Package 'msmtools' reference manual

Title:	Building Augmented Data to Run Multi-State Models with 'msm' Package
Description:	A fast and general method for restructuring classical longitudinal data into augmented ones. The reason for this is to facilitate the modeling of longitudinal data under a multi-state framework using the 'msm' package.
Authors:	Francesco Grossetti [aut, cre]
Maintainer:	Francesco Grossetti <[email protected]>
License:	GPL-3
Version:	2.0.1
Built:	2024-10-28 05:21:44 UTC
Source:	https://github.com/contefranz/msmtools

A fast and general method for building augmented data

Description

A fast and general method for reshaping standard longitudinal data into a new structure called augmented'. This format is suitable under a multi-state framework using the msm package.

Usage

augment(
  data,
  data_key,
  n_events,
  pattern,
  state = list("IN", "OUT", "DEAD"),
  t_start,
  t_end,
  t_cens,
  t_death,
  t_augmented,
  more_status,
  check_NA = FALSE,
  convert = FALSE,
  verbose = TRUE
)
augment(
  data,
  data_key,
  n_events,
  pattern,
  state = list("IN", "OUT", "DEAD"),
  t_start,
  t_end,
  t_cens,
  t_death,
  t_augmented,
  more_status,
  check_NA = FALSE,
  convert = FALSE,
  verbose = TRUE
)

Arguments

`data`	A `data.table` or `data.frame` object in longitudinal format where each row represents an observation in which the exact starting and ending time of the process are known and recorded. If `data` is a `data.frame`, then `augment` internally casts it to a `data.table`.
`data_key`	A keying variable which `augment` uses to define a key for `data`. This represents the subject ID (see `setkey`).
`n_events`	An integer variable indicating the progressive (monotonic) event number of a given ID. `augment` always checks whether `n_events` is monotonic increasing within the provided `data_key` and stops the execution in case the check fails (see 'Details'). If missing, `augment` fastly creates a variable named `"n_events"`.
`pattern`	Either an integer, a factor or a character with 2 or 3 unique values which provides the ID status at the end of the study. `pattern` has a predefined structure. When 2 values are detected, they must be in the format: 0 = "alive", 1 = "dead". When 3 values are detected, then the format must be: 0 = "alive", 1 = "dead during a transition", 2 = "dead after a transition has ended" (see 'Details').
`state`	A list of three and exactly three possible states which a subject can reach. `state` has a predefined structure as follows: IN, OUT, DEAD (see 'Details').
`t_start`	The starting time of an observation. It can be passed as date, integer, or numeric format.
`t_end`	The ending time of an observation. It can be passed as date, integer, or numeric format.
`t_cens`	The censoring time of the study. This is the date until each ID is observed, if still active in the cohort.
`t_death`	The exact death time of a subject ID. If `t_death` is missing, `t_cens` is assumed to contain both censoring and death times and a warning is raised.
`t_augmented`	A variable indicating the name of the new time variable of the process in the augmented format. If `t_augmented` is missing, then the default name 'augmented' is assumed and the corresponding new variable is added to `data`. `t_augmented` is cast to integer or to numeric depending whether `t_start` is a date or a difftime, respectively. The suffix '_int' or '_num' is pasted to `t_augmented` and a new variable is computed accordingly. This is done because `msm` can't correctly deal with date or difftime variables. Both variables are positioned before `t_start`.
`more_status`	A variable which marks further transitions beside the default ones given by `state`. `more_status` can be a factor or a character (see 'Details'). If missing, `augment` ignores it.
`check_NA`	If `TRUE`, then arguments `data_key`, `n_events`, `pattern`, `t_start` and `t_end` are looked up for any missing data and if the function finds any, it stops with error. Default is `FALSE` because `augment` is not intended for running consistency checks, beside what is mandatory, and because the procedure is computationally onerous and could cause memory overhead for very large datasets. Argument `more_status` is the only one for which `augment` always checks for the presence of missing data and, again, if it finds any it just stops with error.
`convert`	If `TRUE`, then the returned object is automatically converted to the class `data.frame`. This is done in place and comes at very low cost both from running time and memory consumption (see `setDF`).
`verbose`	If `FALSE`, all information produced by `print`, `cat` and `message` are suppressed. Default is `TRUE`.

