A Simple Model of Access and Capacity for Post-Secondary Schooling in Canada (Working Paper 40)

This paper develops a simple model of the post-secondary education system in Canada that provides a useful basis for thinking about issues of capacity and access. It uses a supply-demand framework, where demand comes on the part of individuals wanting places in the system, and supply is determined not only by various directives and agreements between educational ministries and institutions (and other factors), but also the money available to universities and colleges through tuition fees. The supply and demand curves are then put together with a stylised tuition-setting rule to describe the “market” of post-secondary schooling. This market determines the number of students in the system, and their characteristics, especially as they relate to “ability” and family background, the latter being especially relevant to access issues. The manner in which various changes in the system – including tuition fees, student financial aid, government support for institutions, and the returns to schooling – are then discussed in terms of how they affect the number of students and their characteristics, or capacity and access.

An R-package for the Estimation and Testing of Multiple Covariates and Biomarker Interactions for Survival Data Based on Local Partial Likelihood

When we study the variables that a ffect survival time, we usually estimate their eff ects by the Cox regression model. In biomedical research, e ffects of the covariates are often modi ed by a biomarker variable. This leads to covariates-biomarker interactions. Here biomarker is an objective measurement of the patient characteristics at baseline. Liu et al. (2015) has built up a local partial likelihood bootstrap model to estimate and test this interaction e ffect of covariates and biomarker, but the R code developed by Liu et al. (2015) can only handle one variable and one interaction term and can not t the model with adjustment to nuisance variables. In this project, we expand the model to allow adjustment to nuisance variables, expand the R code to take any chosen interaction terms, and we set up many parameters for users to customize their research. We also build up an R package called "lplb" to integrate the complex computations into a simple interface. We conduct numerical simulation to show that the new method has excellent fi nite sample properties under both the null and alternative hypothesis. We also applied the method to analyze data from a prostate cancer clinical trial with acid phosphatase (AP) biomarker.