[R] power and sample size for a GLM with Poisson response variable

Wassell, James T., Ph.D. jtw2 at CDC.GOV
Wed Mar 8 16:01:54 CET 2006

Craig, Thanks for your follow-up note on using the asypow package.  My
problem was not only constructing the "constraints" vector but, for my
particular situation (Poisson regression, two groups, sample sizes of
(1081,3180), I get very different results using asypow package compared
to my other (home grown) approaches.  

info.pois <- info.poisson.kgroup(pois.mean, group.size=c(1081,3180))
constraints <- matrix(c(2,1,2), ncol = 3, byrow=T)
poisson.object <- asypow.noncent(pois.mean, info.pois,constraints)
power.pois <- asypow.power(poisson.object, c(1081,3180), 0.05)

[1] 0.2438309

asy.pwr()   #  the function is shown below.

[1] 0.96

sim.pwr()   #  the function is shown below.
4261000 Poisson random variates simulated 
[1] 0.567

I tend to think the true power is greater than 0.567 but less than 0.96
(not as small as 0.2438309).  

Maybe I am still not using the asypow functions correctly?
Suggestions/comments welcome.  

-----Original Message-----
From: Craig A. Faulhaber [mailto:caf at gis.usu.edu] 
Sent: Saturday, March 04, 2006 12:04 PM
To: Wassell, James T., Ph.D.
Subject: Re: [R] power and sample size for a GLM with Poisson response

Hi James,

Thanks again for your help.  With the assistance of a statistician
colleauge of mine, I figured out the "constraints" vector.  It is a
3-column vector describing the null hypothesis.  For example, let's say
you have 3 populations and your null hypothesis is that there is no
difference between the 3.  The first row of the matrix would be "3 1 2" 
indicating that you have 3 populations and that populations 1 and 2 are
equal.  The second row would be "3 2 3" indicating that you have 3
populations and that populations 2 and 3 are equal.  If you had only 2
populations, there would be only one row ("2 1 2", indicating 2
populations with 1 and 2 equal).  I hope this helps.


Wassell, James T., Ph.D. wrote:

> Craig,   I found the package asypow difficult to use and it did not 
> yield results in the ballpark of other approaches.   (could not figure

> out the "constraints" vector).  
> I wrote some simple functions, one asy.pwr uses the non-central 
> chi-square distn.
> asy.pwr<-function(counts=c(7,1),py=c(1081,3180))
> { # a two group Poisson regression power computation #  py is 
> person-years or person-time however measured
> group<-gl(2,1)
> ncp<-summary(glm(counts~group+offset(log(py)),family=poisson))$null.de
> viance
> q.tile<-qchisq(.95,1)  #  actually just the X2 critical value of 
> 3.841459 list(power=round(1-pchisq(q.tile,df=1,ncp),2))}
> The second function, sim.pwr,  estimates power using simulated Poisson

> random variates.  The most time consuming step is the call to rpois.

> (Maybe someone knows a more efficient way to accomplish this?).  The 
> "for" loop is rather quick in comparison.   I hope you may find this 
> helpful, or if you have solved your problem some other way, please 
> pass along your approach.    Note, that for this problem,  very small 
> values of lambda, the two approaches give  much different power 
> estimates  (96% vs. 55% or so).   My problem may be better addressed 
> as binomial logistic regression, maybe then the simulation and the 
> asymptotic estimates my agree better.   
> sim.pwr<-function(means=c(0.0065,0.0003),ptime=c(1081,3180),nsim=1000)
> { # a two group poisson regression power computation # based 
> simulating lots of Poisson r.v.'s # input rates followed by a vector 
> of the corresponding person times # the most time consuming part is 
> the r.v. generation.
> # power is determined by counting the how often p-values are <= 0.05
> group<-as.factor(rep(c(1,2),ptime))
> rej<-vector(length=nsim)
> y<-rpois(ptime[1]*nsim,means[1])
> y<-c(y,rpois(ptime[2]*nsim,means[2]))
> y<-matrix(y,nrow=nsim)
> cat(sum(ptime)*nsim,"Poisson random variates simulated","\n") for(i in

> 1:nsim){res<-glm(y[i,]~group,family=poisson())
> rej[i]<-summary(res)$coeff[2,4]<=0.05}
> list(power=sum(rej)/nsim) }

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