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From |
Laura Gibbons <gibbonsl@u.washington.edu> |

To |
Statalist <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: sample size calculation for binary repeated measures |

Date |
Mon, 17 Sep 2007 10:40:16 -0700 (PDT) |

Thank you, Joseph, this code was very timely as I am just setting out to do something similar today.

I had to also install "ridder" to get this to work, in addition to ovbd. It's called by ovbd.

Many thanks,

Laura

On Sun, 16 Sep 2007, Joseph Coveney wrote:

Louis Apicella wrote: I am trying to determine the sample size for a binary repeated measures study in which we'll collect baseline data from two groups, intervention and control, then expose the intervention group, followed by collecting data twice more post-intervention from both groups. I've been using this code to come up with sample sizes sampsi .45 .55, sd1(.5) sd2(.5) power(.8) method(ancova) pre(1) post(2) r1(.5) r01(.5) My question is that I have only proportions to work with and I'm not sure that sampsi is designed to use proportions when we specify sd1() and sd2(). I also feel that the variance should be closer to .1 for each sample but then my sample size is ridiculously small (n=8). Essentially, what I want to be able to do is a comparison of the change in the intervention group to the change in the control group. Any ideas? -------------------------------------------------------------------------------- From the dialogue screens, it seems that -sampsi- only works with continuous data for repeated measurements. The -sd1() sd2()- options wouldn't be invoked when using -sampsi- with proportions. There might very well be an analytical method in the literature for power analysis for your situation; Roger Newson has already mentioned one approach. But have you considered using simulation as another? See www.stata.com/support/faqs/stat/power.html and www.stata-press.com/journals/sjabstracts/st0010.pdf for some contributions by Alan Feiveson on the topic. If an analytical method that fits the bill is readily available and facile, then Monte Carlo simulation won't be attractive, but the latter is always doable and often competitive with a trip to the library (and perhaps even with JSTOR) with respect to overall time spent. In your case, you can assess sensitivity toward assumptions regarding correlation, check out link functions, and explore population-average verus subject-specific modeling approaches, as well. See the do-file below for an example of link function exploration using GEE with your ANCOVA-like set up and assumptions for proportions and correlations; it displays power ("Mean" in -summarize-'s output) for the canonical link function ("A") and identity link function ("B") as it climbs through a series of three candidate treatment group sample sizes under the alternative hypothesis, and confirms the Type I error rate (displaying it in the same way as it does power) using the smallest of the probe per-group sample sizes under the null hypothesis. (The illustration do-file requires a user-written command, -ovbd-, that you can download from the SSC archive.) With a subject-specific model (-xtlogit , re- or -gllamm-), the simulation approach will demand increasing patience with increasing integration points. (It's not included below because the program as originally written used -xtlogit , re-, but you can often take advantage of -contract- and [fweight =] to speed things up.) Although I took a chance in the do-file below (especially with Model B), putting a limit on the number of iterations is a good idea when using an iterative analytical method in the simulation program. Note that the illustration do-file and output shown below shouldn't be construed as advocating a risk difference (identity link) model, but if the observed proportions are all in the neighborhood of 50% as you expect, then you might be lucky enough to get away with it. Joseph Coveney clear * set more off set seed `=date("2007-09-16", "YMD")' * capture program drop binsimem program define binsimem, rclass version 10 syntax , n(integer) [Corr(real 0.5) NUll] tempname Control Experimental Correlation tempfile tmpfil0 if ("`null'" == "") { matrix define `Control' = (0.5, 0.55, 0.55) matrix define `Experimental' = (0.5, 0.45, 0.45) } else { matrix define `Control' = (0.5, 0.5, 0.5) matrix define `Experimental' = (0.5, 0.5, 0.5) } matrix define `Correlation' = J(3, 3, `corr') + /// I(3) * (1 - `corr') drop _all ovbd bas res0 res1, means(`Control') /// corr(`Correlation') n(`n') clear generate byte trt = 0 save `tmpfil0' drop _all ovbd bas res0 res1, means(`Experimental') /// corr(`Correlation') n(`n') clear generate byte trt = 1 append using `tmpfil0' erase `tmpfil0' generate int pid = _n reshape long res, i(pid) j(tim) generate byte bia = trt * bas generate byte tia = trt * tim xtgee res bas trt tim bia tia, i(pid) t(tim) /// family(binomial) link(logit) corr(exchangeable) test trt bia tia return scalar A_p = r(p) /* xtlogit res bas trt tim bia tia, i(pid) re /// intmethod(mvaghermite) intpoints(30) */ xtgee res bas trt tim bia tia, i(pid) t(tim) /// family(binomial) link(identity) corr(exchangeable) test trt bia tia return scalar B_p = r(p) end * forvalues n = 250(50)350 { quietly simulate A_p = r(A_p) /// B_p = r(B_p), reps(1000): binsimem , n(`n') display in smcl as text _newline(1) "n = " as result %3.0f `n' capture noisily assert !missing(A_p) & !missing(B_p) generate byte A_pos = (A_p < 0.05) generate byte B_pos = (B_p < 0.05) summarize *_pos } local n 250 quietly simulate A_p = r(A_p) B_p = r(B_p), /// reps(1000): binsimem , n(`n') null display in smcl as text _newline(1) "NULL n = " as result %3.0f `n' capture noisily assert !missing(A_p) & !missing(B_p) generate byte A_pos = (A_p < 0.05) generate byte B_pos = (B_p < 0.05) summarize *_pos exit Edited results: n = 250 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- A_pos | 1000 .754 .430894 0 1 B_pos | 1000 .768 .4223202 0 1 n = 300 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- A_pos | 1000 .825 .3801572 0 1 B_pos | 1000 .833 .3731625 0 1 n = 350 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- A_pos | 1000 .877 .3286016 0 1 B_pos | 1000 .884 .3203852 0 1 [Snip] NULL n = 250 [Snip] Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- A_pos | 1000 .046 .2095899 0 1 B_pos | 1000 .051 .2201078 0 1 . exit end of do-file * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Laura E. Gibbons, PhD General Internal Medicine, University of Washington Box 359780 Harborview Medical Center 325 Ninth Avenue, Seattle, WA 98104 phone: 206-744-1842 fax: 206-744-9917 Office address: 401 Broadway, Suite 5122 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: sample size calculation for binary repeated measures***From:*Joseph Coveney <jcoveney@bigplanet.com>

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