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st: Event history/Survival analysis_how to introduce time varying variables


From   "Simon Oertel" <Sim.Oertel@t-online.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Event history/Survival analysis_how to introduce time varying variables
Date   Mon, 19 Nov 2007 16:18:13 +0100

Dear Stata Users,
 
I need some advice concerning survival analysis. I would like to analyze
the effect of CEO exits on the mortality rate of organizations. My
problem is that I am not sure about the way how to introduce the exit of
a CEO since this variable is not time constant. 
 
Following the book of Blossfeld/Golsch/Rohwer (Event History Analysis
with Stata), there are three basic approaches to analysis such data:
1.) by using piecewise constant exponential model,
2.) by applying the method of episode splitting in parametric and
semiparametric transition rate models,
3.) by specifying the distributional form of the time-dependence and
directly estimating its parameters using the ML method. 
 
I first used the second approach. I split (if a CEO exit has occurred in
the organization) the "living time" of each organization in two
episodes. The first episode starts from the time of foundation and ends
at the exit-time of the CEO. The second spell starts from the time of
the CEO exit and ends at the closing day of the organization. Then I
used parametric/semiparametric models to analyse the influence of the
CEO exit on the risk of an organizational closure. 
 
However, I would like to compare these results with the results of a
piecewise constant exponential model. Following examples in the basic
literature, I used "stsplit time, at (0, 365, .)" and "tab time,
ge(time)" for splitting the data into yearly records and defining time
dummies. Then I generated period-specific dummies. For example: "for any
branch branch . \ any time1 time2 .: gen branchtimeY = X*Y". However, I
am not sure if I can use the same command for modelling period-specific
dummies concerning CEO exits. In the dataset I have two variables for
the CEO exit. One dummy, which tells my, if a CEO exit has occurred in
the organization and a second one which tells me the exactly exit-date. 
Is it right, to use the date of the CEO exit (lets say the CEO of
organization1 would exits 725 days after the foundation of the
organization) for defining the period-specific dummies? This would mean
that the command would be for example: "for any CEO_exit_time
CEO_exit_time . \ any time1 time2 .: gen CEO_exit_timetimeY =  X*Y",
right?
 
Since my results of this analysis-approach are totally different from
the one of the parametric/semiparametric transition rate models, I
suggest that I make some failure (The results of the piecewise constant
exp. model show a very weak, positive influence of CEO exits on the
mortality risk of orgnizations, while the parametric models show a
strong negativ effect of CEO exits on the mortality risk of
organizations.) 

Or is it possible that the results are different, because the case
number is too small for a piecewise constant exponential model (around
1200 exits in 15000 organizations in 15 years)?
 
Also if I suggest that this question is very simple, I will really
appreciate any answer. 
 
Thanks for your help
Simon

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