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R/functions_summary.R
In metaumbrella: Umbrella Review Package for R
Defines functions
summary.umbrella
Documented in
summary.umbrella
#' Synthesize information of an object of class \dQuote{umbrella} in a dataframe
#'
#' @param object an object of class \dQuote{umbrella}
#' @param digits an integer value specifying the number of decimal places for the rounding of numeric values. Default is 3.
#' @param het_max a logical variable indicating whether additional information on heterogeneity should be printed (\eqn{tau^2}, Q-statistic estimate and p-value).
#' @param ... other arguments that can be passed to the function
#'
#' @details
#' Summary method for objects of class \dQuote{umbrella}.
#'
#' @return
#' All main results of an object of class \dQuote{umbrella} are synthesized in a dataframe, with the results of each factors stored in their own row.
#' Depending on the classification used, the dataframe returned include certain information presented below:
#' \tabular{ll}{
#' \code{Factor} \tab the name of the factor.\cr
#' \tab \cr
#' \code{Class} \tab the class assigned during the stratification of evidence.\cr
#' \tab \cr
#' \code{n_studies} \tab the number of independent studies included in the factor.\cr
#' \tab \cr
#' \code{total_n} \tab the total number of participants included in the factor.\cr
#' \tab \cr
#' \code{n_cases} \tab the number of cases included in the factor.\cr
#' \tab \cr
#' \code{n_controls} \tab the number of controls included in the factor.\cr
#' \tab \cr
#' \code{measure} \tab the measured used in the calculations.\cr
#' \tab \cr
#' \code{value} \tab the value of the pooled effect size expressed in its original metric. Note that\cr
#' \tab if a factor includes only one study, its effect size is used as the pooled effect size.\cr
#' \tab \cr
#' \code{value_CI} \tab the 95% confidence interval (CI) around the pooled effect size expressed \cr
#' \tab in its original metric. Note that if a factor includes only one study, its 95% CI is\cr
#' \tab used as the pooled 95% CI.\cr
#' \tab \cr
#' \code{eG} \tab the value of the pooled effect size expressed in equivalent Hedges' g (eG).\cr
#' \tab \cr
#' \code{eG_CI} \tab the 95% CI around the pooled effect size expressed in eG.\cr
#' \tab \cr
#' \code{eOR} \tab the value of the pooled effect size expressed in equivalent Odds ratio (eOR).\cr
#' \tab \cr
#' \code{eOR_CI} \tab the 95% CI around the pooled effect size expressed in eOR.\cr
#' \tab \cr
#' \code{p_value} \tab the p-value of the pooled effect size.\cr
#' \tab \cr
#' \code{I2} \tab the inconsistency (\eqn{I^2}) value (calculated only if the number of studies in the\cr
#' \tab meta-analysis is equal or larger to 2).\cr
#' \tab \cr
#' \code{PI_eG} \tab the 95% prediction interval (PI) expressed in eG (calculated only if the number\cr
#' \tab of studies in the meta-analysis is equal or larger to 3).\cr
#' \tab \cr
#' \code{PI_eOR} \tab the 95% PI expressed in eOR (calculated only if the number of studies in the \cr
#' \tab meta-analysis is equal or larger to 3).\cr
#' \tab \cr
#' \code{PI_sign} \tab whether the 95% PI includes the null value ("notnull" vs. "null").\cr
#' \tab \cr
#' \code{egger_p} \tab the p-value of the Egger's test for publication bias (calculated only\cr
#' \tab if the number of studies in the meta-analysis is equal or larger to 3).\cr
#' \tab \cr
#' \code{egger_sign} \tab whether the p-value of the Egger's test is < .05 ("sig." vs. "ns").\cr
#' \tab \cr
#' \code{ESB_p} \tab the p-value of the test for excess of significance bias.\cr
#' \tab \cr
#' \code{ESB_sign} \tab whether the p-value of the excess of significance test is < .05 ("sig." vs. "ns").\cr
#' \tab \cr
#' \code{power_med} \tab the power to detect a SMD of 0.5 at an alpha of .05 based on the number of\cr
#' \tab cases and controls included in the meta-analysis (when IRR is used as effect size\cr
#' \tab measure, the number of cases and controls in this calculation is assumed to be equal to\cr
#' \tab half the total number of cases included in the meta-analysis).\cr
