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Test rate difference by comparing it to a pre-specified value using the Farrington-Manning test

Usage

fitFarringtonManning(endpoint, placebo, data, alternative, delta = 0, ...)

Arguments

endpoint

Character. Name of the endpoint in data.

placebo

Character. String indicating the placebo in data$arm.

data

Data frame. Usually it is a locked data set.

alternative

a character string specifying the alternative hypothesis, must be one of "greater" or "less". No default value. "greater" means superiority of treatment over placebo is established by rate difference greater than `delta`.

delta

the rate difference between a treatment arm and placebo under the null.

...

Subset conditions compatible with dplyr::filter. glm will be fitted on this subset only. This argument can be useful to create a subset of data for analysis when a trial consists of more than two arms. By default, it is not specified, all data will be used to fit the model. More than one condition can be specified in ..., e.g., fitFarringtonManning('remission', 'pbo', data, delta, arm %in% c('pbo', 'low dose'), cfb > 0.5), which is equivalent to: fitFarringtonManning('remission', 'pbo', data, delta, arm %in% c('pbo', 'low dose') & cfb > 0.5). Note that if more than one treatment arm are present in the data after applying filter in ..., models are fitted for placebo verse each of the treatment arms.

Value

a data frame with three columns:

p

one-sided p-value for log odds ratio (treated vs placebo).

info

sample size in the subset with NA being removed.

z

the z statistics of log odds ratio (treated vs placebo).

References

Farrington, Conor P., and Godfrey Manning. "Test statistics and sample size formulae for comparative binomial trials with null hypothesis of non-zero risk difference or non-unity relative risk." Statistics in medicine 9.12 (1990): 1447-1454.