joineR: Joint modelling of repeated measurements and time-to-event data

The joineR package implements methods for analysing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-toevent outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure. The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty. Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model

A Quantile Regression Model for Failure-Time Data with Time-Dependent Covariates

Since survival data occur over time, often important covariates that we wish to consider also change over time. Such covariates are referred as time-dependent covariates. Quantile regression offers flexible modeling of survival data by allowing the covariates to vary with quantiles. This paper provides a novel quantile regression model accommodating time-dependent covariates, for analyzing survival data subject to right censoring. Our simple estimation technique assumes the existence of instrumental variables. In addition, we present a doubly-robust estimator in the sense of Robins and Rotnitzky (1992). The asymptotic properties of the estimators are rigorously studied. Finite-sample properties are demonstrated by a simulation study. The utility of the proposed methodology is demonstrated using the Stanford heart transplant dataset

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

Nonparametric change point estimation for survival distributions with a partially constant hazard rate

We present a new method for estimating a change point in the hazard function of a survival distribution assuming a constant hazard rate after the change point and a decreasing hazard rate before the change point. Our method is based on fitting a stump regression to p-values for testing hazard rates in small time intervals. We present three real data examples describing survival patterns of severely ill patients, whose excess mortality rates are known to persist far beyond hospital discharge. For designing survival studies in these patients and for the definition of hospital performance metrics (e.g. mortality), it is essential to define adequate and objective end points. The reliable estimation of a change point will help researchers to identify such end points. By precisely knowing this change point, clinicians can distinguish between the acute phase with high hazard (time elapsed after admission and before the change point was reached), and the chronic phase (time elapsed after the change point) in which hazard is fairly constant. We show in an extensive simulation study that maximum likelihood estimation is not robust in this setting, and we evaluate our new estimation strategy including bootstrap confidence intervals and finite sample bias correction