#' \tab \cr
#' \code{power} \tab present only in the 'Personalized' classification. \cr
#' \tab - If the user did not use the 'power' criteria to stratify the evidence, this column \cr
#' \tab contains the power to detect a small effect size (SMD = 0.2), a moderate effect\cr
#' \tab size (SMD = 0.5) and a large effect size (SMD = 0.8) at an alpha of .05 based on \cr
#' \tab the number of cases and controls included in the meta-analysis.\cr
#' \tab - If the user used the 'power' criteria to stratify the evidence, this column contains\cr
#' \tab the power to detect the values entered by the user at an alpha of .05 based on the \cr
#' \tab number of cases and controls included in the meta-analysis.\cr
#' \tab \cr
#' \code{JK_p} \tab the largest p-value obtained in the jackknife leave-one-out meta-analysis (calculated\cr
#' \tab only if the number of studies in the meta-analysis is equal or larger to 2)\cr
#' \tab \cr
#' \code{JK_sign} \tab whether the largest p-value in the jackknife meta-analysis is < .05 ("sig." vs. "ns")\cr
#' \tab \cr
#' \code{largest_CI_eG} \tab the 95% CI of the largest study expressed in eG\cr
#' \tab \cr
#' \code{largest_CI_eOR} \tab the 95% CI of the largest study expressed in eOR\cr
#' \tab \cr
#' \code{largest_sign} \tab whether the 95% CI of the largest study includes the null value ("notnull" vs. \cr
#' \tab "null")\cr
#' \tab \cr
#' \code{rob} \tab the percentage of participants included in studies at low risk of bias (calculated\cr
#' \tab only if this information is indicated in the dataset)\cr
#' \tab \cr
#' \code{amstar} \tab the AMSTAR score of the meta-analysis (calculated only if this information is\cr
#' \tab indicated in the dataset)\cr
#' \tab \cr
#'}
#'
#' @seealso
#' \code{\link{metaumbrella-package}()} for the formatting of well-formatted datasets\cr
#' \code{\link{umbrella}()} for conducting calculations needed for an umbrella review\cr
#' \code{\link{add.evidence}()} for stratifying evidence in an umbrella review\cr
#'
#' @exportS3Method
#'
#' @export summary.umbrella
#'
#' @md
#'
#' @examples
#' ### generate a summary of the results of an umbrella object
#' summary(umbrella(df.SMD))
summary.umbrella = function(object, digits = 3, het_max = FALSE, ...) {
y = NULL
criteria = attr(object, "criteria")
for (name in names(object)) {
x_i = object[[name]]
measure = x_i$measure
if (measure %in% c("SMD", "SMC")) {
value = round(x_i$ma_results$value, digits)
ci_lo = round(x_i$ma_results$ci_lo, digits); ci_up = round(x_i$ma_results$ci_up, digits)
pi_lo = round(x_i$ma_results$pi_lo, digits); pi_up = round(x_i$ma_results$pi_up, digits)
largest_ci_lo = round(x_i$largest$ci_lo, digits)
largest_ci_up = round(x_i$largest$ci_up, digits)
} else if (measure %in% c("OR", "RR", "HR", "IRR")) {
value = round(exp(x_i$ma_results$value), digits)
ci_lo = round(exp(x_i$ma_results$ci_lo), digits); ci_up = round(exp(x_i$ma_results$ci_up), digits)
pi_lo = round(exp(x_i$ma_results$pi_lo), digits); pi_up = round(exp(x_i$ma_results$pi_up), digits)
largest_ci_lo = round(exp(x_i$largest$ci_lo), digits)
largest_ci_up = round(exp(x_i$largest$ci_up), digits)
} else if (measure == "Z") {
value = round(.z_to_r(x_i$ma_results$value), digits)
ci_lo = round(.z_to_r(x_i$ma_results$ci_lo), digits); ci_up = round(.z_to_r(x_i$ma_results$ci_up), digits)
pi_lo = round(.z_to_r(x_i$ma_results$pi_lo), digits); pi_up = round(.z_to_r(x_i$ma_results$pi_up), digits)
largest_ci_lo = round(.z_to_r(x_i$largest$ci_lo), digits)
largest_ci_up = round(.z_to_r(x_i$largest$ci_up), digits)
}
value_CI = paste0("[", ci_lo, ", ", ci_up, "]")
eOR = switch(as.character(measure),
"SMD" =, "SMC" = round(.d_to_or(x_i$ma_results$value), digits),
"Z" = round(.d_to_or(.z_to_d(x_i$ma_results$value)), digits),
"OR" = , "RR" = , "IRR" = , "HR" = value)
eOR_CI = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", round(.d_to_or(x_i$ma_results$ci_lo), digits), ", ", round(.d_to_or(x_i$ma_results$ci_up), digits), "]"),
"Z" = paste0("[", round(.d_to_or(.z_to_d(x_i$ma_results$ci_lo)), digits), ", ", round(.d_to_or(.z_to_d(x_i$ma_results$ci_up)), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", ci_lo, ", ", ci_up, "]"))
