Description Usage Arguments Details Value Author(s) References See Also Examples
The function fits the model of Holling et al. (2012). The adjusted profile maximum likelihood estimator (APMLE) is implemented for homogeneity and heterogeneity of primary studies.
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data |
any object that can be converted to a data frame with integer variables |
subset |
the rows of |
TP |
character or integer: name for vector of integers that is a variable of |
FN |
character or integer: name for vector of integers that is a variable of |
FP |
character or integer: name for vector of integers that is a variable of |
TN |
character or integer: name for vector of integers that is a variable of |
correction |
numeric, continuity correction applied if zero cells |
correction.control |
character, if set to |
hetero |
logical, should heterogeneity of studies be assumed? Will fit model for homogeneity otherwise. |
estimator |
character, determines estimator used. Currently only |
l |
interger, number of iterations for fixed point algorithm |
... |
arguments passed on to other functions (currently not used) |
The model of Holling et al. (2012) assumes that the relationship between false positive rates u and and sensitivities p can be described by
u^θ = p,
where θ is the diagnostic accuracy parameter. If homogeneity of the studies can be assumed, θ is estimated as a fixed effect. Under heterogeneity a random effect with variance τ^2 describes the variation of the diagnostic accuracy parameter in the population of studies. Since the error of each observed θ depends only on the sample size and θ the model has only one parameter in the case of homogeneity and two parameters under heterogeneity, making it suitable for diagnostic meta-analysis with low sample size. Estimation proceeds by a fixed point algorithm derived from the adjusted profile likelihood. More details on the computational approach can be found in Holling et al. (2012).
An object of the class phm
for which many standard methods are available. See phm-class
for details.
Philipp Doebler <philipp.doebler@googlemail.com>, Walailuck Boehning (original implementation of estimation algorithm)
Holling, H., Boehning W., Boehning, D. (2012) “Meta-Analysis of Diagnostic Studies based upon SROC-Curves: a Mixed Model Approach using a Proportional Hazards Model.” Statistical Modelling, 12, 347???-375.
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Loading required package: mvtnorm
Loading required package: ellipse
Attaching package: 'ellipse'
The following object is masked from 'package:graphics':
pairs
Loading required package: mvmeta
This is mvmeta 0.4.11. For an overview type: help('mvmeta-package').
Call:
phm.default(data = AuditC)
Coefficients:
theta taus_sq
0.084631351 0.003706143
Call:
phm.default(data = AuditC)
Estimate 2.5 % 97.5 %
theta 0.084631351 0.047449859 0.121812844
taus_sq 0.003706143 -0.001277798 0.008690085
Log-likelihood: 31.121 on 2 degrees of freedom
AIC: -58.2
BIC: -57
Chi-square goodness of fit test (Adjusted Profile Maximum Likelihood
under heterogeneity)
data: x
Chi-square = 13.726, df = 2, p-value = 0.3185
AUC 2.5 % 97.5 % pAUC 2.5 % 97.5 %
0.922 0.955 0.891 0.891 0.937 0.848
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