| BA.est {MethComp} | R Documentation |
A variance component model is fitted to method comparison data with replicate measurements in each method by item stratum. The purpose is to simplify the construction of a correct Bland-Altman-plot.
BA.est( data, linked=TRUE, IxR=linked,
MxI=TRUE,
varMxI=FALSE, bias=TRUE, alpha=0.05 )
VC.est( data, linked=TRUE, IxR=linked,
MxI=TRUE,
varMxI=FALSE, bias=TRUE )
data |
A data frame representing method comparison data with replicate
measurements, i.e. with columns meth, item,
repl and y. |
linked |
Logical. Are the replicated linked within item across methods? |
IxR |
Logical. Should in item by repl interaction be included in
the model. This is needed when the replicates are linked within item
across methods, so it is just another name for the linked argument. |
MxI |
Logical. Should the method by item interaction be included in the model. |
varMxI |
Logical. Should the method by item interaction have a variance that varies between methods. Ignored if only two methods are compared. |
bias |
Logical. Should a systematic bias between methods be estimated?
If FALSE no bias between methods are assumed, i.e.
alpha_m=0, m=1,...,M. |
alpha |
Numerical. Significance level. By default the value 2 is used when computing prediction intervals, otherwise the 1-alpha/2 t-quantile is used. The number of d.f. is taken as the number of units minus the number of items minus the number of methods minus 1. |
The model fitted is:
y=alpha_m + mu_i + c_mi + a_ir + e_ir, var(c_mi)=tau_m^2, var(a_ir)=omega^2, var(e_mir)=sigma_m^2
We can only fit separate variances for the tau's if more than
two methods are compared (i.e. nM > 2).
NOTE: Currently, the author is too ignorant about the ins and outs of lme
to find out how to get different tau's when nM>2.
Therefore the estimates in this case are equal i.e. from the model postulated.
The function VC.est is the workhorse; BA.est calls it,
but it is also used as part of the fitting algorithm in AltReg.
A list with four elements:
Bias |
Vector of estimates of alpha_m, the first element is always 0. |
Var.comp |
Two-column matrix of sds and variances of the variance components,
nM tau's,
one nu if linked, and
nM sigma's.
Only those in the model specified are included. |
LoA |
Four-column matrix with mean difference, lover and upper limit of agreement and prediction SD. Each row in the matrix represents a pair of methods. |
Rep.coef |
Two-column matrix of repeatability SDs and repeatability coefficients. The SDs are the standard deviation of the difference between two measurements by the same method on the item inder identical circumstances; the repeatability coefficient the numerical extent of the prediction interval for this differnece. |
Bendix Carstensen
Carstensen, Simpson & Gurrin: Statistical models for assessing agreement in method comparison studies with replicate measurements, The International Journal of Biostatistics: Vol. 4 : Iss. 1, Article 16. http://www.bepress.com/ijb/vol4/iss1/16.
data( ox ) BA.est( ox ) BA.est( ox, linked=FALSE ) data( sbp ) BA.est( sbp ) BA.est( sbp, linked=FALSE )