eG = switch(as.character(measure),
"SMD" =, "SMC" = value,
"Z" = round(.z_to_d(x_i$ma_results$value), digits),
"OR" = , "RR" = , "IRR" = , "HR" = round(.or_to_d(exp(x_i$ma_results$value)), digits))
eG_CI = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", ci_lo, ", ", ci_up, "]"),
"Z" = paste0("[", round(.z_to_d(ci_lo), digits), ", ", round(.z_to_d(x_i$ma_results$ci_up), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", round(.or_to_d(exp(x_i$ma_results$ci_lo)), digits), ", ", round(.or_to_d(exp(x_i$ma_results$ci_up)), digits), "]"))
p_value = ifelse(!is.na(as.numeric(as.character(x_i$ma_results$p.value))),
sprintf(paste0("%.", digits - 1, "e"), .as_numeric(x_i$ma_results$p.value)),
as.character(x_i$ma_results$p.value))
# sample sizes
n_studies = x_i$n$studies
n_cases = x_i$n$cases
n_controls = x_i$n$controls
total_n = x_i$n$total_n
# I2
if (het_max) {
I2 = data.frame(
tau2 = ifelse(nrow(x_i$x) == 1, "only 1 study", round(.as_numeric(x_i$heterogeneity$tau2), digits)),
I2 = ifelse(nrow(x_i$x) == 1, "only 1 study", round(.as_numeric(x_i$heterogeneity$i2), digits)),
Q = ifelse(nrow(x_i$x) == 1, "only 1 study", round(.as_numeric(x_i$heterogeneity$qe), digits)),
Q_p_value = ifelse(nrow(x_i$x) == 1, "only 1 study", sprintf(paste0("%.", digits - 1, "e"), .as_numeric(x_i$heterogeneity$p.value))))
} else {
I2 = ifelse(nrow(x_i$x) == 1, "only 1 study", round(.as_numeric(x_i$heterogeneity$i2), digits))
}
# PI
if (x_i$n$studies < 3) {
PI_eOR = "< 3 studies"
PI_eG = "< 3 studies"
} else {
PI_eG = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", pi_lo, ", ", pi_up, "]"),
"Z" = paste0("[", round(.z_to_d(x_i$ma_results$pi_lo), digits), ", ", round(.z_to_d(x_i$ma_results$pi_up), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", round(.or_to_d(exp(x_i$ma_results$pi_lo)), digits), ", ", round(.or_to_d(exp(x_i$ma_results$pi_up)), digits), "]"))
PI_eOR = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", round(.d_to_or(x_i$ma_results$pi_lo), digits), ", ",
round(.d_to_or(x_i$ma_results$pi_up), digits), "]"),
"Z" = paste0("[", round(.d_to_or(.z_to_d(x_i$ma_results$pi_lo)), digits), ", ",
round(.d_to_or(.z_to_d(x_i$ma_results$pi_up)), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", pi_lo, ", ", pi_up, "]"))
}
PI_sign = ifelse(is.numeric(pi_lo),
ifelse(sign(x_i$ma_results$pi_lo) == sign(x_i$ma_results$pi_up), "notnull", "null"),
"NA")
# Egger
egger_p = ifelse(x_i$n$studies < 3,
"< 3 studies",
ifelse(!is.na(x_i$egger$p.value),
sprintf(paste0("%.", digits - 1, "e"), .as_numeric(x_i$egger$p.value)),
as.character(x_i$egger$p.value))
)
egger_sign = factor(x_i$egger$p.value < 0.05, levels = c(FALSE, TRUE), labels = c("ns", "sig."))
# ESB
ESB_p = ifelse(!is.na(x_i$esb$p.value),
sprintf(paste0("%.", digits - 1, "e"), .as_numeric(x_i$esb$p.value)),
as.character(x_i$esb$p.value))
ESB_sign = factor(x_i$esb$p.value < 0.05, levels = c(FALSE, TRUE), labels = c("ns", "sig."))
# Largest
largest_sign = factor(sign(x_i$largest$ci_lo) == sign(x_i$largest$ci_up), levels = c(FALSE, TRUE), labels = c("null", "notnull"))
largest_CI_eOR = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", round(.d_to_or(x_i$largest$ci_lo), digits), ", ",
round(.d_to_or(x_i$largest$ci_up), digits), "]"),
"Z" = paste0("[", round(.d_to_or(.z_to_d(x_i$largest$ci_lo)), digits), ", ",
round(.d_to_or(.z_to_d(x_i$largest$ci_up)), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", largest_ci_lo, ", ", largest_ci_up, "]"))
largest_CI_eG = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", largest_ci_lo, ", ", largest_ci_up, "]"),
"Z" = paste0("[", round(.z_to_d(x_i$largest$ci_lo), digits), ", ",
round(.z_to_d(x_i$largest$ci_up), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", round(.or_to_d(exp(x_i$largest$ci_lo)), digits), ", ",
round(.or_to_d(exp(x_i$largest$ci_up)), digits), "]"))
# JK
JK_p = ifelse(x_i$n$studies == 1, "only 1 study",
sprintf(paste0("%.", digits - 1, "e"), .as_numeric(max(x_i$jk)))
)
JK_sign = factor(JK_p < .05, levels = c(FALSE, TRUE), labels = c("n.s.", "sig."))
