es_from_rr_ci function

Convert a risk ratio value and 95% confidence interval to various effect size measures

es_from_rr_ci(
  rr,
  rr_ci_lo,
  rr_ci_up,
  logrr,
  logrr_ci_lo,
  logrr_ci_up,
  baseline_risk,
  n_exp,
  n_nexp,
  n_cases,
  n_controls,
  rr_to_or = "metaumbrella",
  smd_to_cor = "viechtbauer",
  max_asymmetry = 10,
  reverse_rr
)

Arguments

• rr: risk ratio value
• rr_ci_lo: lower bound of the 95% CI around the risk ratio value
• rr_ci_up: upper bound of the 95% CI around the risk ratio value
• logrr: log risk ratio value
• logrr_ci_lo: lower bound of the 95% CI around the log risk ratio value
• logrr_ci_up: upper bound of the 95% CI around the log risk ratio value
• baseline_risk: proportion of cases in the non-exposed group (only required for the rr_to_or = "grant_CI" and rr_to_or = "grant_2x2" arguments).
• n_exp: number of participants in the exposed group (only required for the rr_to_or = "grant_CI", rr_to_or = "grant_2x2" arguments).
• n_nexp: number of participants in the non-exposed group (only required for the rr_to_or = "grant_CI", rr_to_or = "grant_2x2" arguments).
• n_cases: number of cases/events
• n_controls: number of controls/no-event
• rr_to_or: formula used to convert the rr value into an odds ratio (see details).
• smd_to_cor: formula used to convert the SMD value (converted from RR) into a coefficient correlation (see es_from_cohen_d).
• max_asymmetry: A percentage indicating the tolerance before detecting asymmetry in the 95% CI bounds.
• reverse_rr: a logical value indicating whether the direction of the generated effect sizes should be flipped.

Returns

This function estimates and converts between several effect size measures.

natural effect size measure	RR
converted effect size measure	OR + NNT
required input data	See 'Section 3. Risk Ratio'
	https://metaconvert.org/input.html

Details

This function uses the 95% CI of the (log) risk ratio to obtain the standard error (Section 6.5.2.2 in the Cochrane Handbook).

$$logrr\_se = \frac{\log{rr\_ci\_up} - \log{rr\_ci\_lo}}{2 * qnorm(.975)}$$

Then, calculations of the es_from_rr_se() are applied.

Examples

es_from_rr_ci(
  rr = 1, rr_ci_lo = 0.5, rr_ci_up = 2,
  n_cases = 42, n_controls = 38, baseline_risk = 0.08
)

References

Higgins JPT, Li T, Deeks JJ (editors). Chapter 6: Choosing effect size measures and computing estimates of effect. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Cochrane, 2022. Available from www.training.cochrane.org/handbook.