sample size calculation

• Part I:
  understand the reasoning
  • introduce building blocks
  • highlight how the relate
  • focus on t-test only
  • a few exercises in GPower

• Part II:
  explore more complex situations
  • go beyond the t-test
  • simple but common
  • many exercises in GPower
  • not one formula for all

• Part of the design of a study
  • before data collection
  • requires understanding:
    • parameters: effect size of interest
    • data: future data properties
    • model: relation outcome and its conditions under which observed
  • decision based on (highly) incomplete information, thus based on (strong) assumptions

• Not always possible nor meaningful !
  • confirmatory studies easier than exploratory
  • experiments (control) easier than observational
  • not obvious for complex models
    → simulation
  • not obvious for predictive models
    → no standard error

• If not possible in a meaningful way
  use alternative justification

  • common practice
  • feasibility
  • …
     

• Avoid retrospective power analyses
  → OK for future study only

  Hoenig, J., & Heisey, D. (2001). The Abuse of Power:
  The Pervasive Fallacy of Power Calculations for Data Analysis. The American Statistician, 55, 19–24.

• Because less strong,
  put more weight on non-statistical justification
  • low cost
  • importance
  • …

• Does my radiotherapy work ?
  • aim: show my radiotherapy reduces tumor size
  • method: compare treatment and control group
    • tumor induced in N mice
    • random assignment mice
  • data: tumor sizes after 20 days
  • analysis: unpaired t-test

• Sample size question
  • how many mice are required
  • to show treatment reduces tumor size more
  • assuming effect size:
    • my radiotherapy works if 25% more reduction treatment group
    • 20% ~ 4mm (control) versus 5mm (treatment)
  • with 80% probability (+ type I error probability .05)

• Apriori specifications
  • intend to perform a statistical test
  • comparing 2 equally sized groups
  • to detect difference of at least 2
  • assuming an uncertainty of 4 SD on each average
  • which results in an effect size of .5
  • evaluated on a Student t-distribution
  • allowing for a type I error prob. of .05 \((\alpha)\)
  • allowing for a type II error prob. of .2 \((\beta)\)
• Sample size
  conditional on specifications being true

Difference detected approximately 80% of the times.

• Specifications for this particular case:
  • sample size (n → ?)
  • difference ( \(\Delta\) =signal → 2)
  • uncertainty ( \(\sigma\) =noise → 4)
  • type I errors ( \(\alpha\) → .05, so \(Z_{ \alpha /2}\) → -1.96)
  • type II errors ( \(\beta\) → .2, so \(Z_ \beta\) → -0.84)
• Sample size = 2 groups x 63 observations = 126

\(n = \frac{(Z_{\alpha/2}+Z_\beta)^2 * 2 * \sigma^2}{ \Delta^2} = \frac{(-1.96-0.84)^2 * 2 * 4^2}{2^2} = 62.79\)

• Formula’s are test and statistic specific but logic remains same
• This and other formula’s implemented in various tools, our focus: GPower

• Use it
  • implements wide variety of tests
  • free @ http://www.gpower.hhu.de/
  • popular and well established
  • implements various visualizations
  • documented -fairly- well
• Maybe not use it
  • not all tests are included, the simpler
  • not without flaws
  • other tools exist (some paying)

• Test family - statistical tests [in window]
  • Exact Tests (8)
  • \(t\)-tests (11) → reference
  • \(z\)-tests (2)
  • \(\chi^2\)-tests (7)
  • \(F\)-tests (16)
• Focus on the density functions

• Tests [in menu]
  • correlation & regression (15)
  • means (19) → reference
  • proportions (8)
  • variances (2)
• Focus on the type of parameters

• ~ reference example input
  • t-test : difference two independent averages
  • apriori: calculate sample size
  • effect size = standardized difference (Cohen’s \(d\))
    • Determine =>
      • \(d\) = |difference| / SD_pooled
      • \(d\) = |0-2| / 4 = .5
  • \(\alpha\) = .05; two-tailed ( \(\alpha\) /2 → .025 & .975 )
  • \(power = 1-\beta\) = .8
  • allocation ratio N2/N1 = 1 (equally sized groups)

