R: Criteria for comparing two multi-state models with nested...
draic.msm
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Criteria for comparing two multi-state models with nested state spaces
Description
A modification of Akaike's information criterion, and a leave-one-out likelihood
cross-validation criterion, for comparing the predictive ability of two
Markov multi-state models with nested state spaces. This is evaluated based
on the restricted or aggregated data which the models have in common.
Note that standard AIC can be computed for one or more fitted
msm models x,y,... using AIC(x,y,...), and
this can be used to compare models fitted to the same
data. draic.msm and drlcv.msm are designed for models
fitted to data with differently-aggregated state spaces.
The two models must both be non-hidden Markov models without
censored states.
The two models must be fitted to the same datasets, except that the
state space of the coarse model must be an aggregated version of the
state space of the full model. That is, every state in the full
dataset must correspond to a unique state in the coarse dataset.
For example, for the full state variable c(1,1,2,2,3,4), the
corresponding coarse states could be c(1,1,2,2,2,3), but not
c(1,2,3,4,4,4).
The structure of allowed transitions in the coarse model must also
be a collapsed version of the big model structure, but no check is
currently made for this in the code.
To use these functions, all objects
which were used in the calls to fit msm.full and
msm.coarse must be in the working environment, for example,
datasets and definitions of transition matrices.
likelihood.only
Don't calculate Hessians and trace term (DRAIC).
information
Use observed or expected information in the DRAIC
trace term. Expected is the default, and much faster, though is only
available for models fitted to pure panel data (all obstype=1
in the call to msm, thus not exact transition times or
exact death times)
tl
Width of symmetric tracking interval, by default 0.95 for a
95% interval.
cores
Number of processor cores to use in drlcv for
cross-validation by parallel processing. Requires the
doParallel package to be installed. If not specified,
parallel processing is not used. If cores is set to the
string "default", the default methods of
makeCluster (on Windows) or
registerDoParallel (on Unix-like) are
used.
verbose
Print intermediate results of each iteration of
cross-validation to the console while running. May not work with
parallel processing.
outfile
Output file to print intermediate results of
cross-validation. Useful to track execution speed when using
parallel processing, where output to the console may not work.
Details
The difference in restricted AIC (Liquet and Commenges, 2011), as
computed by this function, is defined as
where gamma and theta are the maximum likelihood estimates of the smaller and bigger models, fitted to the smaller and bigger data, respectively.
l(gamma_n |x'') represents the likelihood of the simpler model evaluated on the restricted data.
l(theta_n |x'') represents the likelihood of
the complex model evaluated on the restricted data. This is a hidden
Markov model, with a misclassification matrix and initial state
occupancy probabilities as described by Thom et al (2014).
J() are the corresponding (expected or observed, as specified by the user) information matrices.
x is the expanded data, to which the bigger model was originally fitted, and
x' is the data to which the smaller model was originally fitted.
x'' is the restricted data which the two models
have in common. x'' = x in this implementation, so the models are nested.
The difference in likelihood cross-validatory criteria (Liquet and Commenges, 2011) is defined as
where γ_{-i} and θ_{-i} are the maximum likelihood estimates from the smaller and bigger models fitted to datasets with subject i left out, g() and h() are the densities of the corresponding models, and x_i'' is the restricted data from subject i.
Tracking intervals are analogous to confidence intervals, but not strictly the same, since the quantity which D_RAIC aims to estimate, the difference in expected Kullback-Leibler discrepancy for predicting a replicate dataset, depends on the sample size. See the references.
Positive values for these criteria indicate the coarse model is
preferred, while negative values indicate the full model is preferred.
Value
A list containing D_RAIC (draic.msm) or
D_RLCV (drlcv.msm), its component terms, and tracking intervals.
Thom, H. and Jackson, C. and Commenges, D. and Sharples, L. (2015)
State selection in multistate models with application to quality of
life in psoriatic arthritis. Statistics In Medicine 34(16) 2381 -
2480.
Liquet, B. and Commenges D. (2011) Choice of estimators based on
different observations: Modified AIC and LCV criteria. Scandinavian
Journal of Statistics; 38:268-287.