precisely (version 0.1.2)

n_risk_difference: Estimate sample size based on precision of a measure

Description

These functions calculate the sample size needed to estimate a measure with a certain precision. For ratio measures, like the risk ratio, rate ratio, and odds ratio, this is the ratio of the upper to lower limit of the confidence interval. For difference measures, like the risk difference or rate difference, this is the absolute width of the confidence interval.

Usage

n_risk_difference(precision, exposed, unexposed, group_ratio, ci = 0.95)
n_risk_ratio(precision, exposed, unexposed, group_ratio, ci = 0.95)
n_rate_difference(precision, exposed, unexposed, group_ratio, ci = 0.95)
n_rate_ratio(precision, exposed, unexposed, group_ratio, ci = 0.95)
n_odds_ratio(
  precision,
  exposed_cases,
  exposed_controls,
  group_ratio,
  ci = 0.95
)

Arguments

precision

For differences, the width of the CI. For ratios, the ratio of the upper to lower CI.

exposed

The risk or rate among the exposed cohort.

unexposed

The risk or rate among the unexposed cohort.

group_ratio

In cohort studies, the ratio of the unexposed to the exposed. In case-control studies, the ratio of the controls to the cases.

ci

The confidence interval as a probability or percent. Default is .95.

exposed_cases

The proportion of exposed cases.

exposed_controls

The proportion of exposed controls.

Value

a tibble with sample size, effect measure, and precision

References

Rothman, K.J. and Greenland, S. 2018. Planning Study Size Based on Precision Rather Than Power. 29(5):599-603.

Examples

# NOT RUN {
# From Rothman and Greenland 2018

n_risk_difference(
  precision = .08,
  exposed = .4,
  unexposed = .3,
  group_ratio = 3,
  ci = .90
)

n_risk_ratio(
  precision = 2,
  exposed = .4,
  unexposed = .3,
  group_ratio = 3
)

# }