• ~ reference example output
  • sample size \((n)\) = 64 x 2 = (128)
  • degrees of freedom \((df)\) = 126 (128-2)
  • power ≥ .80 (1- \(\beta\)) = 0.8015
  • distributions: central + non-central
  • critical t = 1.979
    • decision boundary given \(\alpha\) and \(df\)
      qt(.975,126)
  • non centrality parameter ( \(\delta\) ) = 2.8284
    • shift Ha (true) away from Ho (null)
      2/(4*sqrt(2))*sqrt(64)

• Summary for future reference or communication
  • central and non-central distributions (figure)
  • protocol of power analysis (text)

• File/Edit save or print file (copy-paste)
  

• non-centrality parameter \(\delta\) combines SIZES
  • assumed effect size ((standardized) signal)
  • conditional on sample size (information)
• \(\delta\) determines overlap Ho and Ha: bigger ncp less overlap
  • \(\delta\) as violation of Ho → shift (location/shape)
  • power = probability beyond \(\color{green}{cut off}\) at Ho evaluated on Ha
  • push with sample size
• Ha acts as \(\color{blue}{truth}\) assumed difference of e.g. .5 SD
  • Ha ~ t(ncp=2.828,df)
• Ho acts as \(\color{red}{benchmark}\): typically no difference, no relation
  • set \(\color{green}{cutoff}\) on Ho ~ t(ncp=0,df) using \(\alpha\)

• Sample sizes determine shape, not location
  • divide by n: sample size ~ standard error
    • peakedness of both distributions
    • often preferred didactically
  • non-centrality parameter: sample size ~ location
    • standardized distributions
    • often preferred in software / algorithms
• Formula’s same (DIY: two equations for critical value)

\(n = \frac{(Z_{\alpha/2}+Z_\beta)^2 * 2 * \sigma^2}{d^2}\)

• Inference (test) based on cut-off’s (density → AUC=1)

• Type I error: incorrectly reject Ho (false positive):

  • cut-off at Ho, error prob. \(\alpha\) controlled
  • one/two tailed → one/both sides informative ?

• Type II error: incorrectly fail to reject Ho (false negative):

  • cut-off at Ho, error prob. \(\beta\) obtained from Ha
  • Ha assumed known in a power analyses

• power = 1 - \(\beta\) = probability correct rejection (true positive)

• Inference versus truth
  • infer: effect exists vs. unsure
  • truth: effect exist vs. does not

• Create a plot
  • X-Y plot for range of values
  • assumes calculated analysis
    • ~ reference example
  • specify Y-axis / X-axis / curves and constant
    • beware of order !
• Plot sample size (y-axis)
  • by type I error \(\alpha\) (x-axis) → from .01 to .2 in steps of .01
  • for 4 values of power (curves) → with values .8 in steps of .05
  • assume effect size (constant) → .5 from reference example

• Notice Table option

• Where on the red curve (right) is
  the type II error equal to 4 * type I error ?
• When smaller effect size (e.g., .25), what changes ?

• Plot power instead of sample size

  • with 4 power curves
    with sample sizes 32 in step of 32

• What is relation type I and II error ?

• What would be difference between curves for \(\alpha\) = 0 ?

• Reasoning on error probabilities
  • \(\alpha\) & \(\beta\) inversely related
  • which error you want to avoid most ?
    • cheap aids test ? → avoid type II
    • heavy cancer treatment ? → avoid type I

• Popular choices for error probabilities
  • \(\alpha\) often in range .01 - .05 → 1/100 - 1/20
  • \(\beta\) often in range .2 to .1 → power = 80% to 90%

• Popular rules of thumb
  • 4 * \(\alpha\) ~ \(\beta\)
    • type I error is ~4 times worse !!
• Probability for both errors always exists

• Defined on the Ho, known
  • assumes only sampling variability

• Multiple testing
  • control \(\alpha\) over set of tests
    • each test \(\alpha\) error
  • inflates type I error \(\alpha\)
    • k tests with probability for an error: \(1-(1- \alpha)^k\)
  • correct → eg., Bonferroni ( \(\alpha/k\))

• Interim analysis
  • analyze and ‘conditionally’ proceed
  • type of multiple testing
  • plan in advance
  • adjustments of either \(p\) or \(\alpha\)
  • alpha spending, eg., O’Brien-Flemming bounds
  • NOT GPower
    our own simulation tool (Susanne Blotwijk)
  • determine boundaries with PASS, R (ldbounds), …

• Comparing the control group and two treatments

• Pairwise comparisons, typically not an omnibus

  • looked at as a set of t-tests, not as ANOVA
  • requires multiple testing correction, e.g., Bonferonni correction: divide \(\alpha\) by number of tests

• use reference example (C = 0, T1 = 2), and extend with group 3 with T2 = 4 (same sd)

  • what samples sizes are necessary for all three pairwise tests combined ?
  • what if biggest difference (C-T2) is ignored, because considered easiest to detect ?

