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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, 30 Dec 2010 14:53:18 -0500 |

Doug:

http://www.demographic-research.org/volumes/vol23/25/23-25.pdf

Steve Steven J. Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 --

Steve On Dec 28, 2010, at 2:49 PM, Steven Samuels wrote:

Steve On Dec 28, 2010, at 2:08 PM, Douglas Levy wrote: Is there a way to do this in Stata 10?

Actually, the following code will work whether or not exposure was astratumvariable 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)) postvce(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 tosay more.Were separate samples taken from the "exposed" and "unexposed" units(whatever they were)? Were the PSUs stratified by exposure status?Describethe 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, DougOn 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 oftreatment (exposure, here) among the treated (ATT). For anoverview ofpossibilities, see Austin Nichols's 2010 conference presentations;his 2007Stata Journal Causal Inference article; and the 2008 Erratum, alllinked athttp://ideas.repec.org/e/pni54.html.Holding covariates at the means in non-linear models can bedangerous.For an example, seehttp://www.stata.com/statalist/archive/2010-07/msg01596.html andMichael 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]samplemeans. 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/ * * 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: 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>

**Re: 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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