priskfix: Pooled risk estimate using the fixed effect model...

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

Description

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

Usage

priskfix(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 logarithmic scale.
conf.level	Coverage for confidence interval

Value

A list of a pooled result from the individual studies

Object of class "metaan.rr". A list that print the output from the priskfix 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

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)


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


priskfix(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.