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?).
epi.dsl(ev.trt, n.trt, ev.ctrl, n.ctrl, names, method = "odds.ratio",
alternative = c("two.sided", "less", "greater"), conf.level = 0.95)A list containing the following:
the odds ratio for each trial and the lower and upper bounds of the confidence interval of the odds ratio for each trial.
the risk ratio for each trial and the lower and upper bounds of the confidence interval of the risk ratio for each trial.
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.
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.
the inverse variance and DerSimonian and Laird weights for each trial.
a vector containing Q the heterogeneity test statistic, df the degrees of freedom and its associated P-value.
the relative excess of the heterogeneity test statistic Q over the degrees of freedom df.
the percentage of total variation in study estimates that is due to heterogeneity rather than chance.
the variance of the treatment effect among trials.
a vector containing z the test statistic for overall treatment effect and its associated P-value.
observed number of events in the treatment group.
number in the treatment group.
observed number of events in the control group.
number in the control group.
character string identifying each trial.
a character string indicating the method to be used. Options are odds.ratio or risk.ratio.
a character string specifying the alternative hypothesis, must be one of two.sided, greater or less.
magnitude of the returned confidence interval. Must be a single number between 0 and 1.
alternative = "greater" tests the hypothesis that the DerSimonian and Laird summary measure of association is greater than 1.
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.
epi.iv, epi.mh, epi.smd
## 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)
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