A comparison of a linear and proportional hazards approach to analyse discrete longevity data in dairy cows

2000 ◽  
Vol 70 (2) ◽  
pp. 197-206 ◽  
Author(s):  
R. Lubbers ◽  
S. Brotherstone ◽  
V.P. Ducrocq ◽  
P.M. Visscher
Keyword(s):  
Discrete Time ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Weibull Model ◽  
Logarithmic Scale ◽  
Data Set ◽  
Breeding Values ◽  
Hazards Model ◽  
Estimated Breeding Values ◽  
The Uk

AbstractThe objective of this study was to compare two methods for analysis of longevity in dairy cattle. The first method, currently used for routine genetic evaluation in the UK, uses a linear model to analyse lifespan, i.e. the number of lactations a cow has survived or is expected to survive. The second method was based on the concept of proportional hazard, i.e. modelling the conditional survival probability of a cow as a function of time. Comparisons were based on estimated heritabilities, ranking of estimated breeding values of sires, estimated effects of covariates used in the final models, and the distribution of residuals. The same data set, 21497 observations on the number of lactations cows had survived, was used for both analyses, even in the presence of censored observations. Cows in the data were progeny of 487 sires. Heritability estimates for lifespan or survival were approximately 0·06 for both methods, using the definition of heritability on a logarithmic scale for the proportional hazards model. Correlations between breeding values for sires were high, with absolute values ranging from 0·93 to 0·98, depending on the model fitted. It was concluded that it may be justified to use the standard Weibull model even for discrete time measures such as the number of completed lactations, but that more research is needed in the area of discrete time variates.

scholarly journals A general semiparametric Bayesian discrete-time recurrent events model

Biostatistics ◽  
2019 ◽  
Author(s):  
Adam J King ◽  
Robert E Weiss
Keyword(s):  
Discrete Time ◽  
Recurrent Events ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Additive Models ◽  
Discrete Models ◽  
Survival Times ◽  
Data Set ◽  
Patient Reported ◽  
Time Varying Covariates

SUMMARY Event time variables are often recorded in a discrete fashion, especially in the case of patient-reported outcomes. This work is motivated by a study of illicit drug users, in which time to drug use cessation has been recorded as a number of whole months. Existing approaches for handling such discrete data include treating the survival times as continuous (with adjustments for inevitable tied outcomes), or using discrete models that omit important features like random effects. We provide a general Bayesian discrete-time proportional hazards model, incorporating a number of features popular in continuous-time models such as competing risks and frailties. Our model also provides flexible baseline hazards for time effects, as well as generalized additive models style semiparametric incorporation of other time-varying covariates. Our specific modeling choices enable efficient Markov chain Monte Carlo inference algorithms, which we provide to the user in the form of a freely available R package called $\texttt{brea}$. We demonstrate that our model performs better on our motivating substance abuse application than existing approaches. We also present a reproducible application of the $\texttt{brea}$ software to a freely available data set from a clinical trial of anesthesia administration methods.

Subdistribution hazard models for competing risks in discrete time

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 449-466 ◽  
Author(s):  
Moritz Berger ◽  
Matthias Schmid ◽  
Thomas Welchowski ◽  
Steffen Schmitz-Valckenberg ◽  
Jan Beyersmann
Keyword(s):  
Competing Risks ◽  
Discrete Time ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Model Parameters ◽  
Subdistribution Hazard ◽  
Ml Estimation ◽  
Modeling Approach ◽  
Hazards Model ◽  
Event Times

Summary A popular modeling approach for competing risks analysis in longitudinal studies is the proportional subdistribution hazards model by Fine and Gray (1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association94, 496–509). This model is widely used for the analysis of continuous event times in clinical and epidemiological studies. However, it does not apply when event times are measured on a discrete time scale, which is a likely scenario when events occur between pairs of consecutive points in time (e.g., between two follow-up visits of an epidemiological study) and when the exact lengths of the continuous time spans are not known. To adapt the Fine and Gray approach to this situation, we propose a technique for modeling subdistribution hazards in discrete time. Our method, which results in consistent and asymptotically normal estimators of the model parameters, is based on a weighted ML estimation scheme for binary regression. We illustrate the modeling approach by an analysis of nosocomial pneumonia in patients treated in hospitals.

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.

