A reference relative time-scale as an alternative to chronological age for cohorts with long follow-up

Background: Epidemiologists have debated the appropriate time-scale for cohort survival studies; chronological age or time-on-study being two such time-scales. Importantly, assessment of risk factors may depend on the choice of time-scale. Recently, chronological or attained age has gained support but a case can be made for a ‘reference relative time-scale’ as an alternative which circumvents difficulties that arise with this and other scales. The reference relative time of an individual participant is the integral of a reference population hazard function between time of entry and time of exit of the individual. The objective here is to describe the reference relative time-scale, illustrate its use, make comparison with attained age by simulation and explain its relationship to modern and traditional epidemiologic methods. Results: A comparison was made between two models; a stratified Cox model with age as the time-scale versus an un-stratified Cox model using the reference relative time-scale. The illustrative comparison used a UK cohort of cotton workers, with differing ages at entry to the study, with accrual over a time period and with long follow-up. Additionally, exponential and Weibull models were fitted since the reference relative time-scale analysis need not be restricted to the Cox model. A simulation study showed that analysis using the reference relative time-scale and analysis using chronological age had very similar power to detect a significant risk factor and both were equally unbiased. Further, the analysis using the reference relative time-scale supported fully-parametric survival modelling and allowed percentile predictions and mortality curves to be constructed. Conclusions: The reference relative time-scale was a viable alternative to chronological age, led to simplification of the modelling process and possessed the defined features of a good time-scale as defined in reliability theory. The reference relative time-scale has several interpretations and provides a unifying concept that links contemporary approaches in survival and reliability analysis to the traditional epidemiologic methods of Poisson regression and standardised mortality ratios. The community of practitioners has not previously made this connection

Parametric Reversed Hazards Model for Left Censored Data with Application to HIV

Left censoring is generally a rare type of censoring in time-to-event data, however there are some fields such as HIV related studies where it commonly occurs. Currently, there is no clear recommendation in the literature on the optimal model and distribution to analyze left-censored data. Recommendations can help researchers apply more accurate models for this type of censoring. This study derives the Parametric Reversed Hazards (PRH) Model for a variety of distributions which may be appropriate for left censored data. The performance of these derived PRH models to analyze HIV viral load data are compared using extensive simulations and a guideline is established for which distribution/s are most appropriate. Each simulation setup is varied by sample size and proportion of censoring to find a consistently high performance distribution. The best distribution is determined using the information criteria: AIC, AICC, HQIC, and CAIC. The South Carolina Enhanced HIV/AIDS Reporting Surveillance System (SC eHARS) data were utilized and a bootstrap study provided further insights towards appropriateness of the distributions in analyzing HIV viral load data. Results from simulation studies point to the Generalized Inverse Weibull distribution to outperform all others across censoring rates and sample sizes. The bootstrap study, however, contradicts this and suggests the Marshal-Olkin distribution to be the superior performer. This disagreement may have resulted from the special heavy tail nature of viral load data that demands further attention. Application of the best performing models on the SC eHARS database revealed important effects explaining trends of viral load over time

Relation between early life socioeconomic position and all cause mortality in two generations. A longitudinal study of Danish men born in 1953 and their parents

Objective: To examine (1) the relation between parental socioeconomic position and all cause mortality in two generations, (2) the relative importance of mother’s educational status and father’s occupational status on offspring mortality, and (3) the effect of factors in the family environment on these relations. Design: A longitudinal study with record linkage to the Civil Registration System. The data were analysed using Cox regression models. Setting: Copenhagen, Denmark. Subjects: 2890 men born in 1953, whose mothers were interviewed regarding family social background in 1968. The vital status of this population and their parents was ascertained from April 1968 to January 2002. Main outcome measures: All cause mortality in study participants, their mothers, and fathers. Results: A similar pattern of relations was found between parental social position and all cause mortality in adult life in the three triads of father, mother, and offspring constituted of the cohort of men born in 1953, their parents, and grandparents. The educational status of mothers showed no independent effect on total mortality when father’s occupational social class was included in the model in either of the triads. Low material wealth was the indicator that remained significantly associated with adult all cause mortality in a model also including parental social position and the intellectual climate of the family in 1968. In the men born in 1953 the influence of material wealth was strongest for deaths later in adult life. Conclusion: Father’s occupational social class is associated with adult mortality in all members of the mother-father-offspring triad. Material wealth seems to be an explanatory factor for this association

