R/alpexfix.R
alpexfix.RdFixed effect meta-analysis of excess relative risk (ERR) or excess odds ratio (EOR) estimates with Richardson et al 2020 alternative model.
alpexfix(err, u, l, d, conf.level = 0.95)
| err | 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. |
| d | A numeric vector of the maximum dose reported from the individual studies. |
| conf.level | Coverage for the confidence interval |
Object of class "metaan.arf". A list that print the output from the alpexfix function. The following could be found from the list :
err_tot (Effect): The pooled effect from excess relative risk (ERR) or excess odd ratio (EOR) estimates
sd_tot_lnERR (SE-Log(Effect)): The standard error of the logarithm 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 (err_tot)
u_tot (Upper CI): The upper confidence interval bound of the pooled effect (err_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
Richardson, D. B., Abalo, K., Bernier, M. O., Rage, E., Leuraud, K., Laurier, D., ... & Little, M. P. (2020). Meta-analysis of published excess relative risk estimates. Radiation and Environmental Biophysics, 1-11.
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) dose <- c(32.586, 15.257, 72.431, 6.528, 10.886, 11.659, 17.254, 20.312, 10.996, 30.499, 41.145) donne <- data.frame(cbind(study, Risk, lower_ci, upper_ci, dose)) 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)) donne$dose <- as.numeric(as.character(donne$dose)) alpexfix(err=donne$Risk, u=donne$upper_ci, l=donne$lower_ci, d=donne$dose, conf.level=0.95)#> #> Alternative meta-analysis with fixed effect model #> ---------------------------------------------------- #> #> Effect SE-Log(Effect) Lower CI Upper CI #> 0.65 0.04 0.59 0.71 #> #> ---------------------------------------------------- #> #> Test of heterogeneity #> #> Cochran Q statistic Degree of Freedom P-Value #> 159.52 10.00 0 #> #> ---------------------------------------------------- #> #> Higgins and Thompson I^2 (%) #> 93.73 #> ____________________________________________________ #>