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From |
Hilde Karlsen <Hilde.Karlsen@hio.no> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Sun, 15 Feb 2009 21:43:40 +0100 |

Regards, Hilde Quoting Steven Samuels <sjhsamuels@earthlink.net>:

I agree with Austin. Just to be clear: sigma_u is a parameter thatis meaningless for this problem, No interpretation is possible.On Feb 15, 2009, at 9:22 AM, Austin Nichols wrote: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 yousuggest because I have to use a multilevel approach for this essay(it is anessay for a multilevel course I followed a while ago). I should probablyhave been more clear on this issue, and on what my problem reallyis. What Iam wondering is not which method/command I should use, but how Iam going tointerprete 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 Ishould 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 whichdate/monthpeople 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, notdays, of starting and quitting, use that time unit in a survivalor discretesurvival 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/* * 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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**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>

**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>

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