survivalI

just back from doing some survival analysis. the model produces good predictions having been fit a lognormal distribution. however, the timing predictions are typically off by one period. this would have been pretty good for any other kind of prediction I think. but not for timing. we dont want to give the treatment one peirod off. maybe its better to produce the probability of an event occurring in an interval rather than at a point. or maybe make the periods broader, which amounts to the same thing. what about producing the median failure time in each period? something like 50% of those surviving the 2nd period are likely to fail within the 8th period.

the chances of something happening or not is modelled using logistic regression. the chances of something happening in a particuler period is modelled by studying the distribution of occurences over time. what if we model this as a evolving bernoulli distribution over periods: a stochastic process?

Agenda:

1. read kalbfleisch and prentice

2. read other approaches to survival analysis like the book on viewing survival analysis as a case of stochastic processes

3. study what fader etc has done on this