Estimate sample size based on precision of a measure

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.

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

# From Rothman and Greenland 2018

n_risk_difference(
  precision = .08,
  exposed = .4,
  unexposed = .3,
  group_ratio = 3,
  ci = .90
)
#> # A tibble: 1 × 9
#>   n_exposed n_unexposed n_total risk_difference precision exposed unexposed
#>       <dbl>       <dbl>   <dbl>           <dbl>     <dbl>   <dbl>     <dbl>
#> 1      524.       1573.   2097.             0.1      0.08     0.4       0.3
#> # … with 2 more variables: group_ratio <dbl>, ci <dbl>

n_risk_ratio(
  precision = 2,
  exposed = .4,
  unexposed = .3,
  group_ratio = 3
)
#> # A tibble: 1 × 9
#>   n_exposed n_unexposed n_total risk_ratio precision exposed unexposed
#>       <dbl>       <dbl>   <dbl>      <dbl>     <dbl>   <dbl>     <dbl>
#> 1      72.8        219.    291.       1.33         2     0.4       0.3
#> # … with 2 more variables: group_ratio <dbl>, ci <dbl>