scholarly journals Cox proportional hazards models with left truncation and time-varying coefficient: Application of age at event as outcome in cohort studies

2017 ◽  
Vol 59 (3) ◽  
pp. 405-419 ◽  
Author(s):  
Minjin Kim ◽  
Myunghee Cho Paik ◽  
Jiyeong Jang ◽  
Ying K. Cheung ◽  
Joshua Willey ◽  
...  
Keyword(s):  
Cohort Studies ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Time Varying ◽  
Left Truncation ◽  
Proportional Hazards Models ◽  
Varying Coefficient ◽  
Cox Proportional Hazards Models

scholarly journals Fitting Marginal Structural and G-Estimation Models Under Complex Treatment Patterns: Investigating the Association Between De Novo Vitamin D Supplement Use After Breast Cancer Diagnosis and All-Cause Mortality Using Linked Pharmacy Claim and Registry Data

2019 ◽  
Vol 189 (3) ◽  
pp. 224-234
Author(s):  
Jamie M Madden ◽  
Finbarr P Leacy ◽  
Lina Zgaga ◽  
Kathleen Bennett
Keyword(s):  
Vitamin D ◽  
Cancer Diagnosis ◽  
Proportional Hazards ◽  
De Novo ◽  
Breast Cancer Diagnosis ◽  
Cox Proportional Hazards ◽  
Time Varying ◽  
Proportional Hazards Models ◽  
Cox Proportional Hazards Models ◽  
All Cause Mortality

Abstract Studies have shown that accounting for time-varying confounding through time-dependent Cox proportional hazards models may provide biased estimates of the causal effect of treatment when the confounder is also a mediator. We explore 2 alternative approaches to addressing this problem while examining the association between vitamin D supplementation initiated after breast cancer diagnosis and all-cause mortality. Women aged 50–80 years were identified in the National Cancer Registry Ireland (n = 5,417) between 2001 and 2011. Vitamin D use was identified from linked prescription data (n = 2,570). We sought to account for the time-varying nature of vitamin D use and time-varying confounding by bisphosphonate use using 1) marginal structural models (MSMs) and 2) G-estimation of structural nested accelerated failure-time models (SNAFTMs). Using standard adjusted Cox proportional hazards models, we found a reduction in all-cause mortality in de novo vitamin D users compared with nonusers (hazard ratio (HR) = 0.84, 95% confidence interval (CI): 0.73, 0.99). Additional adjustment for vitamin D and bisphosphonate use in the previous month reduced the hazard ratio (HR = 0.45, 95% CI: 0.33, 0.63). Results derived from MSMs (HR = 0.44, 95% CI: 0.32, 0.61) and SNAFTMs (HR = 0.45, 95% CI: 0.34, 0.52) were similar. Utilizing MSMs and SNAFTMs to account for time-varying bisphosphonate use did not alter conclusions in this example.

scholarly journals Generating Survival Times Using Cox Proportional Hazards Models with Cyclic and Piecewise Time-Varying Covariates

2020 ◽  
Vol 12 (3) ◽  
pp. 324-339 ◽  
Author(s):  
Yunda Huang ◽  
Yuanyuan Zhang ◽  
Zong Zhang ◽  
Peter B. Gilbert
Keyword(s):  
Survival Data ◽  
Dose Regimen ◽  
Hazard Function ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Time Varying ◽  
Proportional Hazards Models ◽  
Cox Proportional Hazards Models ◽  
Time Varying Covariates

Abstract Time-to-event outcomes with cyclic time-varying covariates are frequently encountered in biomedical studies that involve multiple or repeated administrations of an intervention. In this paper, we propose approaches to generating event times for Cox proportional hazards models with both time-invariant covariates and a continuous cyclic and piecewise time-varying covariate. Values of the latter covariate change over time through cycles of interventions and its relationship with hazard differs before and after a threshold within each cycle. The simulations of data are based on inverting the cumulative hazard function and a log link function for relating the hazard function to the covariates. We consider closed-form derivations with the baseline hazard following the exponential, Weibull, or Gompertz distribution. We propose two simulation approaches: one based on simulating survival data under a single-dose regimen first before data are aggregated over multiple-dosing cycles and another based on simulating survival data directly under a multiple-dose regimen. We consider both fixed intervals and varying intervals of the drug administration schedule. The method’s validity is assessed in simulation experiments. The results indicate that the proposed procedures perform well in generating data that conform to their cyclic nature and assumptions of the Cox proportional hazards model.

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