The R library survival is very handy for fitting survival data. There are some examples in the practicals for Davison's book, a pdf file available here.

This pdf from John Fox discusses coxph in detail, showing how to incorporate time-dependent covariates, and discussing model checking and residuals

This note discusses various types of bias that often come up in medical studies involving survival data

November 23, 24

Notes on case-control studies (from STA 2101 lecture on November 9)

The data in cloth is the same as on the handout, but in a different order, and x has been rescaled by division by 100. It seems more likely to be correct, as it is hard to envision a 600-metre roll of cloth.

Ascii file of UN Data for homework 1, for those of you not using R. You can read this file into R by first putting into a file of your choosing, and then using read.table("filename", head=T, row.names=1).
However you can just as easily load the data as indicated on the HW sheet.

September 23

FIX!! to load the datasets for the book, you should install "SMPracticals", not the "statmod" package that I told you. When I tried this, it asked me to first install another package, called "ellipse". After installing the package, I typed library(ellipse), then I installed the SMPracticals package. (A student who did this under windows had ellipse installed automatically.) Then type (within R) library(SMPracticals) and you should have all the text data sets available. For example, try data(venice) and data(nuclear)

Handout on linear regression using the Venice data

Also, check the list of R resources above: Jeff's is good for novices, and many further resources are available from the "Another list"

Statistical Models by A.C. Davison. We will emphasize Chapters 5, 8, 9 and 10.

Computing

You are welcome to use the statistical computing package of your choice,
but
I will refer exclusively to the R computing package.
Statistics Dept graduate students can access R on the
Statistics Dept
computers; undergraduate students can access R on
CQUEST. Alternatively, students
can install R on the computer(s) of their choice, by downloading its
"base" package (for free) from
probability.ca/cran
or www.r-project.org.

Sep 30: I just stumbled across a very nice introduction to R, that has lots of Windows screenshots. It can be downloaded from
this web site; it's the file "introduction to R".

There are many helpful introductions to R, including:

Computing: You are welcome to use the statistical computing package of your choice,
but I will refer exclusively to the R computing package. Statistics Dept graduate students
can access R on the Statistics Dept computers; undergraduate students can access R
on CQUEST. Alternatively, students can install R on the computer(s) of their
choice, by downloading its ”base” package (for free) from probability.ca/cran or
www.r-project.org. There are many helpful introductions to R listed on the course
webpage.

Contact: Nancy Reid: SS 6002A, reid@utstat.utoronto.ca, 978-5046. Tuesday 4 to 5, Wednesday 4 to 5, or by appointment.