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\name{epi.dsl}
\alias{epi.dsl}
\title{Mixed-effects meta-analysis of binary outcomes using the DerSimonian and Laird method
}
\description{
Computes individual study odds or risk ratios for binary outcome data. Computes the summary odds or risk ratio using the DerSimonian and Laird method. Performs a test of heterogeneity among trials. Performs a test for the overall difference between groups (that is, after pooling the studies, do treated groups differ significantly from controls?).
}
\usage{
epi.dsl(ev.trt, n.trt, ev.ctrl, n.ctrl, names, method = "odds.ratio",
alternative = c("two.sided", "less", "greater"), conf.level = 0.95)
}
\arguments{
\item{ev.trt}{observed number of events in the treatment group.}
\item{n.trt}{number in the treatment group.}
\item{ev.ctrl}{observed number of events in the control group.}
\item{n.ctrl}{number in the control group.}
\item{names}{character string identifying each trial.}
\item{method}{a character string indicating the method to be used. Options are \code{odds.ratio} or \code{risk.ratio}.}
\item{alternative}{a character string specifying the alternative hypothesis, must be one of \code{two.sided}, \code{greater} or \code{less}. }
\item{conf.level}{magnitude of the returned confidence interval. Must be a single number between 0 and 1.}
}
\details{
\code{alternative = "greater"} tests the hypothesis that the DerSimonian and Laird summary measure of association is greater than 1.
}
\value{
A list containing the following:
\item{OR}{the odds ratio for each trial and the lower and upper bounds of the confidence interval of the odds ratio for each trial.}
\item{RR}{the risk ratio for each trial and the lower and upper bounds of the confidence interval of the risk ratio for each trial.}
\item{OR.summary}{the DerSimonian and Laird summary odds ratio and the lower and upper bounds of the confidence interval of the DerSimonian and Laird summary odds ratio.}
\item{RR.summary}{the DerSimonian and Laird summary risk ratio and the lower and upper bounds of the confidence interval of the DerSimonian and Laird summary risk ratio.}
\item{weights}{the inverse variance and DerSimonian and Laird weights for each trial.}
\item{heterogeneity}{a vector containing \code{Q} the heterogeneity test statistic, \code{df} the degrees of freedom and its associated P-value.}
\item{Hsq}{the relative excess of the heterogeneity test statistic \code{Q} over the degrees of freedom \code{df}.}
\item{Isq}{the percentage of total variation in study estimates that is due to heterogeneity rather than chance.}
\item{tau.sq}{the variance of the treatment effect among trials.}
\item{effect}{a vector containing \code{z} the test statistic for overall treatment effect and its associated P-value.}
}
\references{
Deeks JJ, Altman DG, Bradburn MJ (2001). Statistical methods for examining heterogeneity and combining results from several studies in meta-analysis. In: Egger M, Davey Smith G, Altman D (eds). Systematic Review in Health Care Meta-Analysis in Context. British Medical Journal, London, 2001, pp. 291 - 299.
DerSimonian R, Laird N (1986). Meta-analysis in clinical trials. Controlled Clinical Trials 7: 177 - 188.
Higgins J, Thompson S (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 21: 1539 - 1558.
}
\note{
Under the random-effects model, the assumption of a common treatment effect is relaxed, and the effect sizes are assumed to have a normal distribution with variance \code{tau.sq}.
Using this method, the DerSimonian and Laird weights are used to compute the pooled odds ratio.
The function checks each strata for cells with zero frequencies. If a zero frequency is found in any cell, 0.5 is added to all cells within the strata.
}
\seealso{
\code{\link{epi.iv}}, \code{\link{epi.mh}}, \code{\link{epi.smd}}
}
\examples{
## EXAMPLE 1:
data(epi.epidural)
epi.dsl(ev.trt = epi.epidural$ev.trt, n.trt = epi.epidural$n.trt,
ev.ctrl = epi.epidural$ev.ctrl, n.ctrl = epi.epidural$n.ctrl,
names = as.character(epi.epidural$trial), method = "odds.ratio",
alternative = "two.sided", conf.level = 0.95)
}
\keyword{univar}% at least one, from doc/KEYWORDS
\keyword{univar}% __ONLY ONE__ keyword per line
|
