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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: AW: Sample selection models under zero-truncated negative binomial models |

Date |
Fri, 5 Jun 2009 12:14:58 -0400 |

John Ataguba <johnataguba@yahoo.co.uk> : Again, why split the analysis? If you are interested in the count, use a count model, and then talk about what the results from that model predict about the probability of a nonzero count when you are interested in whether people have any visits. You don't seem to have any theory requiring "standard logit/probit model" assumptions. -poisson- seems the natural starting point. Why would you drop the zeros when trying to assess how many GP visits a person seems likely to make conditional on X? Zero is one possible outcome... On Fri, Jun 5, 2009 at 10:03 AM, John Ataguba <johnataguba@yahoo.co.uk> wrote: > Hi Austin, > > Specifically, I am not looking at the time dimension of the visits. The data set is such that I have total number of visits to a GP (General Practitioner) in the past one month collected from a national survey of individuals. Given that this is a household survey, there are zero visits for some individuals. > > One of my objective is to determine the factors that predict positive utilization of GPs. This is easily implemented using a standard logit/probit model. The other part is the factors that affect the number of visits to a GP. Given that the dependent variable is a count variable, the likely candidates are count regression models. My fear is with how to deal with unobserved heterogeneity and sample selection issues if I limit my analysis to the non-zero visits. If I use the standard two-part or hurdle model, I do not know if this will account for sample selection in the fashion of Heckman procedure. > > I think the class of mixture models (fmm) will be an anternative that I want to explore. I don't know much about them but will be happy to have some brighter ideas. > > Regards > > Jon > > > ----- Original Message ---- > From: Austin Nichols <austinnichols@gmail.com> > To: statalist@hsphsun2.harvard.edu > Sent: Friday, 5 June, 2009 14:27:20 > Subject: Re: st: RE: AW: Sample selection models under zero-truncated negative binomial models > > Steven--I like this approach in general, but from the original post, > it's not clear that data on the timing of first visit or even time at > risk is on the data--perhaps the poster can clarify? Also, would you > propose using the predicted hazard in the period of first visit as > some kind of selection correction? The outcome is visits divided by > time at risk for subsequent visits in your setup, so represents a > fractional outcome (constrained to lie between zero and one) in > theory, though only the zero limit is likely to bind, which makes it > tricky to implement, I would guess--if you are worried about the > nonnormal error distribution and the selection b > > Ignoring the possibility of detailed data on times of utilization, why > can't you just run a standard count model on number of visits and use > that to predict probability of at least one visit? One visit in 10 > years is not that different from no visits in 10 years, yeah? It > makes no sense to me to predict utilization only for those who have > positive utilization and worry about selection etc. instead of just > using the whole sample, including the zeros. I.e. run a -poisson- to > start with. If you have a lot of zeros, that can just arise from the > fact that a lot of people have predicted number of visits in the .01 > range and number of visits has to be an integer. Zero inflation or > overdispersion also can arise often from not having the right > specification for the explanatory variables... but you can also move > to another model in the -glm- or -nbreg- family. > > On Tue, Jun 2, 2009 at 1:21 PM, <sjsamuels@gmail.com> wrote: >> A potential problem with Jon's original approach is that the use of >> services is an event with a time dimension--time to first use of >> services. People might not use services until they need them. >> Instead of a logit model (my preference also), a survival model for >> the first part might be appropriate. >> >> With later first-use, the time available for later visits is reduced, >> and number of visits might be associated with the time from first use >> to the end of observation. Moreover, people with later first-visits >> (or none) might differ in their degree of need for subsequent visits. >> >> To account for unequal follow-up times, I suggest a supplementary >> analysis in which the outcome for the second part of the hurdle model >> is not the number of visits, but the rate of visits (per unit time at >> risk). >> >> -Steve. >> >> On Tue, Jun 2, 2009 at 12:22 PM, Lachenbruch, Peter >> <Peter..Lachenbruch@oregonstate.edu> wrote: >>> This could also be handled by a two-part or hurdle model. The 0 vs. non-zero model is given by a probit or logit (my preference) model. The non-zeros are modeled by the count data or OLS or what have you. The results can be combined since the likelihood separates (the zero values are identifiable - no visits vs number of visits). >>> >>> >>> -----Original Message----- >>> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Martin Weiss >>> Sent: Tuesday, June 02, 2009 7:02 AM >>> To: statalist@hsphsun2.harvard.edu >>> Subject: st: AW: Sample selection models under zero-truncated negative binomial models >>> >>> ************* >>> ssc d cmp >>> ************* >>> -----Ursprüngliche Nachricht----- >>> Von: owner-statalist@hsphsun2.harvard.edu >>> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von John Ataguba >>> Gesendet: Dienstag, 2. Juni 2009 16:00 >>> An: Statalist statalist mailing >>> Betreff: st: Sample selection models under zero-truncated negative binomial >>> models >>> >>> Dear colleagues, >>> >>> I want to enquire if it is possible to perform a ztnb (zero-truncated >>> negative binomial) model on a dataset that has the zeros observed in a >>> fashion similar to the heckman sample selection model. >>> >>> Specifically, I have a binary variable on use/non use of outpatient health >>> services and I fitted a standard probit/logit model to observe the factors >>> that predict the probaility of use.. Subsequently, I want to explain the >>> factors the influence the amount of visits to the health facililities. Since >>> this is a count data, I cannot fit the standard Heckman model using the >>> standard two-part procedure in stata command -heckman-. >>> >>> My fear now is that my sample of users will be biased if I fit a ztnb model >>> on only the users given that i have information on the non-users which I >>> used to run the initial probit/logit estimation. >>> >>> Is it possible to generate the inverse of mills' ratio from the probit model >>> and include this in the ztnb model? will this be consistent? etc... >>> >>> Are there any smarter suggestions? Any reference that has used the similar >>> sample selection form will be appreciated. >>> >>> Regards >>> >>> Jon * * 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: RE: AW: Sample selection models under zero-truncated negative binomial models***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**References**:**st: Sample selection models under zero-truncated negative binomial models***From:*John Ataguba <johnataguba@yahoo.co.uk>

**st: RE: AW: Sample selection models under zero-truncated negative binomial models***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**Re: st: RE: AW: Sample selection models under zero-truncated negative binomial models***From:*sjsamuels@gmail.com

**Re: st: RE: AW: Sample selection models under zero-truncated negative binomial models***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: RE: AW: Sample selection models under zero-truncated negative binomial models***From:*John Ataguba <johnataguba@yahoo.co.uk>

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