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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: event history analysis with years clustered in individuals |

Date |
Sun, 15 Feb 2009 09:22:26 -0500 |

Hilde Karlsen <Hilde.Karlsen@hio.no>: If you have to use a mixed model as an exercise, and you have no compelling reason to choose a particular research question, you should ask a different research question where a mixed model is a more appropriate model, not apply it blindly to data you know is better suited to a survival model. Why not use the attrition dummy you have made as the explanatory variable in a mixed model instead--what other variables do you have on the data? On Sun, Feb 15, 2009 at 8:26 AM, Hilde Karlsen <Hilde.Karlsen@hio.no> wrote: > Thank you both for the advice. However, I don't think I can do as you > suggest because I have to use a multilevel approach for this essay (it is an > essay for a multilevel course I followed a while ago). I should probably > have been more clear on this issue, and on what my problem really is. What I > am wondering is not which method/command I should use, but how I am going to > interprete the sigma_u estimate when my level 1 variable is years and my > level 2 variable is individuals. > > As mentioned, I find it more intuitive to grasp the point of separate > variance estimates when the levels are schools, classes etc, but for some > reason I have a hard time understanding how I should interpreate the > variance estimate sigma_u when the years are clustered in individuals. How > should I interpreate sigma_u when years are clustered in individuals. > > I asked the professor who was leading the course which command I should use, > and he told me I should use xtmelogit (my advicor told me the same thing). > As he is the one who is going to judge wheter I pass or not on this essay, > it is probably best to follow his advice. > > I agree that it is a survival model, and I have designed my data for this > type of analysis (i.e. all individuals in the file start out with 0 on the > dependent variable, and when/if they drop out of the nursing occupation, > they receive 1 on the dependent variable. I have no info on which date/month > people drop out; I only have information on which year they drop out). > > Regards, > Hilde > > > Quoting Steven Samuels <sjhsamuels@earthlink.net>: > >> >> Hilde, I agree with Austin's approach. Even if you have only months, not >> days, of starting and quitting, use that time unit in a survival or discrete >> survival model. I recommend Stephen Jenkins's -hshaz- (get it from SSC); >> his "model 1" (the "Prentice-Gloeckler model" is the same as that fit by >> -cloglog-. His model 2 adds unobserved heterogeneity and so may be more >> realistic (Heckman and Singer, 1984). >> >> I would not be surprised if prediction equations for of early and later >> quitting differed. If so, time-dependent covariates or separate models for >> early and later quitting, would be informative. >> >> -Steve >> >> Prentice, R. and Gloeckler L. (1978). Regression analysis of grouped >> survival data with application to breast cancer data. Biometrics 34 (1): >> 57-67. >> >> Heckman, J.J. and Singer, B. (1984). A Method for minimizing the impact of >> distributional assumptions in econometric models for duration data, >> Econometrica, 52 (2): 271-320. >> >> >> >>> Hilde Karlsen <Hilde.Karlsen@hio.no>: >>> Attrition from nursing sounds like a survival model, probably in >>> discrete time, using -logit- or -cloglog- with time dummies, not >>> -xtmelogit- (see >>> http://www.iser.essex.ac.uk/iser/teaching/module-ec968 for a textbook >>> and self-guided course on discrete time survival models). If you have >>> T years of data on each individual, all of whom are first-year nurses >>> in period 1, and some of whom quit nursing in each of the subsequent >>> years, with a variable nurse==1 when a nurse (and zero otherwise), an >>> individual identifier id, a year variable year, and a bunch of >>> explanatory variables x*, you can just: >>> >>> tsset id year >>> bys id (year): g quit=(l.nurse==1 & nurse==0) >>> by id: replace quit=. if l.quit==1 | (mi(l.quit)&_n>1) >>> tab year, gen(_t) >>> drop _t1 >>> logit quit _t* x* >>> >>> and then work up to more complicated models with heterogeneous >>> frailty, etc. The main issues are that someone who quit nursing last >>> year cannot quit nursing again this year, and people who never quit >>> nursing might at some future point that you don't observe, which is >>> why you use survival models... >>> >>> If you know the day they started work and the day they quit, you might >>> prefer a continuous-time model (help st). >>> >>> I've been assuming you had data on people working as nurses, but >>> rereading your email, maybe you have data on breastfeeding mothers, >>> though I suppose the same considerations apply (though with multiple >>> years of data on breastfeeding mothers, there is probably no >>> censoring). >>> >>> On Fri, Feb 13, 2009 at 9:19 AM, Hilde Karlsen <Hilde.Karlsen@hio.no> >>> wrote: >>>> >>>> Dear statalisters, >>>> >>>> This is probably a stupid question, but I've been searching around the >>>> nets >>>> and in books and articles, and I've still not grasped the concept: When >>>> I'm >>>> performing a multilevel analysis of attrition from nursing using >>>> xtmelogit, >>>> and time (year) is the level 1 variable and individuals (id) is the >>>> level 2 >>>> variable (i.e. years are clustered within individuals; I have a >>>> person-year >>>> file), how do I formulate the expectation related to this model? Why is >>>> it >>>> important to separate between these two levels? >>>> >>>> I find it more intuitive to grasp the fact that individuals are >>>> clustered >>>> within schools, and that variables on the school level - as well as >>>> variables on the individual level - may influence e.g. which grades a >>>> student gets. >>>> >>>> I understand (at least I hope I understand) the point that when the same >>>> individuals are followed over a period of time, the individual's >>>> responses >>>> are probably highly correlated, and that this implies a violation to >>>> the >>>> assumption about the heteroskedastic error-terms. As I see it, I could >>>> have >>>> used the cluster() - command (cluster(id))to 'avoid' this violation; >>>> however, I have to write an essay using multilevel analysis, so this is >>>> not >>>> an option. >>>> >>>> I don't know if I'm being clear enough about what my problem is, but any >>>> information regarding this topic (how to grasp the concept of years >>>> clustered in individuals) will be greatly appreciated. >>>> I'm really sorry for having to ask you such an infantile question.. My >>>> colleagues and friends are not familiar with multilevel analyses, so I >>>> don't >>>> know who to turn to. >>>> >>>> Best regards, >>>> Hilde * * 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/

**Follow-Ups**:**Re: st: event history analysis with years clustered in individuals***From:*Steven Samuels <sjhsamuels@earthlink.net>

**References**:**st: event history analysis with years clustered in individuals***From:*Hilde Karlsen <Hilde.Karlsen@hio.no>

**Re: st: event history analysis with years clustered in individuals***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: event history analysis with years clustered in individuals***From:*Steven Samuels <sjhsamuels@earthlink.net>

**Re: st: event history analysis with years clustered in individuals***From:*Hilde Karlsen <Hilde.Karlsen@hio.no>

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