Modeling trends in cohort survival probabilities

A new dynamic parametric model is proposed for analyzing the cohort survival function. A one-factor parameterized polynomial in age effects, complementary log-log link and multinomial cohort responses are utilized, within the generalized linear models (GLM) framework. Sparse Principal component analysis (SPCA) is then applied to cohort dependent parameter estimates and provides (marginal) estimates for a two-factor structure. Modeling the two-factor residuals in a similar way, in age-time effects, provides estimates for the three-factor age-cohort-period model. An application is presented for Sweden, Norway, England & Wales and Denmark mortality experience

A General Procedure for Constructing Mortality Models

Recently a large number of new mortality models have been proposed to analyze historic mortality rates and project them into the future. Many of these suffer from being over-parametrized or have terms added in an ad hoc manner that cannot be justified in terms of demographic significance. In addition, poor specification of a model can lead to period effects in the data being wrongly attributed to cohort effects, which results in the model making implausible projections. We present a general procedure for constructing mortality models using a combination of a toolkit of functions and expert judgment. By following the general procedure, it is possible to identify sequentially every significant demographic feature in the data and give it a parametric structural form. We demonstrate using U.K. mortality data that the general procedure produces a relatively parsimonious model that nevertheless has a good fit to the data

Re-thinking households - using administrative data to count and classify households with some application

Households rather than individuals are being increasingly used for research and to target and evaluate public policy. As a result accurate and timely household level statistics have become an increasing necessity especially at local level. However, present sources of information on households are fragmented with significant gaps and inaccuracies that limit their usefulness. This paper reviews present statistical arrangements and then describes a new approach to data collection and household classification based on local administrative sources. The result is a more integrated and flexible system. The utility and advantages are demonstrated using recent examples from the six Olympic London Boroughs

Mortality and longevity risks in the United Kingdom: Dynamic factor models and copula-functions

We present a methodology to forecast mortality rates and estimate longevity and mortality risks. The methodology uses Generalized Dynamic Factor Models fitted over the differences of the log-mortality rates. We compare prediction performance with models previously proposed in the literature, such as the traditional Static Factor Model fitted over the level of log-mortality rates. We also construct risks measures by the means of vine-copula simulations, taking into account the dependence between the idiosyncratic components of the mortality rates. The methodology is implemented to project the mortality rates of the United Kingdom, for which we consider a portfolio and study longevity and mortality risks

Analysis of Models for Epidemiologic and Survival Data

Mortality statistics are useful tools for public-health statisticians, actuaries and policy makers to study health status of populations in communities and to make plans in health care systems. Several statistical models and methods of parameter estimation have been proposed. In this thesis, we review some benchmark mortality models and propose three alternative statistical models for both epidemiologic data and survival data. For epidemiologic data, we propose two statistical models, a Smoothed Segmented Lee-Carter model and a Smoothed Segmented Poisson Log-bilinear model. The models are modifications of the Lee-Carter (1992) model which combine an age segmented Lee-Carter parameterization with spline smoothed period effects within each age segment. With different period effects across age groups, the two models are fitted by maximizing respectively a penalized least squares criterion and a penalized Poisson likelihood. The new methods are applied to the 1971-2006 public-use mortality data sets released by the National Center for Health Statistics (NCHS). Mortality rates for three leading causes of death, heart diseases, cancer and accidents, are studied. For survival data, we propose a phase type model having features of mixtures, multiple stages or hits and a trapping state. Two parameter estimation techniques studied are a direct numerical method and an EM algorithm. Since phase type model parameters are known to be difficult to estimate, we study in detail the performance of our parameter estimation techniques by reference to the Fisher Information matrix. An alternative way to produce a Fisher Information matrix for an EM parameter estimation is also provided. The proposed model and the best available parameter estimation techniques are applied to a large SEER 1992-2002 breast cancer dataset

