Sample size, power and minimum detectable difference when comparing continuous outcomes
Sample size, power and minimum detectable difference when comparing continuous outcomes.
epi.sscompc(treat, control, n, sigma, power, r = 1, design = 1, sided.test = 2, nfractional = FALSE, conf.level = 0.95)
treat |
the expected value for the treatment group (see below). |
control |
the expected value for the control group (see below). |
n |
scalar, defining the total number of subjects in the study (i.e. the number in the treatment and control group). |
sigma |
the expected standard deviation of the variable of interest for both treatment and control groups. |
power |
scalar, the required study power. |
r |
scalar, the number in the treatment group divided by the number in the control group. |
design |
scalar, the estimated design effect. |
sided.test |
use a one- or two-sided test? Use a two-sided test if you wish to evaluate whether or not the outcome in the exposed (treatment) group is greater than or less than the outcome in the unexposed (control) group. Use a one-sided test to evaluate whether or not the outcome in the exposed (treatment) group is greater than the outcome in the unexposed (control) group. |
nfractional |
logical, return fractional sample size. |
conf.level |
scalar, defining the level of confidence in the computed result. |
The methodology in this function follows the approach described in Chapter 8 of Woodward (2005), pp. 381 - 426.
A list containing the following:
n.total |
the total number of subjects required for the specified level of confidence and power, respecting the requirement for |
n.treat |
the total number of subjects in the treatment group for the specified level of confidence and power, respecting the requirement for |
n.control |
the total number of subjects in the control group for the specified level of confidence and power, respecting the requirement for |
power |
the power of the study given the number of study subjects, the expected effect size and level of confidence. |
delta |
the minimum detectable difference given the specified level of confidence and power. |
The power of a study is its ability to demonstrate the presence of an association, given that an association actually exists.
A detailed description of sample size calculations for case-control studies (with numerous worked examples, many of them reproduced below) is provided by Woodward (2005), pages 381 to 426.
See the documentation for epi.sscohortc for an example using the design facility implemented in this function.
Kelsey JL, Thompson WD, Evans AS (1986). Methods in Observational Epidemiology. Oxford University Press, London, pp. 254 - 284.
Woodward M (2005). Epidemiology Study Design and Data Analysis. Chapman & Hall/CRC, New York, pp. 381 - 426.
## EXAMPLE 1 (from Woodward 2005 p. 399): ## Supposed we wish to test, at the 5% level of significance, the hypothesis ## that cholesterol means in a population are equal in two study years against ## the one-sided alternative that the mean is higher in the second of the ## two years. Suppose that equal sized samples will be taken in each year, ## but that these will not necessarily be from the same individuals (i.e. the ## two samples are drawn independently). Our test is to have a power of 0.95 ## at detecting a difference of 0.5 mmol/L. The standard deviation of serum ## cholesterol in humans is assumed to be 1.4 mmol/L. epi.sscompc(treat = 5, control = 4.5, n = NA, sigma = 1.4, power = 0.95, r = 1, design = 1, sided.test = 1, nfractional = FALSE, conf.level = 0.95) ## To satisfy the study requirements 340 individuals need to be tested: 170 in ## the first year and 170 in the second year. ## EXAMPLE 2 (from Woodward 2005 pp. 399 - 400): ## Women taking oral contraceptives sometimes experience anaemia due to ## impaired iron absorption. A study is planned to compare the use of iron ## tablets against a course of placebos. Oral contraceptive users are ## randomly allocated to one of the two treatment groups and mean serum ## iron concentration compared after 6 months. Data from previous studies ## indicates that the standard deviation of the increase in iron ## concentration will be around 4 micrograms% over a 6-month period. ## The average increase in serum iron concentration without supplements is ## also thought to be 4 micrograms%. The investigators wish to be 90% sure ## of detecting when the supplement doubles the serum iron concentration using ## a two-sided 5% significance test. It is decided to allocate 4 times as many ## women to the treatment group so as to obtain a better idea of its effect. ## How many women should be enrolled in this study? epi.sscompc(treat = 8, control = 4, n = NA, sigma = 4, power = 0.90, r = 4, design = 1, sided.test = 2, nfractional = FALSE, conf.level = 0.95) ## The estimated sample size is 70. We allocate 70/5 = 14 women to the ## placebo group and four times as many (56) to the iron treatment group.
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