# RoB
rob = round(x_i$riskofbias, 2)
# AMSTAR
amstar = x_i$amstar
# Power
power = if (measure == "IRR") {
paste0(
"Low es = ",
round(.power_d(x_i$n$cases/2, x_i$n$cases/2, 0.2), digits)*100,
"; Med. es = ",
round(.power_d(x_i$n$cases/2, x_i$n$cases/2, 0.5), digits)*100,
"; Large es = ",
round(.power_d(x_i$n$cases/2, x_i$n$cases/2, 0.8), digits)*100)
} else if (measure == "Z") {
paste0(
"Low es = ",
round(.power_d(x_i$n$total_n/2, x_i$n$total_n/2, 0.2), digits)*100,
"; Med. es = ",
round(.power_d(x_i$n$total_n/2, x_i$n$total_n/2, 0.5), digits)*100,
"; Large es = ",
round(.power_d(x_i$n$total_n/2, x_i$n$total_n/2, 0.8), digits)*100)
} else {
paste0(
"Low es = ",
round(.power_d(x_i$n$cases, x_i$n$controls, 0.2), digits)*100,
"; Med. es = ",
round(.power_d(x_i$n$cases, x_i$n$controls, 0.5), digits)*100,
"; Large es = ",
round(.power_d(x_i$n$cases, x_i$n$controls, 0.8), digits)*100)
}
power_med = switch(as.character(measure),
"IRR" = round(.power_d(x_i$n$cases/2, x_i$n$cases/2, 0.5), digits)*100,
"Z" = round(.power_d(x_i$n$total_n/2, x_i$n$total_n/2, 0.5), digits)*100,
"SMD"=, "SMC" =, "OR"=, "RR"=, "HR" = round(.power_d(x_i$n$cases, x_i$n$controls, 0.5), digits)*100)
measure = ifelse(measure %in% c("SMD"), "G", ifelse(measure == "Z", "R", measure))
######################################################################################
if (is.null(criteria)) {
out_i = data.frame(
Factor = name, n_studies, total_n, n_cases, n_controls,
measure,
value, value_CI,
eG, eG_CI,
eOR, eOR_CI,
p_value,
I2,
PI_eG, PI_eOR,
egger_p, ESB_p,
power_med, JK_p,
largest_CI_eG, largest_CI_eOR,
rob, amstar
)
y = rbind(y, out_i)
} else {
######################################################################################
switch (criteria,
"Ioannidis" = {
out_i = data.frame(
Factor = name,
Criteria = "Ioannidis",
Class = x_i$evidence,
measure = measure,
value,
value_CI,
eG,
eG_CI,
eOR,
eOR_CI,
p_value,
n_studies,
n_cases,
n_controls,
PI_eG, PI_eOR,
PI_sign,
I2,
egger_p,
egger_sign,
ESB_p,
ESB_sign,
largest_CI_eG, largest_CI_eOR,
largest_sign
)
y = rbind(y, out_i)
},
######################################################################################
"Personalised" = {
out_i = data.frame(Factor = name,
Criteria = "Personalized",
Class = x_i$evidence,
measure = measure,
value,
value_CI,
eG,
eG_CI,
eOR,
eOR_CI,
p_value,
n_studies,
total_n,
n_cases,
PI_eG, PI_eOR,
PI_sign,
I2,
egger_p,
ESB_p,
largest_CI_eG, largest_CI_eOR,
largest_sign,
JK_p,
JK_sign,
rob,
amstar,
power_med,
power = if (all(is.na(x_i$imprecision))) {
power
} else {
paste0(
"es1 = ", round(x_i$imprecision[1]*100), "; es2 = ",
round(x_i$imprecision[2]*100), "; es3 = ",
round(x_i$imprecision[3]*100), "; es4 = ",
round(x_i$imprecision[4]*100))
}
)
y = rbind(y, out_i)
},
######################################################################################
"GRADE" = {
out_i = data.frame(
Factor = name,
Criteria = "GRADE",
Class = x_i$evidence,
measure = measure,
value,
value_CI,
eG,
eG_CI,
eOR,
eOR_CI,
p_value,
n_studies,
n_cases,
total_n,
I2,
egger_p,
egger_sign,
rob,
power
)
y = rbind(y, out_i)
}
)
}
}
if (is.null(criteria)) {
} else if (criteria == "Ioannidis") {
y$Class = factor(y$Class, levels = c('I','II','III','IV','ns'))
y = y[order(log(y$eOR), decreasing = TRUE),]
y = y[order(y$Class), ]
} else if (criteria == "Personalised") {
y$Class = factor(y$Class, levels = c("I", "II", "III", "IV", "V"))
y = y[order(y$eOR, decreasing = TRUE),]
y = y[order(y$Class), ]
} else if (criteria == "GRADE") {
y$Class = factor(y$Class, levels = c('High','Moderate','Weak','Very weak'))
y = y[order(y$eOR, decreasing = TRUE),]
y = y[order(y$Class), ]
}
return(y)
}
#' Print a summary of an object of class \dQuote{umbrella}
#'
#' @param x an object of class \dQuote{umbrella}
#' @param ... other arguments that can be passed to the function
#'
#' @details
#' Summary method for objects of class \dQuote{umbrella}.
#'
#' @return
#' Implicitly calls the \code{\link{summary.umbrella}()} function and displays error or warning messages below the object returned.
#' This is useful when many factors are included in the review and that the results of the \code{\link{summary.umbrella}()} are not stored in an object.
#'
#' @export
#'
#' @md
#'
#' @seealso
#' \code{\link{summary.umbrella}()}
#'
#' @examples
#' \donttest{
#' ### print the results of an object of class umbrella
#' umbrella(df.OR.multi, mult.level = TRUE)
#' }
print.umbrella = function (x, ...) {
cat("\nUmbrella review:\n")
if (!is.null(attr(x, "message"))) {
y <- summary.umbrella(x, ...)
print(y)
message(attr(x, "message"))
} else {
y = summary.umbrella(x, ...)
invisible(y)
return(print(y))
}
}
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Defines functions summary.umbrella
Documented in summary.umbrella
#' Synthesize information of an object of class \dQuote{umbrella} in a dataframe
#'
#' @param object an object of class \dQuote{umbrella}
#' @param digits an integer value specifying the number of decimal places for the rounding of numeric values. Default is 3.
