berkey98 {mvmeta}

Five Published Trials on Periodontal Disease

Description

The dataset contains the results of 5 published trials comparing surgical and non-surgical treatments for medium-severity periodontal disease, one year after treatment. The 2 estimated outcomes are average improvements (surgical minus non-surgical, in mm) in probing depth (PD) and attachment level (AL).

Usage

berkey98

Format

A data frame with 5 observations on the following 7 variables:

pubyear

publication year of the trial.

npat

number of patients included in the trial.

PD

estimated improvement of surgical versus non-surgical treatments in probing depth (mm).

AL

estimated improvement of surgical versus non-surgical treatments in attachment level (mm).

var_PD

variance of the estimated outcome for PD.

cov_PD_AL

covariance of the estimated outcomes for PD and AL.

var_AL

variance of the estimated outcome for AL.

Row names specify the author of the paper reporting the results of each trial.

Source

Berkey CS, Hoaglin DC, et al. (1998). Meta-analysis of multiple outcomes by regression with random effects. Statistics in Medicine. 17:2537–2550.

Berkey C. S., Antczak-Bouckoms A., et al. (1995). Multiple-outcomes meta-analysis of treatments for periodontal disease. Journal of Dental Research. 74(4):1030–1039.

Sera F, Armstrong B, Blangiardo M, Gasparrini A (2019). An extended mixed-effects framework for meta-analysis.Statistics in Medicine. 2019;38(29):5429-5444. [Freely available here].

Gasparrini A, Armstrong B, Kenward MG (2012). Multivariate meta-analysis for non-linear and other multi-parameter associations. Statistics in Medicine. 31(29):3821–3839. [Freely available here].

Examples

### REPRODUCE THE RESULTS IN BERKEY ET AL. (1998)

# INSPECT THE DATA
berkey98

# FIXED-EFFECTS
year <- berkey98$pubyear - 1983
model <- mvmeta(cbind(PD,AL)~year,S=berkey98[5:7],data=berkey98,method="fixed")
print(summary(model),digits=3)

# GLS MODEL (VARIANCE COMPONENTS) 
model <- mvmeta(cbind(PD,AL)~year,S=berkey98[5:7],data=berkey98,method="vc",
  control=list(vc.adj=FALSE))
print(summary(model),digits=3)
round(model$Psi,3)

# ML MODEL
model <- mvmeta(cbind(PD,AL)~year,S=berkey98[5:7],data=berkey98,method="ml")
print(summary(model),digits=3)
round(model$Psi,3)