# Difference between revisions of "Power Calculations in Stata"

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== Guidelines == | == 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 2=== | ||

===Subsection 3=== | ===Subsection 3=== |

## Revision as of 18:20, 7 February 2017

**NOTE: this article is only a template. Please add content!**

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## Read First

- include here key points you want to make sure all readers understand

## 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)

- Covariates (individual and/or cluster level) reduce the residual variance of the outcome variable, leading to lower required sample sizes

- Number of follow-up surveys

- Autocorrelation of outcome between FUP rounds

### Subsection 2

### Subsection 3

## Back to Parent

This article is part of the topic Sampling & Power Calculations

## Additional Resources

- list here other articles related to this topic, with a brief description and link