epiR (version 0.9-22)

epi.dsl: 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", 
   conf.level = 0.95)

Arguments

ev.trt

observed number of events in the treatment group.

n.trt

number in the treatment group.

ev.ctrl

observed number of events in the control group.

n.ctrl

number in the control group.

names

character string identifying each trial.

method

a character string indicating the method to be used. Options are odds.ratio or risk.ratio.

conf.level

magnitude of the returned confidence interval. Must be a single number between 0 and 1.

Value

• A list containing the following:
• OR
  the odds ratio for each trial, the standard error of the odds ratio for each trial, and the lower and upper bounds of the confidence interval of the odds ratio for each trial.
• RR
  the risk ratio for each trial, the standard error of the risk ratio for each trial, and the lower and upper bounds of the confidence interval of the risk ratio for each trial.
• OR.summary
  the DerSimonian and Laird summary odds ratio, the standard error of the DerSimonian and Laird summary odds ratio, the lower and upper bounds of the confidence interval of the DerSimonian and Laird summary odds ratio.
• RR.summary
  the DerSimonian and Laird summary risk ratio, the standard error of the DerSimonian and Laird summary risk ratio, the lower and upper bounds of the confidence interval of the DerSimonian and Laird summary risk ratio.
• weights
  the inverse variance and DerSimonian and Laird weights for each trial.
• heterogeneity
  a vector containing Q the heterogeneity test statistic, df the degrees of freedom and its associated P-value.
• Hsq
  the relative excess of the heterogeneity test statistic Q over the degrees of freedom df.
• Isq
  the percentage of total variation in study estimates that is due to heterogeneity rather than chance.
• tau.sq
  the variance of the treatment effect among trials.
• effect
  a vector containing 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.

See Also

epi.iv, epi.mh, epi.smd

Examples

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", 
   conf.level = 0.95)