 Monte Carlo for survival data    Message/Author  Hanjoe Kim posted on Wednesday, January 20, 2016 - 6:33 pm
I am trying to understand the HAZARDC command.
First I thought that this command is used to specify the censoring proportion in data but I learned that the value given here is the lambda value for an exponential parameter.
From Asparouhov and Muthen (2014), if lambda=lambda1 (where lambda is defined within Model Population T#1*lambda with GENERATE = T(s) and lambda1 is the value used in HAZARDC), about 50% of the observations will be censored.

Can you give me guidance to adjust the censoring proportion in Monte Carlo generated data?
For example, if T#1*.1, then HAZARDC=T(.1) will give about 50% censoring. Given that the lambda value is fixed at .1, what would the lambda1 values be to get 10% or 30% censoring?  Tihomir Asparouhov posted on Thursday, January 21, 2016 - 11:33 am
We generate the survival variable T and independently censoring variable C with exponential distribution with parameter given by the HAZARDC command. If C > T the variable is not censored. If C < T the T variable is censored at time C.

If C~Exp(a) and T~Exp(b) then
Prob(C<T)=a/(a+b), see
http://www.math.wm.edu/~leemis/chart/UDR/PDFs/ExponentialLaplace.pdf

If a=b you get 50% censoring.
If a=(3/7)*b you get 30% censoring.
If a=(1/9)*b you get 10% censoring.

HAZARDC=T(.043) will give you 30%
HAZARDC=T(.011) will give you 10%  Hanjoe Kim posted on Thursday, January 21, 2016 - 1:35 pm
This is crystal clear! Thank you Dr. Asparouhov!

Hanjoe.    Topics | Tree View | Search | Help/Instructions | Program Credits Administration