msmtools (version 2.0.0)

prevplot: Plot observed and expected prevalences for a multi-state model

Description

Provides a graphical indication of goodness of fit of a multi-state model computed by msm using observed and expected prevalences. It also computes a rough indicator of where the data depart from the estimated Markov model.

Usage

prevplot(x, prev.obj, exacttimes = TRUE, M = FALSE, ci = FALSE)

Arguments

x

A msm object.

prev.obj

A list computed by prevalence.msm. It can be with or without confidence intervals. prevplot will behaves accordingly.

exacttimes

If TRUE (default) then transition times are known and exact. This is inherited from msm and should be set the same way.

M

If TRUE, then a rough indicator of deviance from the model is computed (see 'Details'). Default is FALSE.

ci

If TRUE, then confidence intervals, if they exist, are plotted. Default is FALSE.

Details

When M = TRUE, a rough indicator of the deviance from the Markov model is computed according to Titman and Sharples (2008). A comparison at a given time \(t_i\) of a patient k in the state s between observed counts \(O_{is}\) with expected ones \(E_{is}\) is build as follows: $$M_{is} = \frac{(O_{is} - E_{is})^2}{E_{is}}$$

The plot of the deviance M is returned together with the standard prevalence plot in the second row. This is not editable by the user.

References

Titman, A. and Sharples, L.D. (2010). Model diagnostics for multi-state models, Statistical Methods in Medical Research, 19, 621-651.

Titman, A. and Sharples, L.D. (2008). A general goodness-of-fit test for Markov and hidden Markov models, Statistics in Medicine, 27, 2177-2195.

Gentleman RC, Lawless JF, Lindsey JC, Yan P. (1994). Multi-state Markov models for analysing incomplete disease data with illustrations for HIV disease. Statistics in Medicine, 13:805-821.

Jackson, C.H. (2011). Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software, 38(8), 1-29. URL https://www.jstatsoft.org/v38/i08/.

See Also

plot.prevalence.msm msm prevalence.msm

Examples

# NOT RUN {
data( hosp )

# augmenting the data
hosp_augmented = augment( data = hosp, data_key = subj, n_events = adm_number,
                          pattern = label_3, t_start = dateIN, t_end = dateOUT,
                          t_cens = dateCENS )

# let's define the initial transition matrix for our model
Qmat = matrix( data = 0, nrow = 3, ncol = 3, byrow = TRUE )
Qmat[ 1, 1:3 ] = 1
Qmat[ 2, 1:3 ] = 1
colnames( Qmat ) = c( 'IN', 'OUT', 'DEAD' )
rownames( Qmat ) = c( 'IN', 'OUT', 'DEAD' )

# attaching the msm package and running the model using
# gender and age as covariates
library( msm )
msm_model = msm( status_num ~ augmented_int, subject = subj,
                 data = hosp_augmented, covariates = ~ gender + age,
                 exacttimes = TRUE, gen.inits = TRUE, qmatrix = Qmat,
                 method = 'BFGS', control = list( fnscale = 6e+05, trace = 0,
                 REPORT = 1, maxit = 10000 ) )

# defining the times at which compute the prevalences
t_min = min( hosp_augmented$augmented_int )
t_max = max( hosp_augmented$augmented_int )
steps = 100L

# computing prevalences
prev = prevalence.msm( msm_model, covariates = 'mean', ci = 'normal',
                       times = seq( t_min, t_max, steps ) )

# and plotting them using prevplot()
gof = prevplot( x = msm_model, prev.obj = prev, ci = TRUE, M = TRUE )
# }
# NOT RUN {
# }