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From |
Hitesh Chandwani <hchandwani.stata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: How to model a positive continuous dependent variable with many zeros? |

Date |
Tue, 31 May 2011 09:19:02 -0400 |

Have you considered a two-part model? You could first run a logistic regression to get the probability of having a non-zero seclusion duration. Then, depending on the distribution of non-zero duration, you could run the relevant GLM. Of course, whether or not to run a two-part model will depend on the question you are trying to answer. Regards, Hitesh S. Chandwani University of Texas at Austin On Tue, May 31, 2011 at 3:24 AM, Adriaan Hoogendoorn <aw.hoogendoorn@gmail.com> wrote: > Dear Statalisters, > > I try to run regression models for two dependent variables that > concern the seclusions of psychiatric patients: > y1 = the number of seclusion incidents and y2 = the seclusion duration. > Fortunately (at least from the patients perspective), there are many zeroes. > I successfully applied a Poisson model (xtpoisson) to model the number > of seclusion incidents > for patients (level 1) in different clinics (level 2) > taking exposure time t (the time that a psychiatric patient spent in > the clinic) into account: > xtpoisson y1 x1 x2 x3, re exposure(t) > > I am running into problems when I try modeling the duration of seclusions. > Because of the many zeroes (85%) and the successful analysis of the > number of seclusion incidents, I applied the xtpoisson model for the duration > variable as well. Obviously duration it is not exactly a count variable, > but I can count the number of hours within the duration, or the number > of days, can’t I? So I estimated the duration counting the number of hours. > > The problem appears when I alternatively estimate the duration by counting > the number of hours. I seem to get different model estimates for duration when > I count the number of days than when I count the number of hours. In fact, > not so much the parameter estimates change, but their significance levels > are very sensitive to the scale (days or hours or even minutes) on which > duration is measured. > > It appears that in > xtpoisson y2 x1 x2 x3, re exposure(t) > it does not matter if “t” is measured in days or hours, but it does > matter if the duration “y2” is measured in days, hours or minutes. > > So my trick of counting days or hours seems to fail, and modeling > seclusion duration by a poisson model seems not a good idea. > Therefore my question to you is: do you know of a model that can deal > with a positive continuous dependent variable (duration) with many > zeros? > > Kind regards, > Adriaan Hoogendoorn > GGZ inGeest, Amsterdam > > * > * 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/ > -- Hitesh S. Chandwani University of Texas at Austin * * 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/

**References**:**st: How to model a positive continuous dependent variable with many zeros?***From:*Adriaan Hoogendoorn <aw.hoogendoorn@gmail.com>

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