R/convert_between_riskchange.R
oddsratio_to_riskratio.RdConvert Between Odds Ratios, Risk Ratios and Other Metrics of Change in Probabilities
oddsratio_to_riskratio(OR, p0, log = FALSE, verbose = TRUE, ...)
oddsratio_to_arr(OR, p0, log = FALSE, verbose = TRUE, ...)
oddsratio_to_nnt(OR, p0, log = FALSE, verbose = TRUE, ...)
logoddsratio_to_riskratio(logOR, p0, log = TRUE, verbose = TRUE, ...)
logoddsratio_to_arr(logOR, p0, log = TRUE, verbose = TRUE, ...)
logoddsratio_to_nnt(logOR, p0, log = TRUE, verbose = TRUE, ...)
riskratio_to_oddsratio(RR, p0, log = FALSE, verbose = TRUE, ...)
riskratio_to_arr(RR, p0, verbose = TRUE, ...)
riskratio_to_logoddsratio(RR, p0, log = TRUE, verbose = TRUE, ...)
riskratio_to_nnt(RR, p0, verbose = TRUE, ...)
arr_to_riskratio(ARR, p0, verbose = TRUE, ...)
arr_to_oddsratio(ARR, p0, log = FALSE, verbose = TRUE, ...)
arr_to_logoddsratio(ARR, p0, log = TRUE, verbose = TRUE, ...)
arr_to_nnt(ARR, ...)
nnt_to_oddsratio(NNT, p0, log = FALSE, verbose = TRUE, ...)
nnt_to_logoddsratio(NNT, p0, log = TRUE, verbose = TRUE, ...)
nnt_to_riskratio(NNT, p0, verbose = TRUE, ...)
nnt_to_arr(NNT, ...)Odds-ratio of odds(p1)/odds(p0), log-Odds-ratio
of log(odds(p1)/odds(p0)), Risk ratio of p1/p0, Absolute Risk Reduction
of p1 - p0, or Number-needed-to-treat of 1/(p1 - p0). OR and logOR
can also be a logistic regression model.
Baseline risk
If:
TRUE:
In oddsratio_to_*(), OR input is treated as log(OR).
In *_to_oddsratio(), returned value is log(OR).
FALSE:
In logoddsratio_to_*(), logOR input is treated as OR.
In *_to_logoddsratio(), returned value is OR.
Toggle warnings and messages on or off.
Arguments passed to and from other methods.
Converted index, or if OR/logOR is a logistic regression model, a
parameter table with the converted indices.
Grant, R. L. (2014). Converting an odds ratio to a range of plausible relative risks for better communication of research findings. Bmj, 348, f7450.
oddsratio(), riskratio(), arr(), and nnt().
Other convert between effect sizes:
d_to_r(),
diff_to_cles,
eta2_to_f2(),
odds_to_probs(),
w_to_fei()
p0 <- 0.4
p1 <- 0.7
(OR <- probs_to_odds(p1) / probs_to_odds(p0))
#> [1] 3.5
(RR <- p1 / p0)
#> [1] 1.75
(ARR <- p1 - p0)
#> [1] 0.3
(NNT <- arr_to_nnt(ARR))
#> [1] 3.333333
riskratio_to_oddsratio(RR, p0 = p0)
#> [1] 3.5
oddsratio_to_riskratio(OR, p0 = p0)
#> [1] 1.75
riskratio_to_arr(RR, p0 = p0)
#> [1] 0.3
arr_to_oddsratio(nnt_to_arr(NNT), p0 = p0)
#> [1] 3.5
m <- glm(am ~ factor(cyl),
data = mtcars,
family = binomial()
)
oddsratio_to_riskratio(m, verbose = FALSE) # RR is relative to the intercept if p0 not provided
#> Parameter | Risk Ratio | 95% CI
#> ---------------------------------------
#> (Intercept) | 0.73 |
#> cyl [6] | 0.59 | [0.11, 1.16]
#> cyl [8] | 0.20 | [0.02, 0.70]
#>
#> Uncertainty intervals (profile-likelihood) and p-values (two-tailed)
#> computed using a Wald z-distribution approximation.