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From |
Patrick Munywoki <pmunywoki@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Interval censoring using intcens |

Date |
Wed, 1 Aug 2012 11:58:21 +0100 |

Many thanks for the suggestions. The main problem in my dataset is i do not have an exact date/time of when the study participants either started or stopped shedding the respiratory virus of interest. note i sample participant twice-a-week hence there are intervals of 3 to 4 days(longer in cases where sample was not collected) between sample collections for all the participants. Any further ideas on how to analyse this data is welcome. I am currently thinking of using imputation techniques to determine when the infection episodes started and ended before i proceed with the survival analysis. Your thoughts on this approach is also welcome. Thanks Patrick On 29 July 2012 13:02, <S.Jenkins@lse.ac.uk> wrote: > > 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 <s.jenkins@lse.ac.uk> > 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 <pmunywoki@gmail.com> > 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/ -- Patrick Munywoki * * 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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