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 Eosinophil count and mortality risk in incident hemodialysis patients

2020 ◽  
Vol 35 (6) ◽  
pp. 1032-1042
Author(s):  
Duk-Hee Kang ◽  
Yuji Lee ◽  
Carola Ellen Kleine ◽  
Yong Kyu Lee ◽  
Christina Park ◽  
...  
Keyword(s):  
Mortality Risk ◽  
Eosinophil Count ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Time Varying ◽  
Cox Proportional Hazards Models ◽  
Lower Baseline ◽  
All Cause Mortality ◽  
Relationship Of ◽  
The Relationship

Abstract Background Eosinophils are traditionally known as moderators of allergic reactions; however, they have now emerged as one of the principal immune-regulating cells as well as predictors of vascular disease and mortality in the general population. Although eosinophilia has been demonstrated in hemodialysis (HD) patients, associations of eosinophil count (EOC) and its changes with mortality in HD patients are still unknown. Methods In 107 506 incident HD patients treated by a large dialysis organization during 2007–11, we examined the relationships of baseline and time-varying EOC and its changes (ΔEOC) over the first 3 months with all-cause mortality using Cox proportional hazards models with three levels of hierarchical adjustment. Results Baseline median EOC was 231 (interquartile range 155–339) cells/μL and eosinophilia (>350 cells/μL) was observed in 23.4% of patients. There was a gradual increase in EOC over time after HD initiation with a median ΔEOC of 5.1 (IQR −53–199) cells/μL, which did not parallel the changes in white blood cell count. In fully adjusted models, mortality risk was highest in subjects with lower baseline and time-varying EOC (<100 cells/μL) and was also slightly higher in patients with higher levels (≥550 cells/μL), resulting in a reverse J-shaped relationship. The relationship of ΔEOC with all-cause mortality risk was also a reverse J-shape where both an increase and decrease exhibited a higher mortality risk. Conclusions Both lower and higher EOCs and changes in EOC over the first 3 months after HD initiation were associated with higher all-cause mortality in incident HD patients.

scholarly journals Absence of Obesity Paradox in All-Cause Mortality Among Chinese Patients With an Implantable Cardioverter Defibrillator: A Multicenter Cohort Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Bin Zhou ◽  
Xuerong Sun ◽  
Na Yu ◽  
Shuang Zhao ◽  
Keping Chen ◽  
...  
Keyword(s):  
Implantable Cardioverter Defibrillator ◽  
Proportional Hazards ◽  
Obesity Paradox ◽  
Cox Proportional Hazards ◽  
Chinese Patients ◽  
Proportional Hazards Models ◽  
Cardioverter Defibrillator ◽  
Kaplan Meier ◽  
Cox Proportional Hazards Models ◽  
All Cause Mortality

Background: The results of studies on the obesity paradox in all-cause mortality are inconsistent in patients equipped with an implantable cardioverter-defibrillator (ICD). There is a lack of relevant studies on Chinese populations with large sample size. This study aimed to investigate whether the obesity paradox in all-cause mortality is present among the Chinese population with an ICD.Methods: We conducted a retrospective analysis of multicenter data from the Study of Home Monitoring System Safety and Efficacy in Cardiac Implantable Electronic Device–implanted Patients (SUMMIT) registry in China. The outcome was all-cause mortality. The Kaplan–Meier curves, Cox proportional hazards models, and smooth curve fitting were used to investigate the association between body mass index (BMI) and all-cause mortality.Results: After inclusion and exclusion criteria, 970 patients with an ICD were enrolled. After a median follow-up of 5 years (interquartile, 4.1–6.0 years), in 213 (22.0%) patients occurred all-cause mortality. According to the Kaplan–Meier curves and multivariate Cox proportional hazards models, BMI had no significant impact on all-cause mortality, whether as a continuous variable or a categorical variable classified by various BMI categorization criteria. The fully adjusted smoothed curve fit showed a linear relationship between BMI and all-cause mortality (p-value of 0.14 for the non-linearity test), with the curve showing no statistically significant association between BMI and all-cause mortality [per 1 kg/m2 increase in BMI, hazard ratio (HR) 0.97, 95% CI 0.93–1.02, p = 0.2644].Conclusions: The obesity paradox in all-cause mortality was absent in the Chinese patients with an ICD. Prospective studies are needed to further explore this phenomenon.

