Utility Analysis and the Consumption Function: An Interpretation of Cross-Section Data

On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data

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Progressively Type-II censored competing risks data from Lomax distributions

In this paper we consider the Bayesian inference of the unknown parameters of the progressively censored competing risks data, when the lifetime distributions are Weibull It is assumed that the latent cause of failures have independent Weibull distributions with the common shape parameter, but different scale parameters In this article, it is assumed that the shape parameter has a log-concave prior density function, and for the given shape parameter, the scale parameters have Beta-Dirichlet priors When the common shape parameter is known, the Bayes estimates of the scale parameters have closed form expressions, but when the common shape parameter is unknown, the Bayes estimates do not have explicit expressions In this case we propose to use MCMC samples to compute the Bayes estimates and highest posterior density (HPD) credible intervals Monte Carlo simulations are performed to investigate the performances of the estimators Two data sets are analyzed for illustration Finally we provide a methodology to compare two different censoring schemes and thus find the optimum Bayesian censoring scheme

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Bayesian analysis of progressively censored competing risks data

On progressively censored competing risks data for Weibull distributions

The goal of this research is to study jointly physician prescription decisions and patient drug request behaviours. We have adopted a binary logit model and a multinomial logit model to study patient drug request data with excessive zero requests and a multinomial logit model to capture physician prescription decisions. These models are further joined by a flexible non‐parametric multivariate distribution for their random effects. We also adopt an analytically consistent expression for interaction effects in our non‐linear and joint modelling framework. We apply our model to a unique physician panel data set from the erectile dysfunction category. Our key empirical findings include that the triggering of drug requests by direct‐to‐consumer advertising (DTCA) is complex with category level DTCA reducing patients’ probabilities of making drug requests and drug‐specific DTCA driving drug requests for the drug advertised, patients’ characteristics may play a role in both the influence of DTCA on drug requests and the influence of patients’ requests on physicians’ prescription decisions, patients’ drug requests have a positive effect on physicians’ prescription decisions and patients can be consistent with physicians in choosing a drug based on their diagnosis levels and some unobserved factors, and there are significant correlations between physician level random effects that drive both patients’ drug requests and physicians’ prescription decisions, which validate the joint modelling approach.

Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions

We explore whether a protective role for savings against future financial hardship exists using household level panel data. We jointly model the incidence and extent of financial problems, as well as the likelihood of having secured debt and the amount of monthly secured debt repayments, allowing for dynamics and interdependence in both of the two-part outcomes. A two-part process is important given the considerable inflation at zero when analysing financial problems. The model is estimated using a flexible Bayesian approach with correlated random effects and the findings suggest that: (i) saving on a regular basis mitigates both the likelihood of experiencing, as well as the number of, future financial problems; (ii) state dependence in financial problems exists; (iii) interdependence exists between financial problems and secured debt, specifically higher levels of mortgage debt are associated with an increased probability of experiencing financial hardship.

Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data

First, we would like to congratulate the authors on a very accurate and clearly written work. The paper provides a thorough literature review on various aspects of the joint model and certainly provides a strong motivation to the use of joint modeling techniques in medical applications. Jointly modelling two or more processes together has been an active area of research for quite sometime now. This is particularly useful when the processes have different distributions, as in mixed data frameworks. A particularly interesting feature is the possibility to predict one event conditional on the others. We believe three extremely interesting experimental designs arise which are amenable to joint modeling techniques. First of all, joint models must be used when the focus is on the longitudinal outcome(s), but there is informative drop-out. Secondly, joint models must be used when the focus is on the survival outcome, and there are time-varying covariates which might be measured with error [1]. Finally, in many applications both aspects of the joint model might be of interest, where one might discover that a treatment is beneficial for survival but detrimental for a longitudinal outcome measuring quality of life or occurrence of a side effect. While much research happened on this topic, it is yet to become popular in the applied field despite the fact that, as surveyed by the authors, there are now many software options for easy implementation of joint models. Reasons in our opinion range from lack of user-friendly software to lack of understanding of the the advantages of the joint model framework.

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Discussion on 'Joint modeling of survival and longitudinal non-survival data' by Gould et al.

Using British panel data, we explore the relationship between saving behaviour and health,as measured by an extensive range of biomarkers, which are rarely available in large nationallyrepresentative surveys. The effects of these objective measures of health are compared withcommonly used self-assessed health measures. We develop a semi-continuous high-dimensionalBayesian modelling approach, which allows different data-generating processes for the decision tosave and the amount saved. We find that composite biomarker measures of health, as well asindividual biomarkers, are significant determinants of saving. Our results suggest that objectivebiomarker measures of health have differential impacts on saving behaviour compared to selfreported health measures, suggesting that objective health measures can further our understanding ofthe effect of health on financial behaviours.

Bhuvanesh Pareek

Papers

On progressively censored competing risks data for Weibull distributions

Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions

Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data

Discussion on 'Joint modeling of survival and longitudinal non-survival data' by Gould et al.

Saving Behaviour and Biomarkers: A High-Dimensional Bayesian Analysis of British Panel Data