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 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.

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 Clinical predictors of symptom resolution for children and adolescents with sport-related concussion

2019 ◽  
Vol 24 (1) ◽  
pp. 54-61 ◽  
Author(s):  
David R. Howell ◽  
Morgan N. Potter ◽  
Michael W. Kirkwood ◽  
Pamela E. Wilson ◽  
Aaron J. Provance ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Outcome Variable ◽  
Duration Of Symptoms ◽  
Symptom Resolution ◽  
Hazards Model ◽  
Romberg Test ◽  
Abnormal Performance

OBJECTIVEThe goal of this study was to determine which variables assessed during an initial clinical evaluation for concussion are independently associated with time until symptom resolution among pediatric patients.METHODSData collected from a prospective clinical registry of pediatric patients with concussion were analyzed. The primary outcome variable was time from injury until symptom resolution. Predictor variables assessed within 10 days after injury included preinjury factors, Health and Behavior Inventory scores, headache severity, and balance, vestibular, and oculomotor test performances. The researchers used univariate Cox proportional models to identify potential predictors of symptom resolution time and constructed a multivariate Cox proportional hazards model in which total duration of concussion symptoms remained the outcome variable.RESULTSThe sample consisted of 351 patients (33% female, mean age 14.6 ± 2.2 years, evaluated 5.6 ± 2.6 days after concussion). Univariate Cox proportional hazards models indicated that several variables were associated with a longer duration of symptoms, including headache severity (hazard ratio [HR] 0.90 [95% CI 0.85–0.96]), headache frequency (HR 0.83 [95% CI 0.71–0.96]), confusion (HR 0.79 [95% CI 0.69–0.92]), forgetfulness (HR 0.79 [95% CI 0.68–0.92]), attention difficulties (HR 0.83 [95% CI 0.72–0.96]), trouble remembering (HR 0.84 [95% CI 0.72–0.98]), getting tired often (HR 0.86 [95% CI 0.76–0.97]), getting tired easily (HR 0.86 [95% CI 0.76–0.98]), dizziness (HR 0.86 [95% CI 0.75–0.99]), and abnormal performance on the Romberg test (HR 0.59 [95% CI 0.40–0.85]). A multivariate Cox proportional hazards model indicated that an abnormal performance on the Romberg test was independently associated with a longer duration of symptoms (HR 0.65 [95% CI 0.44–0.98]; p = 0.038).CONCLUSIONSFor children and adolescents evaluated within 10 days after receiving a concussion, abnormal performance on the Romberg test was independently associated with a longer duration of symptoms during recovery. In line with findings of other recent studies investigating predictors of symptom resolution, postural stability tests may provide useful prognostic information for sports medicine clinicians.

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.

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 Survival time prediction by integrating cox proportional hazards network and distribution function network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eu-Tteum Baek ◽  
Hyung Jeong Yang ◽  
Soo Hyung Kim ◽  
Guee Sang Lee ◽  
In-Jae Oh ◽  
...  
Keyword(s):  
Distribution Function ◽  
Survival Time ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Hazard Ratio ◽  
Cox Proportional Hazards Model ◽  
Specific Distribution ◽  
Function Network

Abstract Background The Cox proportional hazards model is commonly used to predict hazard ratio, which is the risk or probability of occurrence of an event of interest. However, the Cox proportional hazard model cannot directly generate an individual survival time. To do this, the survival analysis in the Cox model converts the hazard ratio to survival times through distributions such as the exponential, Weibull, Gompertz or log-normal distributions. In other words, to generate the survival time, the Cox model has to select a specific distribution over time. Results This study presents a method to predict the survival time by integrating hazard network and a distribution function network. The Cox proportional hazards network is adapted in DeepSurv for the prediction of the hazard ratio and a distribution function network applied to generate the survival time. To evaluate the performance of the proposed method, a new evaluation metric that calculates the intersection over union between the predicted curve and ground truth was proposed. To further understand significant prognostic factors, we use the 1D gradient-weighted class activation mapping method to highlight the network activations as a heat map visualization over an input data. The performance of the proposed method was experimentally verified and the results compared to other existing methods. Conclusions Our results confirmed that the combination of the two networks, Cox proportional hazards network and distribution function network, can effectively generate accurate survival time.

