[R-sig-ME] Minimum detectable effect size in linear mixed model
Steve Denham
@tevedrd @end|ng |rom y@hoo@com
Mon Jul 6 15:49:44 CEST 2020
Power analysis is prospective, never retrospective. You already know the results.
Steve Denham Senior Biostatistical Scientist, Charles River Laboratories
On Friday, July 3, 2020, 07:04:00 PM EDT, Patrick (Malone Quantitative) <malone using malonequantitative.com> wrote:
No, because I don't think it can be. That's not how power analysis works.
It's bad practice.
On Fri, Jul 3, 2020, 6:42 PM Han Zhang <hanzh using umich.edu> wrote:
> Hi Pat,
>
> Thanks for your quick reply. Yes, I already have the data and the actual
> effects, and the analysis was suggested by a reviewer. Can you elaborate on
> when do you think such an analysis might be justified?
>
> Thanks!
> Han
>
> On Fri, Jul 3, 2020 at 6:34 PM Patrick (Malone Quantitative) <
> malone using malonequantitative.com> wrote:
>
>> Han,
>>
>> (1) Usually, yes, but . . .
>>
>> (2) If you have an actual effect, does that mean you're doing post hoc
>> power analysis? If so, that's a whole can of worms, for which the best
>> advice I have is "don't do it." Use the size of the confidence
>> interval of your estimate as an assessment of sample adequacy.
>>
>> Pat
>>
>> On Fri, Jul 3, 2020 at 6:27 PM Han Zhang <hanzh using umich.edu> wrote:
>> >
>> > Hello,
>> >
>> > I'm trying to find the minimum detectable effect size (MDES) given my
>> > sample, alpha (.05), and desired power (90%) in a linear mixed model
>> > setting. I'm using the simr package for a simulation-based approach.
>> What I
>> > did is changing the original effect size to a series of hypothetical
>> effect
>> > sizes and find the minimum effect size that has a 90% chance of
>> producing a
>> > significant result. Below is a toy code:
>> >
>> > library(lmerTest)
>> > library(simr)
>> >
>> > # fit the model
>> > model <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
>> > summary(model)
>> >
>> > Fixed effects:
>> > Estimate Std. Error df t value Pr(>|t|)
>> > (Intercept) 251.405 6.825 17.000 36.838 < 2e-16 ***
>> > Days 10.467 1.546 17.000 6.771 3.26e-06 ***
>> >
>> >
>> > Here is the code for minimum detectable effect size:
>> >
>> > pwr <- NA
>> >
>> > # define a set of reasonable effect sizes
>> > es <- seq(0, 10, 2)
>> >
>> > # loop through the effect sizes
>> > for (i in 1:length(es)) {
>> > # replace the original effect size with new one
>> > fixef(model)['Days'] = es[i]
>> > # run simulation to obtain power estimate
>> > pwr.summary <- summary(powerSim(
>> > model,
>> > test = fixed('Days', "t"),
>> > nsim = 100,
>> > progress = T
>> > ))
>> > # store output
>> > pwr[i] <- as.numeric(pwr.summary)[3]
>> > }
>> >
>> > # display results
>> > cbind("Coefficient" = es,
>> > Power = pwr)
>> >
>> > Output:
>> >
>> > Coefficient Power
>> > [1,] 0 0.09
>> > [2,] 2 0.24
>> > [3,] 4 0.60
>> > [4,] 6 0.99
>> > [5,] 8 1.00
>> > [6,] 10 1.00
>> >
>> > My questions:
>> >
>> > (1) Is this the right way to find the MDES?
>> >
>> > (2) I have some trouble making sense of the output. Can I say the
>> > following: because the estimated power when the effect = 6 is 99%, and
>> > because the actual model has an estimate of 10.47, then the study is
>> > sufficiently powered? Conversely, imagine that if the actual estimate
>> was
>> > 3.0, then can I say the study is insufficiently powered?
>> >
>> > Thank you,
>> > Han
>> > --
>> > Han Zhang, Ph.D.
>> > Department of Psychology
>> > University of Michigan, Ann Arbor
>> > https://sites.lsa.umich.edu/hanzh/
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > _______________________________________________
>> > R-sig-mixed-models using r-project.org mailing list
>> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>>
>> --
>> Patrick S. Malone, Ph.D., Malone Quantitative
>> NEW Service Models: http://malonequantitative.com
>>
>> He/Him/His
>>
>
>
> --
> Han Zhang, Ph.D.
> Department of Psychology
> University of Michigan, Ann Arbor
> https://sites.lsa.umich.edu/hanzh/
>
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