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From |
Steven Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: predicted values in svy glm l(log) f(poisson) |

Date |
Thu, 23 Dec 2010 16:59:55 -0500 |

Steve Steven J. Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 **************************CODE BEGINS************************** sysuse auto, clear svyset turn [pw= trunk] replace foreign = foreign +1 //convenient for -margins- // foreign =2 is the treated group svy: glm rep78 mpg weight i.foreign, link(log) family(poisson) margins, subpop(if foreign==2) at(foreign=(1,2)) post vce(unconditional) // _at2 is foreign as foreign _at1 is foreign as domestic lincom _b[2._at]- _b[1._at] //ATT margins, coeflegend //If you forget the coefficient names lincom _b[2._at] - _b[1bn._at] ***************************CODE ENDS***************************

Steve On Dec 23, 2010, at 2:03 PM, Douglas Levy wrote: I am now revisiting this issue, having, with Steve's guidance, settled on option #2 from my original post. I.e., estimate glm model; predict daysmissed for exposed=1; predict daysmissed for the exposed group when exposed is set to 0; take difference of the [weighted] means of the predictions. Now my question is, how can I put confidence bounds on the difference in the mean predictions? I thank the group for any help it can offer. Best, Doug

--Your second suggestion would be an estimate of the average effect oftreatment (exposure, here) among the treated (ATT). For an overviewof possibilities, see Austin Nichols's 2010 conferencepresentations; his 2007 Stata Journal Causal Inference article; andthe 2008 Erratum, all linked at http://ideas.repec.org/e/pni54.html.Holding covariates at the means in non-linear models can bedangerous. For an example, see http://www.stata.com/statalist/archive/2010-07/msg01596.htmland Michael N. Mitchell's followup.Steve Steven J. Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 On Oct 26, 2010, at 11:24 AM, Douglas Levy wrote: I have complex survey data on school days missed for an exposed and unexposed group. I have modeled the effect of exposure on absenteeism using svy: glm daysmissed exposure $covariates, l(log) f(poisson). I would like to estimate adjusted mean days missed for the exposed and control groups, but I'm not sure of the best way to deal with this in a non-linear model. There are a couple of methods I've encountered, and I would be grateful for some thoughts on the pros and cons of each. 1. Estimate glm model. Reset all covariates to their [weighted] sample means. Predict daysmissed when exposed=0 and when exposed=1. 2. Estimate glm model. Predict daysmissed for exposed=1. Predict daysmissed for the exposed group when exposed is set to 0. Take the [weighted] means of the predictions. 3. Other suggestions? Thanks. -Doug * * 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/

* * 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/

**Follow-Ups**:**Re: st: predicted values in svy glm l(log) f(poisson)***From:*Douglas Levy <douglas_levy@post.harvard.edu>

**References**:**st: predicted values in svy glm l(log) f(poisson)***From:*Douglas Levy <douglas_levy@post.harvard.edu>

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