#' @param het_max a logical variable indicating whether additional information on heterogeneity should be printed (\eqn{tau^2}, Q-statistic estimate and p-value).
#' @param ... other arguments that can be passed to the function
#'
#' @details
#' Summary method for objects of class \dQuote{umbrella}.
#'
#' @return
#' All main results of an object of class \dQuote{umbrella} are synthesized in a dataframe, with the results of each factors stored in their own row.
#' Depending on the classification used, the dataframe returned include certain information presented below:
#' \tabular{ll}{
#' \code{Factor} \tab the name of the factor.\cr
#' \tab \cr
#' \code{Class} \tab the class assigned during the stratification of evidence.\cr
#' \tab \cr
#' \code{n_studies} \tab the number of independent studies included in the factor.\cr
#' \tab \cr
#' \code{total_n} \tab the total number of participants included in the factor.\cr
#' \tab \cr
#' \code{n_cases} \tab the number of cases included in the factor.\cr
#' \tab \cr
#' \code{n_controls} \tab the number of controls included in the factor.\cr
#' \tab \cr
#' \code{measure} \tab the measured used in the calculations.\cr
#' \tab \cr
#' \code{value} \tab the value of the pooled effect size expressed in its original metric. Note that\cr
#' \tab if a factor includes only one study, its effect size is used as the pooled effect size.\cr
#' \tab \cr
#' \code{value_CI} \tab the 95% confidence interval (CI) around the pooled effect size expressed \cr
#' \tab in its original metric. Note that if a factor includes only one study, its 95% CI is\cr
#' \tab used as the pooled 95% CI.\cr
#' \tab \cr
#' \code{eG} \tab the value of the pooled effect size expressed in equivalent Hedges' g (eG).\cr
#' \tab \cr
#' \code{eG_CI} \tab the 95% CI around the pooled effect size expressed in eG.\cr
#' \tab \cr
#' \code{eOR} \tab the value of the pooled effect size expressed in equivalent Odds ratio (eOR).\cr
#' \tab \cr
#' \code{eOR_CI} \tab the 95% CI around the pooled effect size expressed in eOR.\cr
#' \tab \cr
#' \code{p_value} \tab the p-value of the pooled effect size.\cr
#' \tab \cr
#' \code{I2} \tab the inconsistency (\eqn{I^2}) value (calculated only if the number of studies in the\cr
#' \tab meta-analysis is equal or larger to 2).\cr
#' \tab \cr
#' \code{PI_eG} \tab the 95% prediction interval (PI) expressed in eG (calculated only if the number\cr
#' \tab of studies in the meta-analysis is equal or larger to 3).\cr
#' \tab \cr
#' \code{PI_eOR} \tab the 95% PI expressed in eOR (calculated only if the number of studies in the \cr
#' \tab meta-analysis is equal or larger to 3).\cr
#' \tab \cr
#' \code{PI_sign} \tab whether the 95% PI includes the null value ("notnull" vs. "null").\cr
#' \tab \cr
#' \code{egger_p} \tab the p-value of the Egger's test for publication bias (calculated only\cr
#' \tab if the number of studies in the meta-analysis is equal or larger to 3).\cr
#' \tab \cr
#' \code{egger_sign} \tab whether the p-value of the Egger's test is < .05 ("sig." vs. "ns").\cr
#' \tab \cr
#' \code{ESB_p} \tab the p-value of the test for excess of significance bias.\cr
#' \tab \cr
#' \code{ESB_sign} \tab whether the p-value of the excess of significance test is < .05 ("sig." vs. "ns").\cr
#' \tab \cr
#' \code{power_med} \tab the power to detect a SMD of 0.5 at an alpha of .05 based on the number of\cr
#' \tab cases and controls included in the meta-analysis (when IRR is used as effect size\cr
#' \tab measure, the number of cases and controls in this calculation is assumed to be equal to\cr
#' \tab half the total number of cases included in the meta-analysis).\cr
#' \tab \cr
#' \code{power} \tab present only in the 'Personalized' classification. \cr
#' \tab - If the user did not use the 'power' criteria to stratify the evidence, this column \cr
#' \tab contains the power to detect a small effect size (SMD = 0.2), a moderate effect\cr