• Estimate / guestimate of minimal magnitude of interest
   

• Typically standardized: signal to noise ratio

  • eg., effect size \(d\) = .5 means .5 standard deviations (pooled)
    

• Part of non-centrality (as is sample size) → pushing away Ha

• ~ Practical relevance

  • NOT statistical significance
    • p-value ~ effect size AND sample size

• 2 main families of effect sizes
  • d-family (differences) and r-family (associations)
  • transform one into other,
    eg., d = .5 → r = .243
    \(\hspace{20 mm}d = \frac{2r}{\sqrt{1-r^2}}\)
    \(\hspace{20 mm}r = \frac{d}{\sqrt{d^2+4}}\)
    \(\hspace{20 mm}d = ln(OR) * \frac{\sqrt{3}}{\pi}\)

• Cohen, J. (1992).
  A power primer. Psychological Bulletin, 112, 155–159.

• Cohen, J. (1988).
  Statistical power analysis for the behavioral sciences (2nd ed).

• Famous Cohen conventions

  • but beware, just rules of thumb

• Ellis, P. D. (2010).
  The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results.

• more than 70 different effect sizes… most of them related

• Effect sizes are test specific
  • t-test → group means and sd’s
  • one-way anova → variance explained & error
  • regression → sd’s and correlations
  • . . . .

• GPower helps with Determine
  • sliding window
  • one or more effect size specifications

• Total variance: 16.882
  
• Between group variance
  with mean y equal to 0 and 2: 1.008
  
• Within group variance
  (error or residual variance): 15.874
  
• F-test statistic, square root of ratio
  between on within group variance: 0.252
• \(\eta^2\) statistic, ratio between group on total variance 0.06
  
• Design variance
  with X = 0 and 1: 0.252

• Regression analysis results in R

Call:
lm(formula = y ~ factor(group), data = .dta)

Residuals:
     Min       1Q   Median       3Q      Max 
-10.6795  -2.6556   0.5043   2.6463   8.8380 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)   
(Intercept)    9.421e-16  5.000e-01   0.000  1.00000   
factor(group)2 2.000e+00  7.071e-01   2.828  0.00544 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4 on 126 degrees of freedom
Multiple R-squared:  0.0597,    Adjusted R-squared:  0.05224 
F-statistic:     8 on 1 and 126 DF,  p-value: 0.005444

• ANOVA or t-test results in R

             Df Sum Sq Mean Sq F value  Pr(>F)   
group         1    128     128       8 0.00544 **
Residuals   126   2016      16                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

• t-test results in R

[1] 0.00544392

        t 
-2.828427 

• For the reference example:
  • change mean values from 0 and 2 to 4 and 6, what changes ?
  • change sd values to 2 for each, what changes ?
    • effect size ?
    • total sample size ?
    • critical t ?
      
    • non-centrality ?
  • change sd values to 8 for each, what changes ?
    
  • change sd to 2 and 5.3, or 1 and 5.5,
    how does it compare to 4 and 4 ?

• For the reference example:
  • plot powercurve: power by effect size
  • compare 6 sample sizes: 34 in steps of 34
  • for a range of effect sizes in between .2 and 1.2
  • use \(\alpha\) equal to .05
  • how does power change when doubling the effect size ?

• powercurve → X-Y plot for range of values

• Choice of effect size matters → justify choice !!

• Choice of effect size depends on aim of the study

  • realistic (eg., previously observed effect) → replicate
  • important (eg., minimally relevant effect)
  • NOT significant → meaningless, dependent on sample size

• Choice of effect size dependent on statistical test of interest

  • for independent t-test → means and standard deviations
  • possible alternative: variance explained, eg., 1 versus 16+1

• Examples
  • with one-way ANOVA
    \(f\) = .25 instead of d = .5
  • with linear regression
    \(f^2\) = .0625 instead of d = .5
  • psychometric freeware

• Experts / patients
  • minimally clinically relevant effect
  • importance
  • use if possible
• Literature
  • earlier study / systematic review
  • realistic
  • beware of publication bias
• (Internal) Pilot
  • guestimate dispersion estimate
  • not to obtain effect size → small sample

