Difference between revisions of "Power Calculations in Stata"

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** Significance level (α) = 0.05
 
** Significance level (α) = 0.05
 
** Power = 0.80 (i.e. probability of type II error (β) = 0.20
 
** Power = 0.80 (i.e. probability of type II error (β) = 0.20
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Ideally, you will also have:
 
Ideally, you will also have:
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* Autocorrelation of outcome between FUP rounds
 
* Autocorrelation of outcome between FUP rounds
 
  
 
===Subsection 2===
 
===Subsection 2===

Revision as of 18:20, 7 February 2017

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Guidelines

Data needed to perform power calculations

You must have:

  • Mean and variance for outcome variable for your population
    • Typically can assume mean and SD are the same for treatment and control groups if randomized
  • Sample size (assuming you are calculating MDES (δ))
    • If individual randomization, number of people/units (n)
    • If clustered, number of clusters (k), number of units per cluster (m), intracluster correlation (ICC, ρ) and ideally, variation of cluster size
  • The following standard conventions
    • Significance level (α) = 0.05
    • Power = 0.80 (i.e. probability of type II error (β) = 0.20


Ideally, you will also have:

  • Baseline correlation of outcome with covariates
    • Covariates (individual and/or cluster level) reduce the residual variance of the outcome variable, leading to lower required sample sizes
      • Reducing individual level residual variance is akin to increasing # obs per cluster (bigger effect if ICC low)
      • Reducing cluster level residual variance is akin to increasing # of clusters (bigger effect if ICC and m high)
    • If you have baseline data, this is easy to obtain
      • Including baseline autocorrelation will improve power (keep only time invariant portion of variance)
  • Number of follow-up surveys
  • Autocorrelation of outcome between FUP rounds

Subsection 2

Subsection 3

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This article is part of the topic Sampling & Power Calculations


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