Bayesian stochastic mortality modelling under serially correlated local effects

The vast majority of stochastic mortality models in the academic literature are intended to explain the dynamics underpinning the process by a combination of age, period and cohort e ects. In principle, the more such e ects are included in a stochastic mortality model, the better is the in-sample t to the data. Estimates of those parameters are most usually obtained under some distributional assumption about the occurrence of deaths, which leads to the optimisation of a relevant objective function. The present Thesis develops an alternative framework where the local mortality effect is appreciated, by employing a parsimonious multivariate process for modelling the latent residual e ects of a simple stochastic mortality model as dependent rather than conditionally independent variables. Under the suggested extension the cells of the examined data-set are supplied with a serial dependence structure by relating the residual terms through a simple vector autoregressive model. The method is applicable for any of the popular mortality modelling structures in academia and industry, and is accommodated herein for the Lee-Carter and Cairns-Blake-Dowd models. The additional residuals model is used to compensate for factors of a mortality model that might mostly be a ected by local e ects within given populations. By using those two modelling bases, the importance of the number of factors for a stochastic mortality model is emphasised through the properties of the prescribed residuals model. The resultant hierarchical models are set under the Bayesian paradigm, and samples from the joint posterior distribution of the latent states and parameters are obtained by developing Markov chain Monte Carlo algorithms. Along with the imposed short-term dynamics, we also examine the impact of the joint estimation in the long-term factors of the original models. The Bayesian solution aids in recognising the di erent levels of uncertainty for the two naturally distinct type of dynamics across di erent populations. The forecasted rates, mortality improvements, and other relevant mortality dependent metrics under the developed models are compared to those produced by their benchmarks and other standard stochastic mortality models in the literature

Stochastic Decision Modeling to Improve Breast Cancer Preventive Care

Breast cancer is a leading cause of premature mortality among women in the United States. Breast cancer screening tests can help with detecting breast cancer in early stages and thereby reducing the breast cancer mortality risk. However, due to the imperfect nature of screening tests, there is always some associated overdiagnosis, false positives, and false negatives risks. Therefore, to improve breast cancer preventive care, we defined the focus of this dissertation on modeling breast cancer screening decisions.Breast cancer overdiagnosis is the first issue that is addressed in this dissertation. Although overdiagnosis is known to be the major risk inherent in mammography screening; currently there is no way to distinguish between overdiagnosed cancers and the ones that would cause problems over a patient’s lifetime. Overdiagnosis risk significantly depends on a patient’s compliance with screening recommendations. In Chapter 2, we use a stochastic framework to perform a harm-benefit analysis to compare the overdiagnosis risk with the benefits that breast cancer screening provides. In addition, we estimate the lifetime mortality risk of breast cancer while considering the overdiagnosis risk and the uncertainty in a patient’s adherence behavior. Our results show that, although overdiagnosis rate is relatively high in breast cancer screening, the benefits of breast cancer mammography screening outweigh the overdiagnosis risk.The second issue that is addressed in this dissertation is false negative results caused by density of breast tissue. Breast density is known to increase breast cancer risk and decrease mammography screening sensitivity. Breast density notification laws, require physicians to inform women with high breast density of these potential risks. The laws usually require healthcare providers to notify patients of the possibility of using more sensitive supplemental screening tests (e.g., ultrasound). Since the enactment of the laws, there have been controversial debates over i) their implementations due to the potential radiologists bias in breast density classification of mammogram images and ii) the necessity of supplemental screenings for all patients with high breast density. Breast density is a dynamic risk factor. Therefore, in the third chapter, we apply a hidden Markov model (HMM) on a sparse unbalanced longitudinal data to quantify the yearly progression of breast density based on Breast Imaging Reporting and Data System (BI-RADs) classifications.In Chapter 4, we use the results from previous chapter to investigate the effectiveness of supplemental screening and the impact of radiologists’ bias on patients’ outcomes under the breast density notification law. We consider the conditional probability of eventually detecting breast cancer in early states given that the patient develops breast cancer in her lifetime and the expected number of supplemental tests as patient’s outcome. Our results indicate that referring patients to a supplemental test solely based on their breast density may not necessarily improve their health outcomes and other risk factors need to be considered when making such referrals. Additionally, average-skilled radiologists’ performances are shown to be comparable with the performance of a perfect radiologist

Gender convergence in human survival and the postponement of death

It has been a long accepted demographic maxim that females outlive males. Using data for England and Wales, we show that life expectancy at age 30 is converging and continuation of this long-term trend suggests it could reach parity in 2030. Key among the reasons identified for the narrowing of the gap are differences in smoking prevalence between males and females which have narrowed considerably. Using data from 30 comparator countries gender differences in smoking prevalence are found to explain over 75% of the variance in the life expectancy gap, but other factors such as female emancipation and better health care are also considered. The paper presents a model which considers differences in male and female longevity in greater detail using novel methods for analysing life tables. It considers the ages from which death is being postponed to the ages at which people now die; the relative speed at which these changes are taking place between genders; how the changes observed are affecting survival prospects at different ages up to 2030. It finds that as life expectancy continues to rise there is evidence for convergence in the oldest ages to which either gender will live