priskran: Pooled risk estimate using the random effect model...

In Package-Metaan-Rep/Metaan: Meta-Analysis of Published Epidemiological Studies

Description

DerSimonian and Laird-based Random effect model for standard meta-analysis of risk estimate (e.g relative risk (RR), odds ratio (OR) or hazard ratio (HR))

Usage

priskran(rr, u, l, form = c("Log", "nonLog"), conf.level = 0.95)

Arguments

rr	A numeric vector of the risk estimated from the individual studies
u	A numeric vector of the upper bound of the confidence interval of the risk reported from the individual studies.
l	A numeric vector of the lower bound of the confidence interval of the risk reported from the individual studies.
form	Logical, indicating the scale of the data. If Log, then the original data are in logarithme scale.
conf.level	Coverage for confidence interval

Value

Object of class "metaan.ra". A list that print the output from the priskran function. The following could be found from the list :

• rr_tot (Effect): The pooled effect from the individual studies' estimate (RR, OR, or HR)

• sd_tot_lnRR (SE-Log(Effect)): The standard error of the pooled effect (see reference Richardson et al 2020 for more details)

• l_tot (Lower CI): The lower confidence interval bound of the pooled effect (rr_tot)

• u_tot (Upper CI): The upper confidence interval bound of the pooled effect (rr_tot)

• Cochrane_stat (Cochran’s Q statistic): The value of the Cochrane's statistic of inter-study heterogeneity

• Degree_freedom (Degree of Freedom): The degree of freedom

• p_value (P-Value): The p-value of the statistic of Cochrane

• I_square (Higgins’ and Thompson’s I^2 (%)): I square value in percent (%) indicating the amount of the inter-study heterogeneity

References

DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Controlled clinical trials 7:177–188.

Examples

study <- c("Canada", "Northern USA", "Chicago", "Georgia","Puerto", "Comm",
"Madanapalle", "UK", "South Africa", "Haiti", "Madras")
Risk <- c(0.205, 0.411, 0.254, 1.562, 0.712, 0.983, 0.804, 0.237, 0.625,
0.198, 1.012)
lower_ci <- c(0.086, 0.134, 0.149, 0.374, 0.573, 0.582, 0.516, 0.179, 0.393,
0.078, 0.895)
upper_ci <- c(0.486, 1.257, 0.431, 6.528, 0.886, 1.659, 1.254,
0.312, 0.996, 0.499, 1.145)

donne <- data.frame(cbind(study, Risk, lower_ci, upper_ci))

donne$Risk <- as.numeric(as.character(donne$Risk))
donne$upper_ci <- as.numeric(as.character(donne$upper_ci))
donne$lower_ci <- as.numeric(as.character(donne$lower_ci))

# on the log form
donne$ln_risk <- log(donne$Risk)
donne$ln_lower_ci <- log(donne$lower_ci)
donne$ln_upper_ci <- log(donne$upper_ci)


priskran(rr=donne$Risk, u=donne$upper_ci, l=donne$lower_ci,
 form="nonLog", conf.level=0.95)

priskran(rr=donne$ln_risk, u=donne$ln_upper_ci, l=donne$ln_lower_ci,
form="Log", conf.level=0.95)

Package-Metaan-Rep/Metaan documentation built on Dec. 28, 2021, 6:40 a.m.