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samplesizeCMH R Documentation
samplesizeCMH: Power and Sample Size Calculation for the
Cochran-Mantel-Haenszel Test
Description
This package provides functions relating to power and sample size calculation
for the CMH test. There are also several helper functions for interconverting
probability, odds, relative risk, and odds ratio values.
Details
The Cochran-Mantel-Haenszel test (CMH) is an inferential test for
the association between two binary variables, while controlling for a third
confounding nominal variable. Two variables of interest, X and
Y, are compared at each level of the confounder variable Z and
the results are combined, creating a common odds ratio. Essentially, the CMH
test examines the weighted association of X and Y. The
CMH test is a common technique in the field of biostatistics, where it is
often used for case-control studies.
Sample Size Calculation
Given a target power which the researcher would like to achieve, a
calculation can be performed in order to estimate the appropriate number of
subjects for a study. The power.cmh.test function calculates
the required number of subjects per group to achieve a specified power for a
Cochran-Mantel-Haenszel test.
Power Calculation
Researchers interested in estimating the probability of detecting a true
positive result from an inferential test must perform a power calculation
using a known sample size, effect size, significance level, et cetera.
The power.cmh.test function can compute the power of a CMH test,
given parameters from the experiment.
samplesizeCMH documentation built on May 29, 2024, 5:34 a.m.
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| samplesizeCMH | R Documentation |
samplesizeCMH: Power and Sample Size Calculation for the Cochran-Mantel-Haenszel Test
Description
This package provides functions relating to power and sample size calculation for the CMH test. There are also several helper functions for interconverting probability, odds, relative risk, and odds ratio values.
Details
The Cochran-Mantel-Haenszel test (CMH) is an inferential test for the association between two binary variables, while controlling for a third confounding nominal variable. Two variables of interest, X and Y, are compared at each level of the confounder variable Z and the results are combined, creating a common odds ratio. Essentially, the CMH test examines the weighted association of X and Y. The CMH test is a common technique in the field of biostatistics, where it is often used for case-control studies.
Sample Size Calculation
Given a target power which the researcher would like to achieve, a
calculation can be performed in order to estimate the appropriate number of
subjects for a study. The power.cmh.test function calculates
the required number of subjects per group to achieve a specified power for a
Cochran-Mantel-Haenszel test.
Power Calculation
Researchers interested in estimating the probability of detecting a true
positive result from an inferential test must perform a power calculation
using a known sample size, effect size, significance level, et cetera.
The power.cmh.test function can compute the power of a CMH test,
given parameters from the experiment.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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