Sample size calculation for repeated measures using ancova

[Megan Moreton]

Hi,

I am looking to repeat the power calculation found in this protocol (sample size text attached): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4950103/

The trial is a 2 group, 2 measure study (baseline & follow-up), comparing change in a biomarker (HbA1c). The estimated clinically relevant difference is 0.5%, with a cross-sectional SD of 1.1 and correlation of 0.4. The power is set to 90%.

I try to repeat this using the following:

power repeated 0 0 \ 0 0.5, factor(bwithin) corr(0.4) varerror(1.21) power(0.9)

But I think I am misunderstanding something as my results differ. Does anyone have advice on how to perform this calculation?

Thank you very much,
Megan

[Joseph Coveney]

. . . I think I am misunderstanding something as my results differ. Does anyone have advice on how to perform this calculation?

Your sample-size estimation command is for repeated-measures ANOVA. According to the excerpt that you've copied and pasted, they're not using a repeated-measures ANOVA, but rather an ANCOVA.

Here's my take on what they did. (non is the nonpregnant-patient cohort and prg is the pregnant-patient cohort, which as implied in the abstract at the URL that you posted are randomized separately to the two treatment groups.)

.ÿ
.ÿversionÿ16.1

.ÿ
.ÿclearÿ*

.ÿ
.ÿsetÿseedÿ`=strreverse("1578398")'

.ÿ
.ÿprogramÿdefineÿsimem,ÿrclass
ÿÿ1.ÿÿÿÿÿversionÿ16.1
ÿÿ2.ÿÿÿÿÿsyntaxÿ,ÿn1(integer)ÿn2(integer)
ÿÿ3.ÿ
.ÿÿÿÿÿdropÿ_all
ÿÿ4.ÿ
.ÿÿÿÿÿ//ÿNonpregnantÿpatients
.ÿÿÿÿÿdrawnormÿout0ÿout1,ÿdoubleÿcorr(1ÿ0.4ÿ\ÿ0.4ÿ1)ÿ///
>ÿÿÿÿÿÿÿÿÿsd(0.8ÿ0.8)ÿn(`n1')
ÿÿ5.ÿÿÿÿÿgenerateÿbyteÿtrtÿ=ÿmod(_n,ÿ2)
ÿÿ6.ÿÿÿÿÿreplaceÿout1ÿ=ÿout1ÿ-ÿ0.5ÿ*ÿtrt
ÿÿ7.ÿ
.ÿÿÿÿÿanovaÿout1ÿi.trtÿc.out0
ÿÿ8.ÿÿÿÿÿtestÿ1.trt
ÿÿ9.ÿÿÿÿÿtempnameÿnon
ÿ10.ÿÿÿÿÿscalarÿdefineÿ`non'ÿ=ÿr(p)ÿ<ÿ0.05
ÿ11.ÿ
.ÿÿÿÿÿ//ÿPregnantÿpatients
.ÿÿÿÿÿdropÿ_all
ÿ12.ÿÿÿÿÿdrawnormÿout0ÿout1,ÿdoubleÿcorr(1ÿ0.4ÿ\ÿ0.4ÿ1)ÿ///
>ÿÿÿÿÿÿÿÿÿsd(1.1ÿ1.1)ÿn(`n2')
ÿ13.ÿÿÿÿÿgenerateÿbyteÿtrtÿ=ÿmod(_n,ÿ2)
ÿ14.ÿÿÿÿÿreplaceÿout1ÿ=ÿout1ÿ-ÿ0.5ÿ*ÿtrt
ÿ15.ÿ
.ÿÿÿÿÿanovaÿout1ÿi.trtÿc.out0ÿ
ÿ16.ÿÿÿÿÿtestÿ1.trt
ÿ17.ÿÿÿÿÿreturnÿscalarÿprgÿ=ÿr(p)ÿ<ÿ0.05
ÿ18.ÿÿÿÿÿreturnÿscalarÿnonÿ=ÿ`non'
ÿ19.ÿend

.ÿ
.ÿlocalÿn1ÿ=ÿround(110ÿ-ÿ0.15ÿ*ÿ110,ÿ2)ÿ//ÿ15%ÿattritionÿinÿnonpregnantÿpatients

.ÿlocalÿn2ÿ=ÿround(214ÿ-ÿ0.20ÿ*ÿ214,ÿ2)ÿ//ÿ20%ÿattritionÿinÿpregnantÿpatients

.ÿdisplayÿinÿsmclÿasÿtextÿ"Sampleÿsizeÿafterÿattritionÿ=ÿ"ÿ///
>ÿÿÿÿÿasÿresultÿ`n1',ÿ`n2'ÿasÿtextÿ"ÿ(even-numberedÿinÿorderÿtoÿallowÿrandomization)"
Sampleÿsizeÿafterÿattritionÿ=ÿ94ÿ172ÿ(even-numberedÿinÿorderÿtoÿallowÿrandomization)

.ÿ
.ÿquietlyÿsimulateÿnonÿ=ÿr(non)ÿprgÿ=ÿr(prg),ÿreps(300)ÿnodots:ÿsimemÿ,ÿ///
>ÿÿÿÿÿn1(`n1')ÿn2(`n2')

.ÿ
.ÿforeachÿvarÿofÿvarlistÿnonÿprgÿ{
ÿÿ2.ÿÿÿÿÿsummarizeÿ`var',ÿmeanonly
ÿÿ3.ÿÿÿÿÿdisplayÿinÿsmclÿasÿtextÿ"Powerÿ(`var')ÿ=ÿ"ÿasÿresultÿ%04.2fÿr(mean)
ÿÿ4.ÿ}
Powerÿ(non)ÿ=ÿ0.89
Powerÿ(prg)ÿ=ÿ0.90

.ÿ
.ÿexit

endÿofÿdo-file


.

I tend to use simulation instead of canned procedures—it helps make sure that when the time comes I understand how to set up the primary analysis and that the method of analysis is feasible.

[Megan Moreton]

This is amazingly helpful - thank you very much for your time & assistance