#' \tab size (SMD = 0.5) and a large effect size (SMD = 0.8) at an alpha of .05 based on \cr
#' \tab the number of cases and controls included in the meta-analysis.\cr
#' \tab - If the user used the 'power' criteria to stratify the evidence, this column contains\cr
#' \tab the power to detect the values entered by the user at an alpha of .05 based on the \cr
#' \tab number of cases and controls included in the meta-analysis.\cr
#' \tab \cr
#' \code{JK_p} \tab the largest p-value obtained in the jackknife leave-one-out meta-analysis (calculated\cr
#' \tab only if the number of studies in the meta-analysis is equal or larger to 2)\cr
#' \tab \cr
#' \code{JK_sign} \tab whether the largest p-value in the jackknife meta-analysis is < .05 ("sig." vs. "ns")\cr
#' \tab \cr
#' \code{largest_CI_eG} \tab the 95% CI of the largest study expressed in eG\cr
#' \tab \cr
#' \code{largest_CI_eOR} \tab the 95% CI of the largest study expressed in eOR\cr
#' \tab \cr
#' \code{largest_sign} \tab whether the 95% CI of the largest study includes the null value ("notnull" vs. \cr
#' \tab "null")\cr
#' \tab \cr
#' \code{rob} \tab the percentage of participants included in studies at low risk of bias (calculated\cr
#' \tab only if this information is indicated in the dataset)\cr
#' \tab \cr
#' \code{amstar} \tab the AMSTAR score of the meta-analysis (calculated only if this information is\cr
#' \tab indicated in the dataset)\cr
#' \tab \cr
#'}
#'
#' @seealso
#' \code{\link{metaumbrella-package}()} for the formatting of well-formatted datasets\cr
#' \code{\link{umbrella}()} for conducting calculations needed for an umbrella review\cr
#' \code{\link{add.evidence}()} for stratifying evidence in an umbrella review\cr
#'
#' @exportS3Method
#'
#' @export summary.umbrella
#'
#' @md
#'
#' @examples
#' ### generate a summary of the results of an umbrella object
#' summary(umbrella(df.SMD))
summary.umbrella = function(object, digits = 3, het_max = FALSE, ...) {
y = NULL
criteria = attr(object, "criteria")
for (name in names(object)) {
x_i = object[[name]]
measure = x_i$measure
if (measure %in% c("SMD", "SMC")) {
value = round(x_i$ma_results$value, digits)
ci_lo = round(x_i$ma_results$ci_lo, digits); ci_up = round(x_i$ma_results$ci_up, digits)
pi_lo = round(x_i$ma_results$pi_lo, digits); pi_up = round(x_i$ma_results$pi_up, digits)
largest_ci_lo = round(x_i$largest$ci_lo, digits)
largest_ci_up = round(x_i$largest$ci_up, digits)
} else if (measure %in% c("OR", "RR", "HR", "IRR")) {
value = round(exp(x_i$ma_results$value), digits)
ci_lo = round(exp(x_i$ma_results$ci_lo), digits); ci_up = round(exp(x_i$ma_results$ci_up), digits)
pi_lo = round(exp(x_i$ma_results$pi_lo), digits); pi_up = round(exp(x_i$ma_results$pi_up), digits)
largest_ci_lo = round(exp(x_i$largest$ci_lo), digits)
largest_ci_up = round(exp(x_i$largest$ci_up), digits)
} else if (measure == "Z") {
value = round(.z_to_r(x_i$ma_results$value), digits)
ci_lo = round(.z_to_r(x_i$ma_results$ci_lo), digits); ci_up = round(.z_to_r(x_i$ma_results$ci_up), digits)
pi_lo = round(.z_to_r(x_i$ma_results$pi_lo), digits); pi_up = round(.z_to_r(x_i$ma_results$pi_up), digits)
largest_ci_lo = round(.z_to_r(x_i$largest$ci_lo), digits)
largest_ci_up = round(.z_to_r(x_i$largest$ci_up), digits)
}
value_CI = paste0("[", ci_lo, ", ", ci_up, "]")
eOR = switch(as.character(measure),
"SMD" =, "SMC" = round(.d_to_or(x_i$ma_results$value), digits),
"Z" = round(.d_to_or(.z_to_d(x_i$ma_results$value)), digits),
"OR" = , "RR" = , "IRR" = , "HR" = value)
eOR_CI = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", round(.d_to_or(x_i$ma_results$ci_lo), digits), ", ", round(.d_to_or(x_i$ma_results$ci_up), digits), "]"),
"Z" = paste0("[", round(.d_to_or(.z_to_d(x_i$ma_results$ci_lo)), digits), ", ", round(.d_to_or(.z_to_d(x_i$ma_results$ci_up)), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", ci_lo, ", ", ci_up, "]"))
eG = switch(as.character(measure),
"SMD" =, "SMC" = value,
"Z" = round(.z_to_d(x_i$ma_results$value), digits),
