Stats
Category: Survival analysis. Survival data represents data that
indicates with information about the time to a certain event (often death, but
it can represent other events as well). A common feature for most survival data
is the process of censoring. These pages discuss the various ways you can
analysis survival data. Articles are arranged by date with the most recent
entries at the top. You can find the theme
and closely related categories and other resources at the bottom of this page.
Stats: A simple example of a Kaplan-Meier
curve (January 24, 2008). In response to a query, I wanted to write up a
simple example of how to calculate survival probabilities when you have
censored data. It is adapted from Chapter 6 of my book, Statistical Evidence
in Medical Trials. I have updated and simplified the example, for possible use
in a second edition of the book, if I am so lucky.
Stats: Conditional Frailty Models
(January 20, 2006). One of the people I am working with is interested in
using gap time analysis with a conditional frailty model. I was impressed with
this request and asked her to send any relevant references that she had. She
gave me a pointer to the following PDF file: Repeated events survival models:
the conditional frailty model.
Stats: More than 90% censored
values (April 22, 2005). Someone asked me about running a Cox
proportional hazards regression model when over 90% of the observations were
censored. That means (if the outcome of interest was death), that your
research subjects did not cooperate and die fast enough. Good news from the
patients' perspective, but bad news for the statistician. 90% censored
observations is not a problem, though, as long as your sample size is
adequate. As a rough rule of thumb, you need to have 25 to 50 events
(uncensored observations) in each treatment group to have reasonable
precision. Of course, if you have fewer events, the model is still valid, but
your confidence intervals may end up being wider than you'd really like.
Stats: Stratified Cox
regression models (March 22, 2005).
Stats: The price of
Kaplan-Meier [Incomplete] (September 23, 2004). I rarely find time to
read the Statistics journals anymore, but I did run across an excellent
article in the September 2004 issue of JASA. The Price of Kaplan-Meier. Meier
P, Karrison T, Chappell R, Xie H. Journal of the American Statistical
Association 2004: 99(467); 890-896.
Stats: Data
management for survival data (August 27, 2004). Survival data will involve
calculating the time between the various dates and noting when certain dates
are present or absent. In a study of bone marrow transplants for childhood
cancer, we have up to four dates: Date of bone marrow transplant (always
known) Date of last follow-up (always known) Date of relapse (sometimes
censored) Date of death (sometimes censored) The dates of relapse and death
are censored because either they did not occur, or they occurred after the
date of last follow-up.
Stats:
Guidelines for survival data models (October 11, 2002). There are three
steps in a typical survival analysis. Know how much data you have, Graph the
survival function, Compare the survival times.
Stats: Kaplan Meier (June 27, 2000).
Dear Professor Mean: When I read my medical journals, I keep on coming across
terms like "Kaplan-Meier Product Limit Estimate" or "Kaplan-Meier survival
curve." What do these terms mean and when are they used?
Theme and closely related categories:
Other resources:
[Return to full topic list]
[Read current weblog entries]
This webpage was written on 2007-09-12 and was last modified on
2008-07-08.
