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st: Re: Interval censored survival model

From   <>
To   <>
Subject   st: Re: Interval censored survival model
Date   Fri, 25 Jan 2013 09:37:11 -0000


Date: Thu, 24 Jan 2013 15:58:41 -0600
From: plumsh <>
Subject: st: Re: Interval censored survival model

> The manual (Page 20 of the Survival Analysis section) explicitly
> that there are no discrete-time models in Stata. The only user-made
> for grouped (interval censored) data that I found are pgmhaz(8),
hshaz, and
> intcens. The first two don't accommodate intervals of unequal length
> unfortunately, the model and the syntax for INTCENS seems a little
> (at least to me at this point).
> My setup: land plots in agricultural use (farmland) have been
converted to
> residential and other commercial uses. Observations on the same land
> are recorded on, say, Jan 1 of 1980, 1997, 2005, and 2010 (same dates
> all parcels in the sample). Thus, the intervals are of unequal length.
> from that, we have stock sampling (the land has been farmed since a
> time ago; no record when and it does not really matter).
> I want to do survival analysis using location (distance to beach,
> schools), demographic (population density, mix, etc.), and economic
> parcel attributes.
> The theory on Grouped Duration Data analysis (particularly the
> constant proportional hazard) is pretty straightforward (section 20.4
> Wooldridge, Econometric Analysis of Cross Section and Panel Data).
> Since I don't have the time to write a readily working function for
the ml
> command, I would greatly appreciate any advice on how to estimate my
> interval censored (grouped) data on land parcels. Pity they didn't
> exact conversion times. My only alternative now is probit/logit codes
> read most of the relevant posts on the Statalist archives).
> Regards
> Sheng

To be frank, I don't see what the problem with using -intcens- (on SSC)
is. To me, the help file gives examples of how to use it. The command
line seeks, inter alia, the time points that define the intervals. To
me, -intcens- is very nice because of (a) the flexibility regarding
interval length (as you say), and (b) it's a convenient way of fitting a
number of continuous time _parametric_ models in the situation where the
available data are interval-censored. The restrictions of -intcens- to
me are: (c) time-varying predictors are not allowed; (d) there is a
particular set of parametric models and these may not suit you; (e) no
unobserved heterogeneity ('frailty'). 

The other user-written commands that you cite (by me, on SSC) handle (c)
and (e). I think they would also be ok if the unequal-length intervals
are the same unequal length for each person. That is, suppose 2 subjects
have the same spell length (number of intervals) recorded. If the first
interval is 2 months long for both (all) subjects, and the second
interval is 1 month long for all subjects, etc., then the likelihood is
fine. (One has to be careful about post-estimation interpretation,

Also check out -stpm- on SSC. I've not used it, but the help file states
that it can handle interval-censored data. There is also -stpm2- on SSC
which is a development of -stpm-, but I am not sure whether it handles
interval-censored data (not mentioned in help file in the same way). If
Paul Lambert or Michael Crowther are list members, perhaps they can
clarify matters.

I don't see how "probit/logit codes" would be a way forward, unless you
were to ignore the impact of elapsed duration on the hazard rate, and
simply model event occurrence.

Stephen P. Jenkins <>
Professor of Economic and Social Policy
Department of Social Policy 
London School of Economics and Political Science
Houghton Street, London WC2A 2AE, UK
Tel: +44(0)20 7955 6527
The Great Recession and the Distribution of Household Incomes, OUP 2013,
Changing Fortunes: Income Mobility and Poverty Dynamics in Britain, OUP
Survival Analysis Using Stata:
Downloadable papers and software:

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