"OR" = , "RR" = , "IRR" = , "HR" = round(.or_to_d(exp(x_i$ma_results$value)), digits))
eG_CI = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", ci_lo, ", ", ci_up, "]"),
"Z" = paste0("[", round(.z_to_d(ci_lo), digits), ", ", round(.z_to_d(x_i$ma_results$ci_up), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", round(.or_to_d(exp(x_i$ma_results$ci_lo)), digits), ", ", round(.or_to_d(exp(x_i$ma_results$ci_up)), digits), "]"))
p_value = ifelse(!is.na(as.numeric(as.character(x_i$ma_results$p.value))),
sprintf(paste0("%.", digits - 1, "e"), .as_numeric(x_i$ma_results$p.value)),
as.character(x_i$ma_results$p.value))
# sample sizes
n_studies = x_i$n$studies
n_cases = x_i$n$cases
n_controls = x_i$n$controls
total_n = x_i$n$total_n
# I2
if (het_max) {
I2 = data.frame(
tau2 = ifelse(nrow(x_i$x) == 1, "only 1 study", round(.as_numeric(x_i$heterogeneity$tau2), digits)),
I2 = ifelse(nrow(x_i$x) == 1, "only 1 study", round(.as_numeric(x_i$heterogeneity$i2), digits)),
Q = ifelse(nrow(x_i$x) == 1, "only 1 study", round(.as_numeric(x_i$heterogeneity$qe), digits)),
Q_p_value = ifelse(nrow(x_i$x) == 1, "only 1 study", sprintf(paste0("%.", digits - 1, "e"), .as_numeric(x_i$heterogeneity$p.value))))
} else {
I2 = ifelse(nrow(x_i$x) == 1, "only 1 study", round(.as_numeric(x_i$heterogeneity$i2), digits))
}
# PI
if (x_i$n$studies < 3) {
PI_eOR = "< 3 studies"
PI_eG = "< 3 studies"
} else {
PI_eG = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", pi_lo, ", ", pi_up, "]"),
"Z" = paste0("[", round(.z_to_d(x_i$ma_results$pi_lo), digits), ", ", round(.z_to_d(x_i$ma_results$pi_up), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", round(.or_to_d(exp(x_i$ma_results$pi_lo)), digits), ", ", round(.or_to_d(exp(x_i$ma_results$pi_up)), digits), "]"))
PI_eOR = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", round(.d_to_or(x_i$ma_results$pi_lo), digits), ", ",
round(.d_to_or(x_i$ma_results$pi_up), digits), "]"),
"Z" = paste0("[", round(.d_to_or(.z_to_d(x_i$ma_results$pi_lo)), digits), ", ",
round(.d_to_or(.z_to_d(x_i$ma_results$pi_up)), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", pi_lo, ", ", pi_up, "]"))
}
PI_sign = ifelse(is.numeric(pi_lo),
ifelse(sign(x_i$ma_results$pi_lo) == sign(x_i$ma_results$pi_up), "notnull", "null"),
"NA")
# Egger
egger_p = ifelse(x_i$n$studies < 3,
"< 3 studies",
ifelse(!is.na(x_i$egger$p.value),
sprintf(paste0("%.", digits - 1, "e"), .as_numeric(x_i$egger$p.value)),
as.character(x_i$egger$p.value))
)
egger_sign = factor(x_i$egger$p.value < 0.05, levels = c(FALSE, TRUE), labels = c("ns", "sig."))
# ESB
ESB_p = ifelse(!is.na(x_i$esb$p.value),
sprintf(paste0("%.", digits - 1, "e"), .as_numeric(x_i$esb$p.value)),
as.character(x_i$esb$p.value))
ESB_sign = factor(x_i$esb$p.value < 0.05, levels = c(FALSE, TRUE), labels = c("ns", "sig."))
# Largest
largest_sign = factor(sign(x_i$largest$ci_lo) == sign(x_i$largest$ci_up), levels = c(FALSE, TRUE), labels = c("null", "notnull"))
largest_CI_eOR = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", round(.d_to_or(x_i$largest$ci_lo), digits), ", ",
round(.d_to_or(x_i$largest$ci_up), digits), "]"),
"Z" = paste0("[", round(.d_to_or(.z_to_d(x_i$largest$ci_lo)), digits), ", ",
round(.d_to_or(.z_to_d(x_i$largest$ci_up)), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", largest_ci_lo, ", ", largest_ci_up, "]"))
largest_CI_eG = switch(as.character(measure),
"SMD" =, "SMC" = paste0("[", largest_ci_lo, ", ", largest_ci_up, "]"),
"Z" = paste0("[", round(.z_to_d(x_i$largest$ci_lo), digits), ", ",
round(.z_to_d(x_i$largest$ci_up), digits), "]"),
"OR" = , "RR" = , "IRR" = , "HR" = paste0("[", round(.or_to_d(exp(x_i$largest$ci_lo)), digits), ", ",
round(.or_to_d(exp(x_i$largest$ci_up)), digits), "]"))
# JK
JK_p = ifelse(x_i$n$studies == 1, "only 1 study",
sprintf(paste0("%.", digits - 1, "e"), .as_numeric(max(x_i$jk)))
)
JK_sign = factor(JK_p < .05, levels = c(FALSE, TRUE), labels = c("n.s.", "sig."))