Methods for displaying and calibration of Cox proportional hazards models

2017 ◽  
Vol 232 (1) ◽  
pp. 105-115
Author(s):  
Chrianna I Bharat ◽  
Kevin Murray ◽  
Edward Cripps ◽  
Melinda R Hodkiewicz
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Explanatory Power ◽  
Cox Proportional Hazards ◽  
Remaining Useful Life ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Proportional Hazards Models ◽  
Hazards Model ◽  
Cox Proportional Hazards Models

Cox proportional hazards modelling is a widely used technique for determining relationships between observed data and the risk of asset failure when model performance is satisfactory. Cox proportional hazards models possess good explanatory power and are used by asset managers to gain insight into factors influencing asset life. However, validation of Cox proportional hazards models is not straightforward and is seldom considered in the maintenance literature. A comprehensive validation process is a necessary foundation to build trust in the failure models that underpin remaining useful life prediction. This article describes data splitting, model discrimination, misspecification and fit methods necessary to build trust in the ability of a Cox proportional hazards model to predict failures on out-of-sample assets. Specifically, we consider (1) Prognostic Index comparison for training and test sets, (2) Kaplan–Meier curves for different risk bands, (3) hazard ratios across different risk bands and (4) calibration of predictions using cross-validation. A Cox proportional hazards model on an industry data set of water pipe assets is used for illustrative purposes. Furthermore, because we are dealing with a non-statistical managerial audience, we demonstrate how graphical techniques, such as forest plots and nomograms, can be used to present prediction results in an easy to interpret way.

Money transfer between banks

2019 ◽  
Vol 38 (2) ◽  
pp. 283-295
Author(s):  
Andrea Lippi ◽  
Laura Barbieri ◽  
Federica Poli
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Financial Advisors ◽  
Data Set ◽  
Financial Intermediary ◽  
Content Type ◽  
Financial Advisor ◽  
Hazards Model

Purpose The purpose of this paper is to examine which individual traits of financial advisors influence portfolio transfer speed when a financial advisor recommends investors to migrate to a new financial intermediary. Design/methodology/approach With reference to the years 2014–2016, one of the three leading Italian tied-agent banks provided the authors with an exclusive and unique data set containing information regarding the financial advisors who had become tied agents, transferring their existing portfolios from their previous banks (traditional or tied-agent banks). The authors observed the ability of the migrant financial advisor in successfully transferring the entire portfolio declared within 12 months of observation. To investigate empirically which personal traits of financial advisors determine their success in the rapid transfer of clients’ portfolios to a new financial intermediary, the authors applied a Cox proportional hazards model. Findings The authors find that factors such as age, type of bank of origin and size of the managed financial portfolio positively affect the speed transfer. Practical implications The obtained results may be interesting for guiding recruiting policies of financial intermediaries. Social implications Regulators should closely examine the phenomenon analyzed in this paper to avoid conflict of interests. Originality/value The literature on this topic is scarce, mainly due to the lack of available data. This paper represents an original contribution to open a new field of research.

A Note on Standard Errors for Survival Curves in Discrete-Time Survival Analysis

2005 ◽  
Vol 30 (1) ◽  
pp. 75-92 ◽  
Author(s):  
Rebecca Zwick ◽  
Jeffrey C. Sklar
Keyword(s):  
Logistic Regression ◽  
Discrete Time ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Standard Errors ◽  
Survival Functions ◽  
Hazards Model ◽  
Time Period ◽  
Flexible Form ◽  
Ordinary Logistic Regression

Cox (1972) proposed a discrete-time survival model that is somewhat analogous to the proportional hazards model for continuous time. Efron (1988) showed that this model can be estimated using ordinary logistic regression software, and Singer and Willett (1993) provided a detailed illustration of a particularly flexible form of the model that includes one parameter per time period. This work has been expanded to show how logistic regression output can also be used to estimate the standard errors of the survival functions. This is particularly simple under the model described by Singer and Willett, when there are no predictors other than time.

Impact of digital multi-sided platforms on firm survival in retail

2021 ◽  
pp. 93-122
Author(s):  
E. S. Andronova ◽  
A. I. Rey ◽  
G. R. Akzhigitova
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Positive Impact ◽  
Survival Function ◽  
Firm Survival ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Hazards Model ◽  
Kaplan Meier

This paper explores firm survival in Russian retail industry in cases of digital multi-sided platforms penetration such as aggregator Yandex.Market, marketplace Wildberries, electronic store Ozon and classified-ad service Avito. The panel data set of 130 thousand firms was analyzed using two methods: non-parametric Kaplan—Meier estimator and semi-parametric Cox proportional hazards model with time dependent covariates. Kaplan—Meier estimator calculates the survival function for censored data. Cox proportional hazards model examines the effect of platform penetration on hazard rates of differently sized firms in various industry spheres. Platforms-aggregators Yandex.Market and Wildberries have a strong positive impact on firm survival while platformsdisruptors Ozon and Avito increase likelihood of firm failure. The main results of platform influence in various industry spheres are as follows: the aggregator of price offers has a more positive impact on segments with high information asymmetry; and firms specialized on Wildberries key product categories enjoy lower hazard ratios of bankruptcy or liquidation. These hypotheses are not supported for Ozon and Avito platforms.

scholarly journals Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths

2021 ◽  
Author(s):  
Casper Wilstup ◽  
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

AbstractHeart 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.We used a newly invented symbolic regression method called the QLat-tice 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.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.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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