View source for Power Analysis

Commonly we want to know how to design our experiments (''a priori''), or how estimate how big of an effect we could have seen (''a posteriori''). We use two different methods for doing these kinds of analyses. One is using the [https://github.com/heliosdrm/pwr pwr package] in R. Alternatively sometimes we use [https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html G*Power], a standalone software with somewhat more functionality. Here we will focus on pwr, which is sufficient for most of our needs.

= Some Definitions =

; Effect Size
: The effect you expect to see, generally given as Cohen's ''d'' which is calculated as expected difference between groups divided by the standard deviation <math>d=\frac{mean(group1)-mean(group2)}{sd}</math>. If you are asking for the correlation between two parameters it is related to interested in the correlation coefficient rather than the difference between groups (r), you can use this formula <math>d= \frac{2r}{\sqrt{1 - r^2}}</math>
; Power
: The likelihood of being able to observe the effect size (if such an effect exists). Generally for prospective design purposes we use a power of 0.8. This is the same as one minus the false negative rate (FNR; the likelihood that an effect exists, but we cannot detect it, sometimes refered to as <math>\beta</math>), so <math>Power = 1-FNR</math>. Therefore conventionally <math>\beta=0.2</math>. Therefore retrospectively the power says that for some particular effect size, n, and false positive rate, how likely we were to observe that effect.
; False Positive Rate
: The likelihood that we think there is a difference, when there is none, conventionally set as <math>\alpha=0.05</math>.
; Sample Size
: The number used, be it number of participants, number of experimntal animals. '''Not''' the number of technical replicates

These four parameters are related to each other such that if you know three of them, you can calculate the fourth. Some examples:

{|
! Missing Value
! Use Case
|-
| n
| Calculate how many needed in a group
|-
| Power
| Calculate likelihood of seeing an effect
|-
| Effect size
| Calculate detectable difference
|}

= Power Analysis =

::: {.cell}

<pre class="r cell-code">library(pwr)
#desired effect size in standard deviations
effect.size &lt;- 5 # expected difference in absolute terms
assay.sd &lt;- 3 # the standard deviation of the assay, in the same units as the effect size
effect.size.sd &lt;- effect.size/assay.sd
#desired false positive rate
fpr &lt;- 0.05
#desired power (inverse of false negative rate)
power &lt;- 0.8
#calculate required n
required.animals &lt;- pwr.t.test(d=effect.size.sd,
                               power=power,
                               sig.level=fpr,
                               alternative=&quot;greater&quot;,
                               type=&quot;two.sample&quot;)$n</pre>
:::

The assumptions set in this analysis are:

* The desired effect size is 5. This is what we want to power our analysis to be able to detect.
* The standard deviation of the measurement is 3, in the same units as the effect size.
* Therefore Cohen's ''d'' is 1.6666667 or the number of standard deviations we want to be able to detect.
* The acceptable false positive rate is 0.05. This is the percent chance that we observe something that is not actually true.
* The acceptable false negative rate is 0.2. This is the percent chance that we miss something that is actually true.
* The power of our analysis is set at 0.8.

== Calculate Number of Animals ==

At a standard power of 0.8 with a false positive rate of 0.05 and a desired effect size of a 5 difference in percent fat mass we would need '''5.283492''' animals in each group.

== Calculate Detectable Effect Size ==

::: {.cell}

<pre class="r cell-code">required.animals.effect &lt;- round(required.animals)
effective.d &lt;- pwr.t.test(power=power,
                               n=required.animals.effect,
                               sig.level=fpr,
                               alternative=&quot;greater&quot;,
                               type=&quot;two.sample&quot;)$d</pre>
:::

Based on the design above we should expect to detect an effect size of 1.7245893 standard deviations with 0.8 power, 5 animals and a FPR of 0.05.

== Calculate Effective Power ==

::: {.cell}

<pre class="r cell-code">required.animals.power &lt;- round(required.animals)
effective.power &lt;- pwr.t.test(d=effect.size.sd,
                               n=required.animals.power,
                               sig.level=fpr,
                               alternative=&quot;greater&quot;,
                               type=&quot;two.sample&quot;)$power</pre>
:::

Based on the design above we have a 77.5993902% chance of seeing a difference of 1.6666667 with 5 animals and a FPR of 0.05.

The plot below shows how likely we are to detect a difference (the power) as we vary the number of animals (x-axis) and the desired effect size.

::: {.cell}

<pre class="r cell-code">animals &lt;- seq(1:20) #animal range to test
effect.sizes &lt;- seq(1,9,by=1) # effect size range to test
power.table &lt;- expand.grid(animals=animals,effect.sizes=effect.sizes)
power.table$effect.sizes.sd &lt;- power.table$effect.sizes/assay.sd

for (effect.size.sd in power.table$effect.sizes.sd){
for (n.test in power.table$animals){
  power.table[power.table$animals==n.test&amp;power.table$effect.sizes.sd==effect.size.sd,'power'] &lt;- 
    pwr.t.test(d=effect.size.sd,
               n=n.test,
               sig.level=fpr,
               alternative=&quot;greater&quot;,
               type=&quot;two.sample&quot;)$power
}
}


library(ggplot2)
library(RColorBrewer)

power.table$effect.sizes.sd &lt;- as.factor(format(round(power.table$effect.sizes.sd,2),nsmall=2))
p &lt;- ggplot(power.table, aes(y=power,x=animals))
p + geom_line(aes(col=effect.sizes.sd)) +
  labs(y=&quot;Power&quot;,
       x=&quot;Animals&quot;,
       title=&quot;Effective power relative to animal numbers&quot;,
       subtitle=paste(&quot;Based on false positive rate of &quot;, fpr)) +
  geom_hline(yintercept=0.8, lty=2) + 
  scale_colour_manual(&quot;Effect Sizes \n(Number of SD)&quot;, values=brewer.pal(10,'Blues'))</pre>
::: {.cell-output-display} [[File:power-analysis_files/figure-html/effect-size-plot-1.png|672px]] ::: :::

= Session Information =

::: {.cell}

<pre class="r cell-code">sessionInfo()</pre>
::: {.cell-output .cell-output-stdout}

<pre>R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS Monterey 12.7.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Detroit
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] RColorBrewer_1.1-3 ggplot2_3.5.1      pwr_1.3-0         

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5       cli_3.6.3         knitr_1.48        rlang_1.1.4      
 [5] xfun_0.46         generics_0.1.3    jsonlite_1.8.8    labeling_0.4.3   
 [9] glue_1.7.0        colorspace_2.1-1  htmltools_0.5.8.1 scales_1.3.0     
[13] fansi_1.0.6       rmarkdown_2.27    grid_4.4.1        evaluate_0.24.0  
[17] munsell_0.5.1     tibble_3.2.1      fastmap_1.2.0     yaml_2.3.10      
[21] lifecycle_1.0.4   compiler_4.4.1    dplyr_1.1.4       pkgconfig_2.0.3  
[25] farver_2.1.2      digest_0.6.36     R6_2.5.1          tidyselect_1.2.1 
[29] utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3    withr_3.0.0      
[33] tools_4.4.1       gtable_0.3.5     </pre>
::: :::