Exposure assessment for Cox proportional hazards cure models with interval‐censored survival data

2021 ◽  
Author(s):  
Wei Wang ◽  
Ning Cong ◽  
Aijun Ye ◽  
Hui Zhang ◽  
Bo Zhang
Keyword(s):  
Exposure Assessment ◽  
Survival Data ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Censored Survival Data ◽  
Cure Models ◽  
Interval Censored

Survival analysis via cox proportional hazards additive models

2017 ◽  
Vol 01 (01) ◽  
pp. 1650003
Author(s):  
Lu Bai ◽  
Daniel Gillen
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Generalized Additive Models ◽  
Cox Proportional Hazards ◽  
Additive Models ◽  
Cox Proportional Hazards Model ◽  
Linear Predictor ◽  
Censored Survival Data

The Cox proportional hazards model is commonly used to examine the covariate-adjusted association between a predictor of interest and the risk of mortality for censored survival data. However, it assumes a parametric relationship between covariates and mortality risk though a linear predictor. Generalized additive models (GAMs) provide a flexible extension of the usual linear model and are capable of capturing nonlinear effects of predictors while retaining additivity between the predictor effects. In this paper, we provide a review of GAMs and incorporate bivariate additive modeling into the Cox model for censored survival data with applications to estimating geolocation effects on survival in spatial epidemiologic studies.

scholarly journals 1388. Dose Discrimination for ASN100: Bridging from Rabbit Survival Data to Predicted Activity in Humans Using a Minimal Physiologically Based Pharmacokinetic (mPBPK) Model

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S426-S426
Author(s):  
Christopher M Rubino ◽  
Lukas Stulik ◽  
Harald Rouha ◽  
Zehra Visram ◽  
Adriana Badarau ◽  
...  
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Virulent Strain ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Simulated Patients ◽  
Research Support ◽  
Mpbpk Model

Abstract Background ASN100 is a combination of two co-administered fully human monoclonal antibodies (mAbs), ASN-1 and ASN-2, that together neutralize the six cytotoxins critical to S. aureus pneumonia pathogenesis. ASN100 is in development for prevention of S. aureus pneumonia in mechanically ventilated patients. A pharmacometric approach to dose discrimination in humans was taken in order to bridge from dose-ranging, survival studies in rabbits to anticipated human exposures using a mPBPK model derived from data from rabbits (infected and noninfected) and noninfected humans [IDWeek 2017, Poster 1849]. Survival in rabbits was assumed to be indicative of a protective effect through ASN100 neutralization of S. aureus toxins. Methods Data from studies in rabbits (placebo through 20 mg/kg single doses of ASN100, four strains representing MRSA and MSSA isolates with different toxin profiles) were pooled with data from a PK and efficacy study in infected rabbits (placebo and 40 mg/kg ASN100) [IDWeek 2017, Poster 1844]. A Cox proportional hazards model was used to relate survival to both strain and mAb exposure. Monte Carlo simulation was then applied to generate ASN100 exposures for simulated patients given a range of ASN100 doses and infection with each strain (n = 500 per scenario) using a mPBPK model. Using the Cox model, the probability of full protection from toxins (i.e., predicted survival) was estimated for each simulated patient. Results Cox models showed that survival in rabbits is dependent on both strain and ASN100 exposure in lung epithelial lining fluid (ELF). At human doses simulated (360–10,000 mg of ASN100), full or substantial protection is expected for all four strains tested. For the most virulent strain tested in the rabbit pneumonia study (a PVL-negative MSSA, Figure 1), the clinical dose of 3,600 mg of ASN100 provides substantially higher predicted effect relative to lower doses, while doses above 3,600 mg are not predicted to provide significant additional protection. Conclusion A pharmacometric approach allowed for the translation of rabbit survival data to infected patients as well as discrimination of potential clinical doses. These results support the ASN100 dose of 3,600 mg currently being evaluated in a Phase 2 S. aureus pneumonia prevention trial. Disclosures C. M. Rubino, Arsanis, Inc.: Research Contractor, Research support. L. Stulik, Arsanis Biosciences GmbH: Employee, Salary. H. Rouha, 3Arsanis Biosciences GmbH: Employee, Salary. Z. Visram, Arsanis Biosciences GmbH: Employee, Salary. A. Badarau, Arsanis Biosciences GmbH: Employee, Salary. S. A. Van Wart, Arsanis, Inc.: Research Contractor, Research support. P. G. Ambrose, Arsanis, Inc.: Research Contractor, Research support. M. M. Goodwin, Arsanis, Inc.: Employee, Salary. E. Nagy, Arsanis Biosciences GmbH: Employee, Salary.

P1810 Impact of pulmonary hypertension on survival in patients with degenerative aortic stenosis, prospective clinical trial

2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
D Mercea ◽  
R Ianos ◽  
C Pop ◽  
D Pop ◽  
D Zdrenghea ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Survival Rate ◽  
Aortic Stenosis ◽  
Survival Data ◽  
Proportional Hazards ◽  
Negative Impact ◽  
Severe Aortic Stenosis ◽  
Cox Proportional Hazards ◽  
Logrank Test ◽  
Pulmonary Pressure

Abstract Introduction Aortic stenosis has become the most common degenerative valvular heart disease, due to aging of the population. It represents 43% of degenerative valvular disease. Methods We prospectively followed 196 patients with degenerative aortic stenosis, for a period of 6 years. Of these, 106 had mild aortic stenosis, 28 had moderate aortic stenosis and 62 patients had severe aortic stenosis. Of the 196 patients, 54 had mild pulmonary hypertension (PH), 22 had moderate PH and 7 had severe PH. The survival data were estimated by the Kaplan - Meier method and the logrank test. The Cox proportional- hazards regression was performed to assess the differences between the groups. Results During the follow-up period, of a total of 196 patients, 61 patients died. We studied if the presence of PH has an influence on survival in patients with aortic stenosis and we found out that the patients who had PH had reduced survival rates, compared with those who hadn"t. The survival rate at 5 years was lower in the group with mild PH compared with the group with normal pulmonary pressure ( p = 0.045, HR 1.84, 95%CI 1.01-3.36). In the group with mild PH, 20 patients (37.03%) died at 5 years, compared with 23 in the group without PH (20.35%). The survival rate at 5 years was also lower in the group with moderate PH compared with the group with normal pulmonary pressure ( p = 0.001, HR 5.82, 95% CI 3.02-11.22). At 5 years, 15 patients with moderate PH died ( 68.18% compared with 20.35 in the group without PH). Comparing the group with severe PH with the group with normal pulmonary pressure the results showed a reduced survival rate in those with severe pulmonary hypertension ( p = 0.139, HR 2.48, 95% CI 0.74-8.27). At 5 years, 3 patients with severe PH died (42.85% compared with 20.35 in the group without PH). Conclusion The presence of PH in patients with degenerative aortic stenosis has a negative impact on survival, highly statistically significant (p <0.001, HR 1.03, 95% CI, 1.02-1.04). Given these findings, perhaps an increased attention should be paid to the treatment of the patients with PH compared to those without PH.

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