• Guestimate uncertainty…
  • sd from assumed range
    • assume normal and divide by 6
  • sd for proportions at conservative .5
  • sd from control, assume treatment the same
  • ...
• Turn to Cohen
  • use if everything else fails
  • rules of thumb
    • eg., .2 - .5 - .8 for Cohen’s d

• not accounting for the important predictor

• accounting for it, sd (around average) reduced

• Use reference example
  • assume half the unexplained variance is accounted for by the predictor, what are the sample sizes ?
  • assume a non-inferiority margin of -2, and no difference, how big is the sample size ?
  • assume treatment to be 2 higher, compare the sample size for superiority (bigger than 0) and non-inferiority with margin of -2

• For the reference example:
  • compare for allocation ratios 1, .5, 2, 10, 50
  • repeat for effect size 1, and compare
• ? no idea why n1 \(\neq\) n2
  
• Plot with changed allocation ratio

• Building blocks:
  • sizes: sample ( \(n\) ) and effect ( \(\Delta\) )
  • errors: type I ( \(\alpha\) ), type II ~ power ( \(1-\beta\) )

• GPower → type of power analysis
  • Apriori: \(n\) ~ \(\alpha\), power, \(\Delta\)
  • Post Hoc: power ~ \(\alpha\), \(n\), \(\Delta\)
  • Compromise: power, \(\alpha\) ~ \(\beta\:/\:\alpha\), \(\Delta\), \(n\)
  • Criterion: \(\alpha\) ~ power, \(\Delta\), \(n\)
  • Sensitivity: \(\Delta\) ~ \(\alpha\), power, \(n\)

• each parameter conditional on others
• one outgoing arrow, three going in  

• For the reference example:
  • how big is the power for 128 observations (n=2x64, \(\alpha\)=.05 and \(\Delta\)=.5)
  • then, assume a power of .8 but with only half the sample size, how does the effect size \(\Delta\) change ?
  • then, set the ratio \(\beta\)/ \(\alpha\) to 4, what are the resulting \(\alpha\) and \(\beta\) ? and what is the critical value ?
  • then, set the effect size to .7 (same ratio), what are the resulting \(\alpha\) and \(\beta\) ? and what is the critical value ?

• in G*Power

m1=0
m2=2
s1=4
s2=4  
alpha=.025
N=128  
var=.5*s1^2+.5*s2^2  
d=abs(m1-m2)/sqrt(2*var)*sqrt(N/2)  
tc=tinv(1-alpha,N-1)  
power=1-nctcdf(tc,N-1,d)  

• in R

.n <- 64
.df <- 2*.n-2
.ncp <- 2 / (4 * sqrt(2)) * sqrt(.n)
.alpha <- .05
.power <- 1 -
    pt( 
        qt(1-.alpha/2,df=.df,ncp=0), 
        df=.df, ncp=.ncp
    )
round(.power,4)

[1] 0.8015

• 2 steps
  • qt → quantile on H0 (p=\(Z_{1-\alpha/2}\))
  • pt → probability on Ha < quantile

• So far, comparing two independent means  

• From now on, selected topics beyond independent t-test
  with small exercises

  • dependent instead of independent
  • proportions, dependent and independent
  • non-parametric instead of assuming normality
  • relations instead of groups (regression)
  • correlations
  • more than 2 groups (compare jointly, pairwise, focused)
  • more than 1 predictor
  • repeated measures

• Look into - GPower manual
  27 tests → effect size, non-centrality parameter and example !!

• When comparing 2 dependent groups (eg., before/after treatment) → account for correlations

• Correlation are typically obtained from pilot data, earlier research

• GPower: matched pairs (t-test / means, difference 2 dependent means)

• Use reference example,

  • assume a correlation of .5 and compare with reference example for effect size and n
  • how many observations are required if no correlation exists (think then try) ? effect size ?
  • what changes with correlation .875 (think: more or less n, higher or lower effect size) ?
  • what power would be obtained for the reference with sample size 2x64, but correlation .5 ?
  • get the sd of the difference and use it (\(\sqrt{\sigma_a^2 + \sigma_b^2 - 2 * \rho * \sigma_a * \sigma_b}\)) ?

