concurve (version 2.1.0)

curve_rev: Reverse engineer consonance and surprisal functions from confidence limits and point estimates

Description

Using the confidence limits and point estimates from a dataset, one can use these estimates to compute thousands of consonance intervals and graph the intervals to form a consonance and surprisal function.

Usage

curve_rev(point, LL, UL, measure = "default", steps = 10000)

Arguments

point

The point estimate from an analysis. Ex: 1.20

LL

The lower confidence limit from an analysis Ex: 1.0

UL

The upper confidence limit from an analysis Ex: 1.4

measure

The type of data being used. If they involve mean differences, then the "default" option should be used, which is also the default setting. If the data are ratios, then the "ratio" option should be used.

steps

Indicates how many consonance intervals are to be calculated at various levels. For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe.

References

Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

Examples

# NOT RUN {
# From a real published study. Point estimate of the result was hazard ratio of 1.61 and
# lower bound of the interval is 0.997 while upper bound of the interval is 2.59.

df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio")

tibble::tibble(df)

# }