract: Propensity‐score matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time‐to‐event outcomes are common in medical research.… read more here.
Keywords: score matching; propensity score; competing risks; matching competing ... See more keywords
Two-stage residual inclusion for survival data and competing risks-An instrumental variable approach with application to SEER-Medicare linked data.
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DOI: 10.1002/sim.8071
Abstract: Instrumental variable is an essential tool for addressing unmeasured confounding in observational studies. Two-stage predictor substitution (2SPS) estimator and two-stage residual inclusion (2SRI) are two commonly used approaches in applying instrumental variables. Recently, 2SPS was… read more here.
Keywords: two stage; instrumental variable; stage residual; residual inclusion ... See more keywords
Variable selection in competing risks models based on quantile regression.
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DOI: 10.1002/sim.8326
Abstract: The proportional subdistribution hazard regression model has been widely used by clinical researchers for analyzing competing risks data. It is well known that quantile regression provides a more comprehensive alternative to model how covariates influence… read more here.
Keywords: selection competing; quantile regression; regression; competing risks ... See more keywords
Finite-sample adjustments in variance estimators for clustered competing risks regression.
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DOI: 10.1002/sim.9375
Abstract: The marginal Fine-Gray proportional subdistribution hazards model is a popular approach to directly study the association between covariates and the cumulative incidence function with clustered competing risks data, which often arise in multicenter randomized trials… read more here.
Keywords: variance; competing risks; clustered competing; variance estimator ... See more keywords
Fast Lasso‐type safe screening for Fine‐Gray competing risks model with ultrahigh dimensional covariates
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DOI: 10.1002/sim.9545
Abstract: The Fine‐Gray proportional sub‐distribution hazards (PSH) model is among the most popular regression model for competing risks time‐to‐event data. This article develops a fast safe feature elimination method, named PSH‐SAFE, for fitting the penalized Fine‐Gray… read more here.
Keywords: psh safe; fine gray; model; competing risks ... See more keywords
Marginal semiparametric transformation models for clustered multivariate competing risks data
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DOI: 10.1002/sim.9573
Abstract: Multivariate survival models are often used in studying multiple outcomes for right‐censored data. However, the outcomes of interest often have competing risks, where standard multivariate survival models may lead to invalid inferences. For example, patients… read more here.
Keywords: marginal semiparametric; risks data; multivariate competing; competing risks ... See more keywords
Absolute and relative risk estimation in the presence of outcome ascertainment gaps and competing risks
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DOI: 10.1002/sim.9668
Abstract: Incomplete coverage by cancer registries can lead to an underreporting of cancers and a resulting bias in risk estimates. When registries are defined by geographic region, gaps in observation can arise for individuals who reside… read more here.
Keywords: outcome ascertainment; relative risk; absolute relative; risk ... See more keywords
New C‐indices for assessing importance of longitudinal biomarkers in fitting competing risks survival data in the presence of partially masked causes
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DOI: 10.1002/sim.9671
Abstract: Competing risks survival data in the presence of partially masked causes are frequently encountered in medical research or clinical trials. When longitudinal biomarkers are also available, it is of great clinical importance to examine associations… read more here.
Keywords: survival data; masked causes; data presence; risks survival ... See more keywords
Variable selection with group structure: exiting employment at retirement age—a competing risks quantile regression analysis
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DOI: 10.1007/s00181-020-01918-z
Abstract: We consider the exit routes of older employees out of employment around retirement age. Our administrative data cover weekly information about the Danish population from 2004 to 2016 and 397 variables from 16 linked administrative… read more here.
Keywords: risks quantile; competing risks; quantile regression; employment ... See more keywords