• a noticeable correlation

• clear correlation

• Test difference two independent proportions → [0..1]

• Simplest version of a logistic regression on two groups

• GPower: Fisher Exact Test (exact / proportions, difference 2 independent proportions)

• Testing whether two proportions are the same

  • for odds ratio 3 and p2 = .50, what is p1 ? and for odds ratio 1/3 and p2 = .75 ?
  • what is the sample size to detect a difference for both situations ?
  • for odds ratio 3 and p2 = .75, determine p1 and sample size, how does it compare with before ?
  • compare sample size for a .15 difference, at p1=.5 ?

• GPower: Fisher Exact Test (exact / proportions, difference 2 independent proportions)

• Plot 5 power curves

  • odds ratio = 2, with p2 reference probability .6
  • proportions .5 to 1
  • 1 curve per sample sizes 328, 428, 528… (intervals of 100)
  • type I error .05

• Explain curve minimum, relation sample size ?

• Repeat for one-tailed, difference ?

• Test difference two dependent proportions → [0..1] categorical shift

  • for two categories, McNemar test: compare \(p_{12}\) with \(p_{21}\)
  • information from changes only → discordant pairs
  • effect size as odds ratio → ratio of discordance

• GPower: McNemar test (exact / proportions, difference 2 dependent proportions)

• Testing whether proportions of discordance are the same

  • assume odds ratio 2, .25 discordant, what is sample size
  • for discordant .5 and 1 what are \(p_12\) and \(p_21\) and sample sizes ?
  • for odds ratio .5 and 1 (prop discordant = .25), what are sample sizes ?
  • repeat for third alpha option first scenario, what happens ?

• When non-normally distributed residuals are expected, not possible to circumvent (eg., transformations)

• Only considers ranks or uses permutations → price is efficiency and flexibility

• Requires a parent distribution (alternative hypothesis), ‘min ARE’ should be default

• GPower: two groups → Wilcoxon-Mann-Whitney (t-test / means, diff. 2 indep. means)

• Use reference example

  • use a normal parent distribution, how much efficiency is lost ?
  • use ‘min ARE’ as parent distribution, how much efficiency is lost ?

• Differences between groups ~ relation with grouping (categorization)

• Example: d = .5 ~ r = .243 (note: slope \(\beta = {r*\sigma_y} / {\sigma_x}\))

  • total variance \(\sigma_y\) = residual variance + model variance (2 or 0) → var((2-1),(0-1),(2-1),(0-1),…)
  • design variance \(\sigma_x\) = variance -.5 and .5 for all observations → var((1-.5),(0-.5),(1-.5),(0-.5),…)

• GPower: regression coefficient (t-test / regression, one group size of slope)

  • what is the slope \(\beta\) and \(\sigma_y\) for reference values, d=.5 (hint:d~r), SD = 4 and \(\sigma_x\) = .5 (1/0)
  • what is the resulting sample size
  • what happens with the slope and sample size if predictor values are taken as 1/-1 instead of 0/1 ?
  • what is \(\sigma_y\) for a slope of 6, \(\sigma_x\) = .5, and SD = 4, would it increase the sample size ?

• A relation as ratio between and within group variance ~ explained variance R²

• Different but related effect sizes \(f^2\) = \({R^2/{(1-R^2)}}\)

  • partial \(R^2\) = variance explained by predictor / total variance
  • \(f^2\) = variance explained by predictor / residual variance (Note: 2\(f\) = \(d\) for 2 groups)

• GPower: regression coefficient (t-test / regression, fixed model single regression coef)

• Use reference example

  • remember the variances, add them to calculate the effect size
  • calculate sample size ?
  • what if also other predictors in the model ?
  • what if 3 predictors extra reduce residual variance to 50% ?
    • hint: total variance remains constant at 17

• Multiple groups, at least two differ → not one effect size d

• F-test statistic & effect size f, ratio of variances \(\sigma_{between}^2 / \sigma_{within}^2\)

• GPower: one-way Anova (F-test / Means, ANOVA - fixed effects, omnibus, one way)

• use reference example

  • what is the sample size, for a difference of 2, each 64 observations ?
  • why are the ncp and critical value different ?
  • how does size matter ? (play with it)

• include a third group (group 1 = 0, group 2 = 2)

  • for third group with mean 0, 2 or 4 (figure), what are the sample sizes ?
  • repeat for between variance 2.67 (balanced: 0, 2, 4) and within variance 16 ?

