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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 15:09:17 -0500 |

My apologies for not pre-specifying my version. Reading up on -predictnl-, this gives predictions (and possibly variances, etc.) for *each observation*. What I'm interested in is establishing confidence bounds on the difference in the average predicted values when the exposure is present (=1) and when the exposure is absent (=0). Would the variance of this difference be the sum of the variances of the observation-specific predictions? -Doug On Tue, Dec 28, 2010 at 2:49 PM, Steven Samuels <sjsamuels@gmail.com> wrote: > -- > > > In Stata 10, the easiest way will be to use -predictnl- after -svy: glm-. > Please read the FAQ next time. Section 3.3 states: > > "The current version of Stata is 11.1. Please specify if you are using an > earlier version; otherwise, the answer to your question is likely to refer > to commands or features unavailable to you." > > > Steve > > On Dec 28, 2010, at 2:08 PM, Douglas Levy wrote: > > 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/ > > * > * 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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