R/Estmeta.R
estmeta.RdFixed effect model or DerSimonian and Laird-based Random effect model for standard meta-analysis of Beta (the parameters or coefficients) estimated from regression models (e.g linear regression or generalised linear regression models).
estmeta(Beta, u, l, test = c("FIXED", "RANDOM"), conf.level = 0.95)
| Beta | A numeric vector of Beta (the parameters or coefficients) estimated from the individual studies |
|---|---|
| u | A numeric vector of the upper bound of the confidence interval of the Beta reported from the individual studies. |
| l | A numeric vector of the lower bound of the confidence interval of the Beta reported from the individual studies. |
| test | Logical, indicating the statistical method to be used. "FIXED" for the fixed effect odel and "RANDOM" for the random effect model. |
| conf.level | Coverage for confidence interval |
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
study <- c("Canada", "Northern USA", "Chicago", "Georgia","Puerto", "Comm", "Madanapalle", "UK", "South Africa", "Haiti", "Madras") beta<- 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, beta, lower_ci, upper_ci)) donne$beta <- as.numeric(as.character(donne$beta)) donne$upper_ci <- as.numeric(as.character(donne$upper_ci)) donne$lower_ci <- as.numeric(as.character(donne$lower_ci)) estmeta(Beta=donne$Risk, u=donne$upper_ci, l=donne$lower_ci, test="RANDOM")#> Warning: production de NaN#> #> Standard meta-analysis with RANDOM effect model #> -------------------------------------------------- #> #> Beta SE-Beta Lower CI Upper CI #> 0.00 0.03 -0.05 0.05 #> #> -------------------------- ----------------------- #> #> Test of heterogeneity : #> #> Cochran Q statistic Degree of Freedom P-Value #> 0 -1 NA #> #> -------------------------------------------------- #> #> Higgins and Thompson I^2 (%) #> Inf #> __________________________________________________ #>estmeta(Beta=donne$Risk, u=donne$upper_ci, l=donne$lower_ci, test="FIXED")#> Warning: production de NaN#> #> Standard meta-analysis with FIXED effect model #> -------------------------------------------------- #> #> Beta SE-Beta Lower CI Upper CI #> 0.00 0.02 -0.05 0.05 #> #> -------------------------- ----------------------- #> #> Test of heterogeneity : #> #> Cochran Q statistic Degree of Freedom P-Value #> 0 -1 NA #> #> -------------------------------------------------- #> #> Higgins and Thompson I^2 (%) #> Inf #> __________________________________________________ #>