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The Fibrinogen Studies Collaboration is a meta-analysis of individual data on 154,012 adults from 31 prospective cohort studies with information on plasma fibrinogen and major disease outcomes. The dataset reports a subset of the results of a first-stage analysis consisting of the log-hazard ratio of coronary heart disease for categories of levels of fibrinogen versus a baseline category.
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A data frame with 31 observations on the following 15 variables:
cohort
: study ID.
b2, b3, b4, b5
: estimated log-hazard ratios for the second to fifth categories versus the baseline category.
V_2_2, V_3_3, V_4_4, V_5_5
: variances of the estimated log-hazard ratios.
V_2_3, V_2_4, V_2_5, V_3_4, V_3_5, V_4_5
: covariances of the estimated log-hazard ratios.
The published analysis adopted a fixed-effects model on 10 categories of fibrinogen (Fibrinogen Studies Collaboration 2004, 2005). Here a subset of the results of the first-stage analysis is reported, namely the log-hazard ratio for 4 categories and associated (co)variance terms, ordered as the lower triangular elements of the (co)variance matrix taken by column. Details on the first-stage model and the second-stage meta-analysis are provided in White (2009) and Jackson and colleagues (2010).
The data provide an example of application of multivariate meta-analysis for multi-parameter association, where a relationship is defined by functions specified by several coefficients. In this case, the coefficients refer to log-hazard ratio for strata of the original variable versus a baseline category. A general overview of the application of multivariate meta-analysis in this setting is provided by Gasparrini and colleagues (2012).
Fibrinogen Studies Collaboration (2004). Collaborative meta-analysis of prospective studies of plasma fibrinogen and cardiovascular disease. European Journal of Cardiovascular Prevention and Rehabilitation. 11:9–17.
Fibrinogen Studies Collaboration (2005). Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis. Journal of the American Medical Association. 294:1799–1809.
White IR (2009). Multivariate random-effects meta-analysis. Stata Journal. 9(1):40–56.
Jackson D, White IR, Thompson SG (2010). Extending DerSimonian and Laird's methodology to perform multivariate random effects meta-analyses. Statistics in Medicine. 29(12):1282–1297.
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].
1 2 3 4 5 6 7 8 9 10 11 12 13 | ### REPRODUCE THE RESULTS IN WHITE (2009) AND JACKSON ET AL. (2010)
# INSPECT THE DATA
head(fibrinogen)
# REML MODEL
y <- as.matrix(fibrinogen[2:5])
S <- as.matrix(fibrinogen[6:15])
model <- mixmeta(y, S)
# SUMMARIZE THE RESULTS
print(summary(model), digits=3)
round(model$Psi, 3)
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