• Contrasts are linear combinations → planned comparison
  • eg., T1-C: \(1 * T1 -1 * C \neq 0\)
  • eg., (T1+T2)/2-C: \(.5 * (1 * T1 + 1 * T2) -1 * C \neq 0\)
• Effect sizes for planned comparisons must be calculated
  • contrast specific between variance \(\sigma_{contrast}^2\)
  • \(f\) ~ variance ratio between / within
  • Note: \(f\) = 2\(d\)
• Obtain effect sizes for contrasts (assume equally sized for convenience)
  • T1-C, T2-C, (T1+T2)/2-C
  • each contrast requires 1 degree of freedom

• Parameters
  • group means \(\mu_i\)
    
  • pre-specified coefficients \(c_i\)
    
  • sample sizes \(n_i\)
    
  • total sample size \(N\)
    
  • k levels

\(\sigma_{contrast} = \frac{|\sum{\mu_i * c_i}|}{\sqrt{N \sum_i^k c_i^2 / n_i}}\)  

\(f = \sqrt{\frac{\sigma_{contrast}^2}{\sigma_{error}^2}}\)

• GPower: one-way ANOVA (F-test / Means, ANOVA-fixed effects,special,main,interaction)

• For the reference example extended, with contrasts \(f_{T1-C}\)=.25, \(f_{T2-C}\)=.50 and \(f_{(T2+T1)/2-C}\)=.3535

  • what are the samples sizes for either contrast 1 or contrast 2 ?
  • what are the samples sizes for both contrast 1 and contrast 2 combined ?
  • if taking that sample size, what will be the power for T1-T2 ?
  • what are the samples size for contrast 3 ?

• When the factor is time: repeated measures

  • relates to dependent t-test for multiple measurements (>2)

• Beware: effect sizes obtained from literature may/may not include correlation

  • Options: as in GPower 3, or SPSS, …

• GPower: repeated measures (F-test / Means, repeated measures within factors)

• For reference example with effect size f = .25 (1/16 explained versus unexplained)

  • mimic independent t-test
  • mimic dependent t-test, correlation .5 !
  • what does an increase in correlation imply, why ?
  • for 4, or 8 repeated measurements (cor=.5), what changes.
  • for 4 groups (4 measures, cor=.5), what changes ?

• Multiple main effects and possibly interaction effects
  • main effect:
    • difference B1-B2 over all conditions of treatment (C,T1,T2)
    • difference C-T1-T2 over all conditions of type (B1,B2)
  • interaction effect:
    • effect of treatment (C-T1-T2) different per level of type (B1 or B2) or vise verse
  • note: numerator degrees of freedom
    • main effect (nr-1) or 2 for treatment and 1 for type
    • interaction (nr1-1)*(nr2-1) or 2 (=2x1)

• note:
  get effect sizes for two way anova:
  in-house shiny app

• effect sizes
  • \(\eta^2\) = \(f^2 / (1+f^2)\)
  • note: \(f = d/2\)
    for two groups

• Get effect size: in-house shiny app

• Use reference example for treatment (C-T1-T2) and add type (B1-B2)

  • determine the effect size \(\eta^2\)
    for averages 0, 2, and if necessary use 4 or 6
    • treatment effect C-T1
    • treatment effect C-T1-T2 - no type effect B1-B2
    • no treatment effect C-T1-T2 - type effect B1-B2
    • treatment effect C-T1-T2 within B1, not B2 - with interaction
    • treatment effect C-T1-T2 - type effect B1-B2 without interaction

• GPower: multiway ANOVA (F-test / Means, ANOVA-fixed effects,special,main,interaction)

• Use reference example for treatment (C-T1-T2) and add type (B1-B2)
  what are the sample sizes

  • C-T1 with \(\eta^2\)=0.0588
  • C-T1-T2 (no B1-B2) with \(\eta^2\)=0.1429
  • B1-B2 (no C-T1-T2) with \(\eta^2\)=0.0588
  • interaction C-T1-T2 x no B1-B2 with \(\eta^2\)=0.04
  • additive C-T1-T2 + B1-B2
    • with \(\eta^2\)=0.1429 for C-T1-T2
    • with \(\eta^2\)=0.0588 for B1-B2

• When repeated measures are obtained for different groups

• GPower: repeated measures (F-test / Means, repeated measures between factors)

• For reference example

  • relate to independent t-test, 2 uncorrelated measurements
  • mimic independent t-test, 2 almost perfectly correlated measurements
  • with a correlation .5, what changes ?
  • what does increase in correlation imply, why ?
  • for 3 groups extended reference, cor=.5, what changes ?
  • for 4, or 8 repeated measurements (cor=.5), what changes.