Multilevel modelling of event history data: comparing methods appropriate for large datasets

Abstract When analysing medical or public health datasets, it may often be of interest to measure the time until a particular pre-defined event occurs, such as death from some disease. As it is known that the health status of individuals living within the same area tends to be more similar than for individuals from different areas, event times of individuals from the same area may be correlated. As a result, multilevel models must be used to account for the clustering of individuals within the same geographical location. When the outcome is time until some event, multilevel event history models must be used. Although software does exist for fitting multilevel event history models, such as MLwiN, computational requirements mean that the use of these models is limited for large datasets. For example, to fit the proportional hazards model (PHM), the most commonly used event history model for modelling the effect of risk factors on event times, in MLwiN a Poisson model is fitted to a person-period dataset. The person-period dataset is created by rearranging the original dataset so that each individual has a line of data corresponding to every risk set they survive until either censoring or the event of interest occurs. When time is treated as a continuous variable so that each risk set corresponds to a distinct event time, as is the case for the PHM, the size of the person-period dataset can be very large. This presents a problem for those working in public health as datasets used for measuring and monitoring public health are typically large. Furthermore, individuals may be followed-up for a long period of time and this can also contribute to a large person-period dataset. A further complication is that interest may be in modelling a rare event, resulting in a high proportion of censored observations. This can also be problematic when estimating multilevel event history models. Since multilevel event history models are important in public health, the aim of this thesis is to develop these models so they can be fitted to large datasets considering, in particular, datasets with long periods of follow-up and rare events. Two datasets are used throughout the thesis to investigate three possible alternatives to fitting the multilevel proportional hazards model in MLwiN in order to overcome the problems discussed. The first is a moderately-sized Scottish dataset, which will be the main focus of the thesis, and is used as a ‘training dataset’ to explore the limitations of existing software packages for fitting multilevel event history models and also for investigating alternative methods. The second dataset, from Sweden, is used to test the effectiveness of each alternative method when fitted to a much larger dataset. The adequacy of the alternative methods are assessed on the following criteria: how effective they are at reducing the size of the person-period dataset, how similar parameter estimates obtained from using methods are compared to the PHM and how easy they are to implement. The first alternative method involves defining discrete-time risk sets and then estimating discrete-time hazard models via multilevel logistic regression models fitted to a person-period dataset. The second alternative method involves aggregating the data of individuals within the same higher-level units who have the same values for the covariates in a particular model. Aggregating the data like this means that one line of data is used to represent all such individuals since these individuals are at risk of experiencing the event of interest at the same time. This method is termed ‘grouping according to covariates’. Both continuous-time and discrete-time event history models can be fitted to the aggregated person-period dataset. The ‘grouping according to covariates’ method and the first method, which involves defining discrete-time risk sets, are both implemented in MLwiN and pseudo-likelihood methods of estimation are used. The third and final method to be considered, however, involves fitting Bayesian event history (frailty) models and using Markov chain Monte Carlo (MCMC) methods of estimation. These models are fitted in WinBUGS, a software package specially designed to make practical MCMC methods available to applied statisticians. In WinBUGS, an additive frailty model is adopted and a Weibull distribution is assumed for the survivor function. Methodological findings were that the discrete-time method led to a successful reduction in the continuous-time person-period dataset; however, it was necessary to experiment with the length of time intervals in order to have the widest interval without influencing parameter estimates. The grouping according to covariates method worked best when there were, on average, a larger number of individuals per higher-level unit, there were few risk factors in the model and little or none of the risk factors were continuous. The Bayesian method could be favourable as no data expansion is required to fit the Weibull model in WinBUGS and time is treated as a continuous variable. However, models took a much longer time to run using MCMC methods of estimation as opposed to likelihood methods. This thesis showed that it was possible to use a re-parameterised version of the Weibull model, as well as a variance expansion technique, to overcome slow convergence by reducing correlation in the Markov chains. This may be a more efficient way to reduce computing time than running further iterations

