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.

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.

Joint Modeling of Multivariate Survival Data With an Application to Retirement

2020 ◽  
pp. 004912412091492
Author(s):  
Grace Li ◽  
Mary Lesperance ◽  
Zheng Wu
Keyword(s):  
Survival Data ◽  
Recurrent Events ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Joint Modeling ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Event Analysis ◽  
Multiple Spells

The Cox proportional hazards model has been pervasively used in many social science areas to examine the effects of covariates on timing to an event. The standard Cox model is intended to study univariate survival data where there is a singular event of interest, which can only be experienced once. However, we may additionally wish to explore a number of other complexities that are prevalent in survival data. For example, an individual may experience events of the same type more than once or may experience multiple types of events. This study introduces innovations in recurrent (repeatable) event analysis, jointly modeling several endogenous survival processes. As an example and an application, we simultaneously model two types of recurrent events in the presence of a dependent terminal event. This model not only correctly handles different types of recurrent events but also explicitly estimates the direction and magnitude of relationships between recurrences and survival. This article concludes with an example of the model to examine how the timing of retirement is associated with the risks of multiple spells of employment and childbearing. The theoretical discussions and empirical analyses suggest that the multivariate joint models have much to offer to a wide variety of substantive research areas.

The Comparison of the Estimator of the Variance in the Cox Proportional Hazards Model

2014 ◽  
Vol 1049-1050 ◽  
pp. 1613-1616
Author(s):  
Yong Li
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model

The Cox model is commonly used to model survival data as a function of covariates. In this paper we compare the three methods to estimate the variance of the parameters in Cox model and presents the simulation result.

Simulating Duration Data for the Cox Model

2018 ◽  
Vol 7 (04) ◽  
pp. 921-928 ◽  
Author(s):  
Jeffrey J. Harden ◽  
Jonathan Kropko
Keyword(s):  
Hazard Function ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Simulation Studies ◽  
Baseline Hazard ◽  
Baseline Hazard Function ◽  
Time Varying Covariates

The Cox proportional hazards model is a popular method for duration analysis that is frequently the subject of simulation studies. However, no standard method exists for simulating durations directly from its data generating process because it does not assume a distributional form for the baseline hazard function. Instead, simulation studies typically rely on parametric survival distributions, which contradicts the primary motivation for employing the Cox model. We propose a method that generates a baseline hazard function at random by fitting a cubic spline to randomly drawn points. Durations drawn from this function match the Cox model’s inherent flexibility and improve the simulation’s generalizability. The method can be extended to include time-varying covariates and non-proportional hazards.

Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models

2010 ◽  
Vol 18 (2) ◽  
pp. 189-205 ◽  
Author(s):  
Luke Keele
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Standard Test ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Cox Models ◽  
Nonproportional Hazards ◽  
Specification Errors ◽  
Hazards Model

The Cox proportional hazards model is widely used to model durations in the social sciences. Although this model allows analysts to forgo choices about the form of the hazard, it demands careful attention to the proportional hazards assumption. To this end, a standard diagnostic method has been developed to test this assumption. I argue that the standard test for nonproportional hazards has been misunderstood in current practice. This test detects a variety of specification errors, and these specification errors must be corrected before one can correctly diagnose nonproportionality. In particular, unmodeled nonlinearity can appear as a violation of the proportional hazard assumption for the Cox model. Using both simulation and empirical examples, I demonstrate how an analyst might be led astray by incorrectly applying the nonproportionality test.

scholarly journals Model Selection Strategy for Cox Proportional Hazards Model

2019 ◽  
Vol 67 (2) ◽  
pp. 111-116
Author(s):  
Fabiha Binte Farooq ◽  
Md Jamil Hasan Karami
Keyword(s):  
Model Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Information Criterion ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Outcome Variable ◽  
Selection Strategy ◽  
True Model

Often in survival regression modelling, not all predictors are relevant to the outcome variable. Discarding such irrelevant variables is very crucial in model selection. In this research, under Cox Proportional Hazards (PH) model we study different model selection criteria including Stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and the extended versions of AIC and BIC to the Cox model. The simulation study shows that varying censoring proportions and correlation coefficients among the covariates have great impact on the performances of the criteria to identify a true model. In the presence of high correlation among the covariates, the success rate for identifying the true model is higher for LASSO compared to other criteria. The extended version of BIC always shows better result than the traditional BIC. We have also applied these techniques to real world data. Dhaka Univ. J. Sci. 67(2): 111-116, 2019 (July)

scholarly journals Assessment of preparation time and 1-year Invisalign aligner attachment survival using flowable and packable composites:

2021 ◽  
Author(s):  
Shuang Lin ◽  
Ling Huang ◽  
Jialing Li ◽  
Juan Wen ◽  
Li Mei ◽  
...  
Keyword(s):  
Survival Data ◽  
Tooth Extraction ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Secondary Outcome ◽  
Preparation Time ◽  
Cox Models ◽  
Significant Difference