Beyond the Hazard Ratio: Generating Expected Durations from the Cox Proportional Hazards Model

2017 ◽  
Vol 50 (1) ◽  
pp. 303-320 ◽  
Author(s):  
Jonathan Kropko ◽  
Jeffrey J. Harden
Keyword(s):  
Political Science ◽  
Hazard Function ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model ◽  
Hazard Ratios ◽  
Quantities Of Interest

The Cox proportional hazards model is a commonly used method for duration analysis in political science. Typical quantities of interest used to communicate results come from the hazard function (for example, hazard ratios or percentage changes in the hazard rate). These quantities are substantively vague, difficult for many audiences to understand and incongruent with researchers’ substantive focus on duration. We propose methods for computing expected durations and marginal changes in duration for a specified change in a covariate from the Cox model. These duration-based quantities closely match researchers’ theoretical interests and are easily understood by most readers. We demonstrate the substantive improvements in interpretation of Cox model results afforded by the methods with reanalyses of articles from three subfields of political science.

scholarly journals Confirmed disability progression provides limited predictive information regarding future disease progression in multiple sclerosis

2021 ◽  
Vol 7 (2) ◽  
pp. 205521732199907
Author(s):  
Brian C Healy ◽  
Bonnie I Glanz ◽  
Elyse Swallow ◽  
James Signorovitch ◽  
Kaitlin Hagan ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Univariate Analysis ◽  
Information Criterion ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Lesion Volume ◽  
Disability Progression ◽  
Fit Statistics

Background Although confirmed disability progression (CDP) is a common outcome in multiple sclerosis (MS) clinical trials, its predictive value for long-term outcomes is uncertain. Objective To investigate whether CDP at month 24 predicts subsequent disability accumulation in MS. Methods The Comprehensive Longitudinal Investigation of Multiple Sclerosis at Brigham and Women’s Hospital includes participants with relapsing-remitting MS or clinically isolated syndrome with Expanded Disability Status Scale (EDSS) scores ≤5 (N = 1214). CDP was assessed as a predictor of time to EDSS score 6 (EDSS 6) and to secondary progressive MS (SPMS) using a Cox proportional hazards model; adjusted models included additional clinical/participant characteristics. Models were compared using Akaike’s An Information Criterion. Results CDP was directionally associated with faster time to EDSS 6 in univariate analysis (HR = 1.61 [95% CI: 0.83, 3.13]). After adjusting for month 24 EDSS, CDP was directionally associated with slower time to EDSS 6 (adjusted HR = 0.65 [0.32, 1.28]). Models including CDP had worse fit statistics than those using EDSS scores without CDP. When models included clinical and magnetic resonance imaging measures, T2 lesion volume improved fit statistics. Results were similar for time to SPMS. Conclusions CDP was less predictive of time to subsequent events than other MS clinical features.

scholarly journals An Application of the Cox-Aalen Model for Breast Cancer Survival

2016 ◽  
Vol 35 (1) ◽  
Author(s):  
Ileana Baldi ◽  
Giovannino Ciccone ◽  
Antonio Ponti ◽  
Stefano Rosso ◽  
Roberto Zanetti ◽  
...  
Keyword(s):  
Cancer Survival ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Tumour Size ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Aalen Model ◽  
Cancer Registry Data ◽  
Hazards Model

Semiparametric hazard function regression models are among the well studied risk models in survival analysis. The Cox proportional hazards model has been a popular choice in modelling data from epidemiological settings. The Cox-Aalen model is one of the tools for handling the problem of non-proportional effects in the Cox model. We show an application on Piedmont cancer registry data. We initially fit standard Cox model and with the help of the score process we detect the violation of the proportionality assumption. Covariates and risk factors that, on the basis of clinical reasoning, best model baseline hazard are then moved into the additive part of the Cox-Aalen model. Multiplicative effects results are consistent with those of the Cox model whereas only the Cox-Aalen model fully represents the timevarying effect of tumour size.

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