• When differences between groups depend on time

• Get effect size: in-house shiny app

• Use reference example for control-treatment (C-T1), and 2 or 4 time points

  • determine the effect size interaction \(\eta^2\) with r=0 and r=.5
    • treatment effect C-T1, both C and T1 increase with 2
    • treatment effect C-T1, only T1 increase with 2
    • if previous situation repeated twice, T1=2,4,2,4
    • if previous situation repeated twice, but reversed, T1=2,4,4,2

• Options: different effect sizes are possible

  • is correlation already part of effect size ?
  • often it is when extracted from literature

• GPower: repeated measures (F-test / Means, repeated measures within-between factors)

• Use effect sizes previous exercise, part 2 and 3

  • determine the sample size, assuming a correlation of .5
    • for effect size assuming no correlation is included, include .5 correlation
    • for effect size assuming correlation is included, include correlation of 0

• Test difference of two independent correlations → [-1..1]

• Use Fisher Z transformations to normalize

  • z = .5 * log( \(\frac{1+r}{1-r}\) )
  • q = z1-z2

• Correlations easier to differentiate as they are more different from 0

• GPower: z-tests / correlation & regressions: 2 indep. Pearson r’s

• Testing whether two correlations are the same

  • with correlation coefficients .7844 and .5, what are the effect & sample sizes ?
  • with the same difference, but stronger correlations, eg., .9844 and .7, what changes ?
  • with the same difference, but weaker correlations, eg., .1 and .3844, what changes ?

• GPower not for always sufficient

• Tests too difficult to specify in GPower

  • statistics / parametervalues difficult to guestimate
  • manual not always sufficient

• Tests not included in GPower

  • eg., survival analysis
  • many tools online, most dedicated to a particular model

• Tests without formula

  • simulation may be the only tool

• simulation in theory is always possible
  • iterate many times:
    • generate: simulated outcome
      introduce randomness ~ standard deviation
    • analyze: estimate parameters + test
  • count proportion of rejections (~power)
  • determine confidence bounds (~accuracy)
  • select sample size
    • with appropriate proportion (test)
    • with appropriate interval (estimation)

• simulation in practice
  • reference example: 0-2 (4), 64x2
  • replicate 10000 times
    • generate:
      dta$y <- dta$y+rnorm(length(dta$y),0,4)
    • analyze:
      res <- t.test(data=dta,y~X)
  • count proportion rejection:
    mean(sims['p.val',] < .05)

gr <- rep(c('T','C'),64)
y <- ifelse(gr=='C',0,2)
dta <- data.frame(y=y,X=gr)
cutoff <- qt(.025,nrow(dta))
 
my_sim_function <- function(){
    dta$y <- dta$y+rnorm(length(dta$y),0,4)     # generate (with sd=4)
    res <- t.test(data=dta,y~X)                 # analyze
    c(res$estimate %*% c(-1,1),res$statistic,res$p.value)
}
sims <- replicate(10000,my_sim_function())      # many iterations
dimnames(sims)[[1]] <- c('diff','t.stat','p.val')

mean(sims['p.val',] < .05)  # p-values  0.8029
mean(sims['t.stat',] < cutoff)  # t-statistics 0.8029
mean(sims['diff',] > sd(sims['diff',])*cutoff*(-1)) # differences 0.8024

• Complex statistical models
  • simulate BUT it requires programming and a thorough understanding of the model
  • alternative: focus on essential elements → simplify the aim
• Sample size calculations (design) for simpler research aim
  • not necessarily equivalent to final statistical testing / estimation
  • requires justification to convince yourself and/or reviewers
    • successful already if simple aim is satisfied
    • ignored part is not too costly

• Example:
  • statistics:
    group difference evolution 4 repeated measurements → mixed model
  • focus:
    difference treatment and control last time point is essential → t-test
  • argument: first 3 measurements low cost, interesting to see change

• Sample size calculation is a design issue, not a statistical one

• It typically focuses on ensuring sufficient data to result in sufficiently strong statistical inference

• Sample size depends on effect size, type I & II errors, and the statistical test of interest

• Effect sizes express the amount of signal compared to the background noise

• GPower deals with not too complex models

  • more complex complex models imply more complex specification
  • simplify using a focus, if justifiable → then GPower can get you a long way