# RoB
rob = round(x_i$riskofbias, 2)
# AMSTAR
amstar = x_i$amstar
# Power
power = if (measure == "IRR") {
paste0(
"Low es = ",
round(.power_d(x_i$n$cases/2, x_i$n$cases/2, 0.2), digits)*100,
"; Med. es = ",
round(.power_d(x_i$n$cases/2, x_i$n$cases/2, 0.5), digits)*100,
"; Large es = ",
round(.power_d(x_i$n$cases/2, x_i$n$cases/2, 0.8), digits)*100)
} else if (measure == "Z") {
paste0(
"Low es = ",
round(.power_d(x_i$n$total_n/2, x_i$n$total_n/2, 0.2), digits)*100,
"; Med. es = ",
round(.power_d(x_i$n$total_n/2, x_i$n$total_n/2, 0.5), digits)*100,
"; Large es = ",
round(.power_d(x_i$n$total_n/2, x_i$n$total_n/2, 0.8), digits)*100)
} else {
paste0(
"Low es = ",
round(.power_d(x_i$n$cases, x_i$n$controls, 0.2), digits)*100,
"; Med. es = ",
round(.power_d(x_i$n$cases, x_i$n$controls, 0.5), digits)*100,
"; Large es = ",
round(.power_d(x_i$n$cases, x_i$n$controls, 0.8), digits)*100)
}
power_med = switch(as.character(measure),
"IRR" = round(.power_d(x_i$n$cases/2, x_i$n$cases/2, 0.5), digits)*100,
"Z" = round(.power_d(x_i$n$total_n/2, x_i$n$total_n/2, 0.5), digits)*100,
"SMD"=, "SMC" =, "OR"=, "RR"=, "HR" = round(.power_d(x_i$n$cases, x_i$n$controls, 0.5), digits)*100)
measure = ifelse(measure %in% c("SMD"), "G", ifelse(measure == "Z", "R", measure))
######################################################################################
if (is.null(criteria)) {
out_i = data.frame(
Factor = name, n_studies, total_n, n_cases, n_controls,
measure,
value, value_CI,
eG, eG_CI,
eOR, eOR_CI,
p_value,
I2,
PI_eG, PI_eOR,
egger_p, ESB_p,
power_med, JK_p,
largest_CI_eG, largest_CI_eOR,
rob, amstar
)
y = rbind(y, out_i)
} else {
######################################################################################
switch (criteria,
"Ioannidis" = {
out_i = data.frame(
Factor = name,
Criteria = "Ioannidis",
Class = x_i$evidence,
measure = measure,
value,
value_CI,
eG,
eG_CI,
eOR,
eOR_CI,
p_value,
n_studies,
n_cases,
n_controls,
PI_eG, PI_eOR,
PI_sign,
I2,
egger_p,
egger_sign,
ESB_p,
ESB_sign,
largest_CI_eG, largest_CI_eOR,
largest_sign
)
y = rbind(y, out_i)
},
######################################################################################
"Personalised" = {
out_i = data.frame(Factor = name,
Criteria = "Personalized",
Class = x_i$evidence,
measure = measure,
value,
value_CI,
eG,
eG_CI,
eOR,
eOR_CI,
p_value,
n_studies,
total_n,
n_cases,
PI_eG, PI_eOR,
PI_sign,
I2,
egger_p,
ESB_p,
largest_CI_eG, largest_CI_eOR,
largest_sign,
JK_p,
JK_sign,
rob,
amstar,
power_med,
power = if (all(is.na(x_i$imprecision))) {
power
} else {
paste0(
"es1 = ", round(x_i$imprecision[1]*100), "; es2 = ",
round(x_i$imprecision[2]*100), "; es3 = ",
round(x_i$imprecision[3]*100), "; es4 = ",
round(x_i$imprecision[4]*100))
}
)
y = rbind(y, out_i)
},
######################################################################################
"GRADE" = {
out_i = data.frame(
Factor = name,
Criteria = "GRADE",
Class = x_i$evidence,
measure = measure,
value,
value_CI,
eG,
eG_CI,
eOR,
eOR_CI,
p_value,
n_studies,
n_cases,
total_n,
I2,
egger_p,
egger_sign,
rob,
power
)
y = rbind(y, out_i)
}
)
}
}
if (is.null(criteria)) {
} else if (criteria == "Ioannidis") {
y$Class = factor(y$Class, levels = c('I','II','III','IV','ns'))
y = y[order(log(y$eOR), decreasing = TRUE),]
y = y[order(y$Class), ]
} else if (criteria == "Personalised") {
y$Class = factor(y$Class, levels = c("I", "II", "III", "IV", "V"))
y = y[order(y$eOR, decreasing = TRUE),]
y = y[order(y$Class), ]
} else if (criteria == "GRADE") {
y$Class = factor(y$Class, levels = c('High','Moderate','Weak','Very weak'))
y = y[order(y$eOR, decreasing = TRUE),]
y = y[order(y$Class), ]
}
return(y)
}
#' Print a summary of an object of class \dQuote{umbrella}
#'
#' @param x an object of class \dQuote{umbrella}
#' @param ... other arguments that can be passed to the function
#'
#' @details
#' Summary method for objects of class \dQuote{umbrella}.
#'
#' @return
#' Implicitly calls the \code{\link{summary.umbrella}()} function and displays error or warning messages below the object returned.
#' This is useful when many factors are included in the review and that the results of the \code{\link{summary.umbrella}()} are not stored in an object.
#'
#' @export
#'
#' @md
#'
#' @seealso
#' \code{\link{summary.umbrella}()}
#'
#' @examples
#' \donttest{
#' ### print the results of an object of class umbrella
#' umbrella(df.OR.multi, mult.level = TRUE)
#' }
print.umbrella = function (x, ...) {
cat("\nUmbrella review:\n")
if (!is.null(attr(x, "message"))) {
y <- summary.umbrella(x, ...)
print(y)
message(attr(x, "message"))
} else {
y = summary.umbrella(x, ...)
invisible(y)
return(print(y))
}
}
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