scholarly journals The U-Shape Relationship Between Glycated Hemoglobin Level and Long-Term All-Cause Mortality Among Patients With Coronary Artery Disease

2021 ◽  
Vol 8 ◽  
Author(s):  
Liwei Liu ◽  
Jianfeng Ye ◽  
Ming Ying ◽  
Qiang Li ◽  
Shiqun Chen ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Proportional Hazards ◽  
Glycated Hemoglobin ◽  
Cox Proportional Hazards ◽  
Proportional Hazards Models ◽  
Cox Proportional Hazards Models ◽  
Risk Of Mortality ◽  
All Cause Mortality ◽  
Artery Disease

Background: Although glycated hemoglobin (HbA1c) was considered as a prognostic factor in some subgroup of coronary artery disease (CAD), the specific relationship between HbA1c and the long-term all-cause death remains controversial in patients with CAD.Methods: The study enrolled 37,596 CAD patients and measured HbAlc at admission in Guangdong Provincial People's Hospital. The patients were divided into 4 groups according to HbAlc level (Quartile 1: HbA1c ≤ 5.7%; Quartile 2: 5.7% < HbA1c ≤ 6.1%; Quartile 3: 6.1% < HbA1c ≤ 6.7%; Quartile 4: HbA1c > 6.7%). The study endpoint was all-cause death. The restricted cubic splines and cox proportional hazards models were used to investigate the association between baseline HbAlc levels and long-term all-cause mortality.Results: The median follow-up was 4 years. The cox proportional hazards models revealed that HbAlc is an independent risk factor in the long-term all-cause mortality. We also found an approximate U-shape association between HbA1c and the risk of mortality, including increased risk of mortality when HbA1c ≤ 5.7% and HbA1c > 6.7% [Compared with Quartile 2, Quartile 1 (HbA1c ≤ 5.7), aHR = 1.13, 95% CI:1.01–1.26, P < 0.05; Quartile 3 (6.1% < HbA1c ≤ 6.7%), aHR = 1.04, 95% CI:0.93–1.17, P =0.49; Quartile 4 (HbA1c > 6.7%), aHR = 1.32, 95% CI:1.19–1.47, P < 0.05].Conclusions: Our study indicated a U-shape relationship between HbA1c and long-term all-cause mortality in CAD patients.

scholarly journals Lower values of a novel index of Vagal-Neuroimmunomodulation are associated to higher all-cause mortality in two large general population samples with 18 year follow up

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marc N. Jarczok ◽  
Julian Koenig ◽  
Julian F. Thayer
Keyword(s):  
General Population ◽  
Proportional Hazards ◽  
Population Studies ◽  
Large Population ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Proportional Hazards Models ◽  
Cox Proportional Hazards Models ◽  
All Cause Mortality

AbstractIn recent clinical practice, a biomarker of vagal neuroimmunomodulation (NIM), namely the ratio of vagally-mediated heart rate variability (vmHRV) and CRP, was proposed to index the functionality of the cholinergic anti-inflammatory pathway. This study aims to transfer and extend the previous findings to two general population-based samples to explore the hypothesis that NIM-ratio is associated with all-cause mortality. Two large population studies (MIDUS 2: N = 1255 and Whitehall II wave 5: N = 7870) with complete data from a total of N = 3860 participants (36.1% females; average age = 56.3 years; 11.1% deaths, last exit 18.1 years post inclusion) were available. NIM indices were calculated using the vagally-mediated HRV measure RMSSD divided by measures of CRP (NIMCRP) or IL-6 (NIMIL6). The NIM-ratios were quartiled and entered into age, ethnicity and body mass index adjusted Cox proportional hazards models. For NIMIL6 the lowest quartile was 45% more likely to die during the observed period (max. 18 years follow-up) compared to the highest quartile (HR = 0.55 CI 0.41–0.73; p < .0001). NIMCRP parallel these results. Here we show that an easily computable index of IL-6 inhibition is associated with all-cause mortality in two large general population samples. These results suggest that this index might be useful for risk stratification and warrant further examination.

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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