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st: Interval censoring using intcens |

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Sun, 29 Jul 2012 13:02:07 +0100 |

Steve Samuels provided very good advice. Some other reflections from me: -intcens- (on SSC) is a program that fits parametric _continuous_ survival time distributions to interval-censored survival time data (a.k.a. as grouped or discrete time data). The program doesn't allow time-varying covariates. It has one row per spell/obs -- convenient for the maximisation by -ml-. I'm not sure that -stpm- (which you ask about) is appropriate for interval-censored data. I would check further if I were you. (If it is, then also check out -stpm2- which is more flexible and faster. Use -findit- to get latest version -- it's from SJ or SSC.) You could think more generally about models for interval-censored data -- see the MS and lessons off my survival analysis webpages (URL below) for discussion and references. This shows how you can fit models which make no assumption about the shape of the underlying survival time distribution. (You can assume shapes for the interval-hazard if you wish; but can also assume interval-specific values if you wish and your data allow it.) And time-varying covariates can be easily incorporated. More complicated is what to do with multiple spells. (You don't mention them explicitly, but it sounds as if you have them according to your description.) The key issue is non-independence across spells from the same person. Steve Samuels remarked on this and suggested clustering the standard errors (persons as clusters). An alternative is to assume some parametric form for the individual-specific effect that generates the non-independence across spells from the same person -- this is 'frailty' a.k.a. 'unobserved heterogeneity'. The most straightforward of handling this would be: * Reorganise (expand) your data so that you have one row in data set for each interval that each person is at risk of infection, and create an event occurrence indicator y_it for person i and interval t (see my Lessons) * Create any time-varying covariates required. At minimum, this will be some specification for the duration dependence of the interval hazard * fit a -xtcloglog- model with the binary outcome variable being y_it. This assumes that the person-specific frailty is normal (Gaussian). Or just fit a -cloglog- model if you want to ignore frailty. Either way, you would be fitting the interval-censored model corresponding to an underlying continuous time model that satisfies the proportional hazards assumption. (That assumption can be tested using interactions between explanatory variables and the variables summarising duration dependence.) An alternative would be -xtlogit- and -logit- to data organised in the same way. [Cf. -pgmhaz8- and -hshaz- (on SSC) which also fit discrete time proportional hazards models with frailty (Gamma, and discrete mass point, respectively), but only to single spell data. -xtcloglog- and -xtlogit- work with multiple spell data because the frailty is integrated out numerically.] Stephen ------------------------------------- Professor Stephen P. Jenkins <[email protected]> Department of Social Policy London School of Economics and Political Science Houghton Street, London WC2A 2AE, U.K. Tel: +44 (0)20 7955 6527 Changing Fortunes: Income Mobility and Poverty Dynamics in Britain, OUP 2011, http://ukcatalogue.oup.com/product/9780199226436.do Survival Analysis using Stata: http://www.iser.essex.ac.uk/survival-analysis Downloadable papers and software: http://ideas.repec.org/e/pje7.html ---------------------------------------------------------------------- Date: Sat, 28 Jul 2012 09:29:15 +0100 From: Patrick Munywoki <[email protected]> Subject: st: Interval censoring using intcens Hi, I have been attempting to analyse interval censored time-to-event data with 'intcens' ado (Griffin et al 2006). My data arise from a longitudinal household-based study with nasal swab collections twice-a-week for a duration of 26 weeks regardless of their any symptoms. I want to be able to estimate the duration of infectious period for one of the viruses we detected. I have reduced the data to one observation per infection episode in order to use the 'intcens' command with t0 being the date last positive sample while t1 is the date of the next negative sample. I hope this data conversion to single observation per infection episode data is alright? My questions? 1. How do i interpret the coefficient given in the results below? intcens t0 t1 male, dist(exp) time nolog stata output Exponential distribution - log acceleration factors Uncensored 0 Right-censored 0 Left-censored 0 Interval-censored 188 Number of obs = 188 Wald chi2(1) = 0.00 Log likelihood = -1796.982 Prob > chi2 = 0.9990 Coef. Std. Err. z P>z [95% Conf. Interval] male -.0001871 .1470683 -0.00 0.999 -.2884356 .2880615 _cons 9.817517 .2234524 43.94 0.000 9.379558 10.25548 Note the actual interval between the dates t0 and t1 is on average(sd) 3.6 (0.98) days; median(IQR) 4 (3-4) days; and range 2-7 days. 2. Whenever i try using any other distribution this error message pops up. What could be the problem here? intcens t0 t1 male, dist(weib) time nolog initial values not feasible r(1400); 3. Is there an alternative method to the interval censoring which allows me to use the multiple records per person accounting for the interval censoring. I have tried stpm but not sure whether it allows for this. I would greatly appreciate your help , Many thanks, - -- Patrick Munywoki Please access the attached hyperlink for an important electronic communications disclaimer: http://lse.ac.uk/emailDisclaimer * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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