Hypothesis testing on a scalar fixed-effect component in meta-analysis and meta-regression models

Description

Performs hypothesis testing on a scalar component of the fixed-effects vector in meta-analysis and meta-regression models, using the signed profile log-likelihood ratio test and its higher-order Skovgaard's adjustment (Skovgaard, 1996), as described in Guolo (2012). See Guolo and Varin (2012) for illustrative examples about the usage of metaLik package.

Usage

test.metaLik(object, param=1, value=0, alternative=c("two.sided", "less", "greater"), 
print=TRUE)

Arguments

Details

test.metaLik allows hypothesis testing on a scalar component of interest in the fixed-effects vector. The signed profile log-likelihood ratio statistic for inference on scalar component β of θ is

r(β) = sign(hat{β}-β)√{2 {l(hat{θ})-l(θ)} },

where l is the log-likelihood function and hat{θ} is the maximum likelihood estimate of θ. The Skovgaard's adjustment is defined as

overline r(β) = r(β) + frac{1}{r(β)}logfrac{u(β)}{r(β)},

where u(β) is a correction term involving the observed and the expected information matrix and covariances of likelihood quantities, as described in Guolo (2012). Skovgaard's statistic has a second-order accuracy in approximating the standard normal distribution. In the rare case of equal within-study variances, Skovgaard's statistic reaches third-order accuracy.

Value

A list with the following components:

Author(s)

Annamaria Guolo and Cristiano Varin.

References

Guolo, A. (2012). Higher-Order Likelihood Inference in Meta-Analysis and Meta-Regression. Statistics in Medicine 31, 313–327.

Guolo, A. and Varin, C. (2012). The R Package metaLik for Likelihood Inference in Meta-Analysis. Journal of Statistical Software 50 (7), 1–14. http://www.jstatsoft.org/v50/i07/.

Skovgaard, I. M. (1996). An Explicit Large-Deviation Approximation to One-Parameter Tests. Bernoulli 2, 145–165.

See Also

Function metaLik for fitting meta-analysis and meta-regression models. Function summary.metaLik for summaries.

Examples

data(vaccine)
m <- metaLik(y~latitude, data=vaccine, sigma2=sigma2)
## significance test for the intercept coefficient
test.metaLik(m)
## significance test for the 'latitude' coefficient
test.metaLik(m, param=2)
## testing for the 'latitude' coefficient less than 0
test.metaLik(m, param=2, value=0, alternative='less')

Results