ABSTRACT Objectives To compare preparation time and 1-year Invisalign aligner attachment survival between a flowable composite (FC) and a packable composite (PC). Materials and Methods Fifty-five participants (13 men and 42 women, mean age ± SD: 24.2 ± 5.9 years) were included in the study. Ipsilateral quadrants (ie, maxillary and mandibular right, or vice versa) of attachments were randomly assigned to the FC group (Filtek Z350XT Flowable Restorative) and the PC group (Filtek Z350XT Universal Restorative) by tossing a coin. The primary outcome was preparation time. The secondary outcome was time to the first damage of an attachment. Preparation times were compared using the paired t-test, and the survival data were analyzed by the Cox proportional hazards model with a shared frailty term, with α = .05. Results The preparation times were significantly shorter with the FC (6.22 ± 0.22 seconds per attachment) than with the PC (32.83 ± 2.16 seconds per attachment; P < .001). The attachment damage rates were 14.79% for the FC and 9.70% for the PC. According to the Cox models, attachment damage was not significantly affected by the attachment material, sex, arch, tooth location, attachment type, presence of overbite, or occurrence of tooth extraction. Conclusions The use of a FC may save time as compared with the use of a PC. With regard to attachment survival, there was no significant difference between the two composites. None of the covariates of attachment materials (sex, arch, tooth location, attachment type, presence of overbite, oir occurrence of tooth extraction) affected attachment damage.

A retrospective analysis of all non-AIDS defining cancers (NADC): A subset analysis of the County Hospital AIDS Malignancy Project (CHAMP).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1611-1611
Author(s):  
Shweta Gupta ◽  
Prantesh Jain ◽  
Emilio Araujo Mino ◽  
Audrey L. French ◽  
Fred R Rosen ◽  
...  
Keyword(s):  
Inner City ◽  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Cd4 Count ◽  
County Hospital ◽  
Male Population ◽  
Poorly Differentiated

1611 Background: County Hospital (CCH) with its HIV clinic, the CORE Center (CC) is the largest provider for HIV patients (pts) in Chicago, treating over 5,500 HIV pts yearly. There is paucity of data on characteristics of HIV+ cancers (ca) in the inner city. The CHAMP cohort is a retrospective study of all HIV associated cancers at CC and CCH over past 14 years (yrs). We analyzed all of the NADC from this cohort. Methods: All HIV pts with NADC were identified from the CHAMP cohort and retrospectively reviewed for HIV and cancer characteristics, overall survival (OS), and pt demographics. Statistics: Survival data was analyzed using Kaplan-Meier analysis and Cox Proportional Hazards model. Results: Of 438 pts identified, 157 were NADC representing 21 ca. The average (ave) age was 48 yrs (range 44-57), with prostate ca having highest age presentation. Over the past 10 yrs, the number of NADC has risen from 10 to over 20 each yr. Unlike historical controls (HC) where lung ca is most common, anal ca (21%) was most frequent followed by lung ca (17%). Prostate, head and neck (HNSCC), liver, and colorectal ca were seen in 9, 9, 8, and 7% respectively. 65% of pts were African Americans (AA) and 18% Caucasians. 78% of all NADC were men. 45% of anal ca present with stage IIIa/b disease, moderately to poorly differentiated ca in 48%, with a median OS of 34 mo. CD4 count did not alter OS but stage predicted better outcomes. 86% lung ca presented as stage III/IV disease with ave CD4 count 204. Histologically, 36% were SCC, 28% adenosquamous and 20% adenocarcinoma. OS was 5.5 mo and did not change by histology, CD4, or age. 68% HNSCC present with stage IVa/b but no IVc. Ave age was 48 yrs with an OS of 18mo. 50% were oropharyngeal compared to 22% in HC. Conclusions: Based on data by Sheilds et. al, CCH treats just over 1% of the country’s NADC population. We demonstrate a higher incidence of NADC over time, dominated by a younger, AA and male population. Each ca presents with advanced stage 45-86% and poorly differentiated tumors ranging from 15-30%. The OS of each cancer is consistent with HC with exception of HNSCC. As HIV pts age becoming prone to cancers of elderly, education and screening of inner city HIV pts will help improve cancer rates.

Regression standardization and attributable fraction estimation with between-within frailty models for clustered survival data

2017 ◽  
Vol 28 (2) ◽  
pp. 462-485 ◽  
Author(s):  
Elisabeth Dahlqwist ◽  
Yudi Pawitan ◽  
Arvid Sjölander
Keyword(s):  
Survival Time ◽  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Frailty Model ◽  
Cox Proportional Hazards ◽  
Attributable Fraction ◽  
Cox Proportional Hazards Model ◽  
Frailty Models ◽  
Analysis Tool

The between-within frailty model has been proposed as a viable analysis tool for clustered survival time outcomes. Previous research has shown that this model gives consistent estimates of the exposure–outcome hazard ratio in the presence of unmeasured cluster-constant confounding, which the ordinary frailty model does not, and that estimates obtained from the between-within frailty model are often more efficient than estimates obtained from the stratified Cox proportional hazards model. In this paper, we derive novel estimation techniques for regression standardization with between-within frailty models. We also show how between-within frailty models can be used to estimate the attributable fraction function, which is a generalization of the attributable fraction for survival time outcomes. We illustrate the proposed methods by analyzing a large cohort on preterm birth and attention deficit hyperactivity disorder. To facilitate use of the proposed methods, we provide R code for all analyses.

scholarly journals Combining Symbolic Regression with the Cox Proportional Hazards Model Improves Prediction of Heart Failure Deaths

2021 ◽  
Author(s):  
Casper Wilstrup ◽  
Chris Cave
Keyword(s):  
Heart Failure ◽  
Medical Records ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Symbolic Regression ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Hazards Model

Abstract Background: Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone. Methods: We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified a minimal set of mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.Results: An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations. Conclusion: Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.

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