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From |
Douglas Levy <douglas_levy@post.harvard.edu> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Tue, 28 Dec 2010 14:08:19 -0500 |

Is there a way to do this in Stata 10? On Thu, Dec 23, 2010 at 4:59 PM, Steven Samuels <sjsamuels@gmail.com> wrote: > Actually, the following code will work whether or not exposure was a stratum > variable at any stage. > > 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*************************** > > > > > > > Use -margins-, but without knowing the survey design it's hard to say more. > Were separate samples taken from the "exposed" and "unexposed" units > (whatever they were)? Were the PSUs stratified by exposure status? Describe > the design and your -svyset- statement. > > > 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 > > > On Tue, Oct 26, 2010 at 1:34 PM, Steven Samuels <sjsamuels@gmail.com> wrote: >> >> -- >> >> Your second suggestion would be an estimate of the average effect of >> treatment (exposure, here) among the treated (ATT). For an overview of >> possibilities, see Austin Nichols's 2010 conference presentations; his 2007 >> Stata Journal Causal Inference article; and the 2008 Erratum, all linked at >> http://ideas.repec.org/e/pni54.html. >> >> Holding covariates at the means in non-linear models can be dangerous. >> For an example, see >> http://www.stata.com/statalist/archive/2010-07/msg01596.html and 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/ > * * 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:*Steven Samuels <sjsamuels@gmail.com>

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

**Re: st: predicted values in svy glm l(log) f(poisson)***From:*Steven Samuels <sjsamuels@gmail.com>

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