Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/powerLongitudinal.R

Sample size calculation for testing if mean changes for 2 groups are the same or not for longitudinal study with 2 time point.

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`es` |
effect size of the difference of mean change. |

`rho` |
correlation coefficient between baseline and follow-up values within a treatment group. |

`alpha` |
Type I error rate. |

`power` |
power for testing for difference of mean changes. |

The sample size formula is based on Equation 8.30 on page 335 of Rosner (2006).

*
n=\frac{2σ_d^2 (Z_{1-α/2} + Z_{power})^2}{δ^2}
*

where *σ_d = σ_1^2+σ_2^2-2ρσ_1σ_2*, *δ=|μ_1 - μ_2|*,
*μ_1* is the mean change over time *t* in group 1,
*μ_2* is the mean change over time *t* in group 2,
*σ_1^2* is the variance of baseline values within a treatment group,
*σ_2^2* is the variance of follow-up values within a treatment group,
*ρ* is the correlation coefficient between baseline and follow-up values within a treatment group,
and *Z_u* is the u-th percentile of the standard normal distribution.

We wish to test *μ_1 = μ_2*.

When *σ_1=σ_2=σ*, then formula reduces to

*
n=\frac{4(1-ρ)(Z_{1-α/2}+Z_{β})^2}{d^2}
*

where *d=δ/σ*.

required sample size per group

The test is a two-sided test. For one-sided tests, please double the
significance level. For example, you can set `alpha=0.10`

to obtain one-sided test at 5% significance level.

Weiliang Qiu [email protected]

Rosner, B.
*Fundamentals of Biostatistics*. Sixth edition. Thomson Brooks/Cole. 2006.

`ssLongFull`

, `powerLong`

,
`powerLongFull`

.

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