Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/powerLogisticsReg.R
Calculating sample size for simple logistic regression with binary predictor.
1 2 3 4 5 | SSizeLogisticBin(p1,
p2,
B,
alpha = 0.05,
power = 0.8)
|
p1 |
pr(diseased | X = 0), i.e. the event rate at X = 0 in logistic regression logit(p) = a + b X, where X is the binary predictor. |
p2 |
pr(diseased | X = 1), the event rate at X = 1 in logistic regression logit(p) = a + b X, where X is the binary predictor. |
B |
pr(X = 1), i.e. proportion of the sample with X = 1 |
alpha |
Type I error rate. |
power |
power for testing if the odds ratio is equal to one. |
The logistic regression mode is
\log(p / (1 - p)) = β_0 + β_1 X
where p = prob(Y = 1), X is the binary predictor, p_1 = pr(diseased | X = 0), p_2 = pr(diseased| X = 1), B = pr(X = 1), and p = (1 - B) p_1 + B p_2. The sample size formula we used for testing if β_1 = 0, is Formula (2) in Hsieh et al. (1998):
n=(Z_{1-α/2}[p(1-p)/B]^{1/2} + Z_{power}[p_1(1-p_1)+p_2(1-p_2)(1-B)/B]^{1/2})^2/[ (p_1-p_2)^2 (1-B) ]
where n is the required total sample size and Z_u is the u-th percentile of the standard normal distribution.
total sample size required.
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 stwxq@channing.harvard.edu
Hsieh, FY, Bloch, DA, and Larsen, MD. A SIMPLE METHOD OF SAMPLE SIZE CALCULATION FOR LINEAR AND LOGISTIC REGRESSION. Statistics in Medicine. 1998; 17:1623-1634.
1 2 3 4 | ## Example in Table I Design (Balanced design with high event rates)
## of Hsieh et al. (1998 )
## the sample size is 1281
SSizeLogisticBin(p1 = 0.4, p2 = 0.5, B = 0.5, alpha = 0.05, power = 0.95)
|
[1] 1281
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