An economic analysis of retirement decisions in Taiwan

Over the last 20 years there has been a growth in the relative importance of labour economics as an area of economics, particularly for labour force participation, retirement, and labour force transition. However in Taiwan, due to a lack of suitable data, most of the work in this area has been cross-sectional and time-series data analyses. This thesis uses micro panel data to fill this gap. The data is from the Survey of Health and Living Status of the Middle Aged and Elderly in Taiwan, a rich source of information on employment history from 1989 to 2003. The main econometric methods use the binary response models and continuous-time hazard models to analyse labour force participation, retirement, and labour force transition, paying particular to gender differences. The main empirical results show that older workers, female workers, Mainlander workers, and workers with poor health have a lower probability of labour force participation and a higher hazard rate of retirement. In contrast, Hakka workers, workers with better educational attainment, married male workers, and rural workers have a higher probability of participation in work and a lower hazard rate of retirement. In particular, there is an interesting and conditional result for the Pension variable that for workers with less than 35 years employment duration, the survival curve for workers eligible for a pension lies above that of workers ineligible for a pension; and after 35 years, die results are expected to change, particularly for women. Furthermore, workers with higher predicted earnings have a lower hazard rate of retirement, and workers with higher predicted pension income have a higher hazard rate of retirement. Finally, in the case of labour force transitions, the duration models incorporate time-varying covariate factors and show that being in poor health increases the hazard rate of retirement, other things being equal. In addition, as the models consider unobserved heterogeneity factors and find that most estimated coefficients on the regressors are lightly larger in magnitude than the corresponding coefficients in the reference model. Further, unobserved heterogeneity factors are also found to be less serious once time-varying covariates are included in the hazard mode

Forecasting Employee Turnover in Large Organizations

Researchers and human resource departments have focused on employee turnover for decades. This study developed a methodology forecasting employee turnover at organizational and departmental levels to shorten lead time for hiring employees. Various time series modeling techniques were used to identify optimal models for effective employee-turnover prediction based on a large U.S organization\u27s 11-year monthly turnover data. A dynamic regression model with additive trend, seasonality, interventions, and a very important economic indicator efficiently predicted turnover. Another turnover model predicted both retirement and quitting, including early retirement incentives, demographics, and external economic indicators using the Cox proportional hazard model. A variety of biases in employee-turnover databases along with modeling strategies and factors were discussed. A simulation demonstrated sampling biases\u27 potential impact on predictions. A key factor in the retirement was achieving full vesting, but employees who did not retire immediately maintain a reduced hazard after qualifying for retirement. Also, the model showed that external economic indicators related to S&P 500 real earnings were beneficial in predicting retirement while dividends were most associated with quitting behavior. The third model examined voluntary turnover factors using logistic regression and forecasted employee tenure using a decision tree for four research and development departments. Company job title, gender, ethnicity, age and years of service affected voluntary turnover behavior. However, employees with higher salaries and more work experience were more likely to quit than those with lower salaries and less experience. The result also showed that college major and education level were not associated with R&D employees\u27 decision to quit

Why Do Women Have Longer Unemployment Durations than Men in Post-Restructuring Urban China?

This paper provides the first systematic analysis of the reasons why women endure longer unemployment durations than men in post-restructuring urban China using data obtained from a national representative household survey. Rejecting the view that women are less earnest than men in their desire for employment, the analysis shows that women's job search efforts are handicapped by lack of access to social networks, social stereotyping (that married women are unreliable employees), unequal access to social reemployment services stemming from sex segregation prior to the displacement, and wage discrimination in the post-restructuring labor market.Gender inequality, unemployment duration, Oaxaca-decomposition

Eliciting Individual Preferences for Pension Reform

Pension systems have recently been under scrutiny because of the expected population ageing threatening its sustainability. This paper's contribution to the debate is from a political economic perspective as it uses data from a choice experiment to investigate individual preferences for an alternative state pension scheme based around preferences for cost, poverty, retirement age and pension parameters. Answers are used to estimate a lifecycle utility model of preferences towards pensions' parameters. Results suggest that individuals’ value orientation is an important determinant of their preferences. Respondents' income determines which degree of redistribution is preferred. However, preferences according to age are in contradiction with what is suggested in theory.redistribution, pension system reform, population ageing, stated preferences