rmeta {robustmeta}

Robust estimation for meta-analysis with influential outlying studies

Description

Implementing the robust inference for meta-analysis involving influential outlying studies based on the density power divergence.

Usage

rmeta(y, v, model="RE", gamma=0.01)

Arguments

y

A vector of the outcome measure estimates (e.g., MD, SMD, log OR, log RR, log HR, RD)

v

A vector of the variance estimate of y

model

Type of the pooling model; "FE": Fixed-effect model or "RE": Random-effects model; Default is "RE"

gamma

Unit of grid search to explore the optimal value of tuning parameter alpha on (0,1); Default is 0.01

Value

Results of the robust inference for meta-analysis.

• mu: Estimate of the common effect (for the fixed-effect model) or the grand mean (for the random-effects model).

• se: Standard error estimate of mu.

• CI: 95 percent confidence interval of mu.

• P: P-value of the hypothesis test of mu=0.

• alpha: Selected alpha by the Hyvarinen score.

• W: Contribution rates of individual studies (ui: contribution rates of the conventional methods, wi: contribution rates of the robust methods).

References

Noma, H., Sugasawa, S. and Furukawa, T. A. (2022). Robust inference methods for meta-analysis involving influential outlying studies. In Preparation.

Basu, A., Harris, I. R., Hjort, N. L., Jones, M. C. (1998). Robust and efficient estimation by minimizing a density power divergence. Biometrika. 85: 549-559.

Sugasawa, S. and Yonekura, S. (2021). On selection criteria for the tuning parameter in robust divergence. Entropy. 23: 1147.

Examples

require(metafor)
data(clbp)
edat1 <- escalc(measure="SMD",m1i=m1,m2i=m2,sd1i=s1,sd2i=s2,n1i=n1,n2i=n2,data=clbp)
DL1 <- rma(yi, vi, data=edat1, method="DL")
print(DL1)         # ordinary DerSimonian-Laird method
plot(DL1)   # plots of influential statistics, etc.

###

y <- as.numeric(edat1$yi)		# definition of summary statistics
v <- edat1$vi

rmeta(y,v)                 # robust inference based on the random-effects model
rmeta(y,v,model="FE")      # robust inference based on the fixed-effect model