Details

In order to get the data processed, a monotonic increasing process needs to be ensured. In the first place, augment checks this both in case n_events is missing or not. The data are efficiently ordered through setkey function with data_key as the primary key and t_start as the secondary key. In the second place, it checks the monotonicity of n_events and if it fails, it stops with error and returns the subjects given by data_key for whom the condition is not met. If n_events is missing, then augment internally computes the progression number with the name n_events and runs the same procedure.

Attention needs to be payed to argument pattern. Integer values can be 0 and 1 if only two status are defined and they must correspond to the status 'alive' and 'dead'. If three values are defined, then they must be 0, 1 and 2 if pattern is an integer, or 'alive', 'dead inside a transition' and dead outside a transition' if pattern is either a character or a factor. The order matters: it is not possible to specify 0 as 'dead' for instance.

When passing a list of states, the order is important so that the first element must be the state corresponding to the starting time (i.e. 'IN', inside the hospital), the second element must correspond to the ending time (i.e. 'OUT', outside the hospital), and the third state is the absorbing state (i.e. 'DEAD').

more_status allows to manage multiple transitions beside what already specified in state. In particular, if the corresponding observation is a standard admission which adds no other information than what is inside state, then more_status must be set to 'df' which stands for 'Default' (see 'Examples' or run ?hosp and look at the variable 'rehab_it'). In general, it is always a good practice to fully specify the transition with a bunch of self-explanatory characters in order to quickly understand which is the current transition.

Value

An augmented format dataset of class data.table, or data.frame when convert is TRUE, where each row represents a specific transition for a given subject. augment returns them after some important variables have been computed:

`augmented`	The new timing variable for the process when looking at transitions. If `t_augmented` is missing, then `augment` creates augmented by default. augmented. The function looks directly to `t_start` and `t_end` to build it and thus it inherits their class. In particular, if `t_start` is a date format, then `augment` computes a new variable cast as integer and names it augmented_int. If `t_start` is a difftime format, then `augment` computes a new variable cast as a numeric and names it augmented_num.
`status`	A status flag which contains the states as specified in `state`. `augment` automatically checks whether argument `pattern` has 2 or 3 unique values and computes the correct structure of a given subject as reported in the vignette. The variable is cast as character.
`status_num`	The corresponding integer version of status.
`n_status`	A mix of `status` and `n_events` cast as character. This becomes useful when a multi-state model on the progression of the process needs to be implemented.

If more_status is passed, then augment computes some more variables. They mimic the meaning of status, status_num, and n_status but they account for the more complex structure defined. They are: status_exp, status_exp_num, and n_status_exp.

Author(s)

Francesco Grossetti [email protected].

References

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/.

M. Dowle, A. Srinivasan, T. Short, S. Lianoglou with contributions from R. Saporta and E. Antonyan (2016):
data.table: Extension of data.frame. R package version 1.9.6
URL https://github.com/Rdatatable/data.table/wiki

Examples

# loading data
data( hosp )

# 1.
# augmenting hosp
hosp_augmented = augment( data = hosp, data_key = subj, n_events = adm_number,
                          pattern = label_3, t_start = dateIN, t_end = dateOUT,
                          t_cens = dateCENS )

# 2.
# augmenting hosp by passing more information regarding transitions
# with argument more_status
hosp_augmented_more = augment( data = hosp, data_key = subj, n_events = adm_number,
                               pattern = label_3, t_start = dateIN, t_end = dateOUT,
                               t_cens = dateCENS, more_status = rehab_it )
# 3.
# augmenting hosp and returning a data.frame
hosp_augmented = augment( data = hosp, data_key = subj, n_events = adm_number,
                          pattern = label_3, t_start = dateIN, t_end = dateOUT,
                          t_cens = dateCENS, convert = TRUE )
class( hosp_augmented )

# loading data
data( hosp )

# 1.
# augmenting hosp
hosp_augmented = augment( data = hosp, data_key = subj, n_events = adm_number,
                          pattern = label_3, t_start = dateIN, t_end = dateOUT,
                          t_cens = dateCENS )

# 2.
# augmenting hosp by passing more information regarding transitions
# with argument more_status
hosp_augmented_more = augment( data = hosp, data_key = subj, n_events = adm_number,
                               pattern = label_3, t_start = dateIN, t_end = dateOUT,
                               t_cens = dateCENS, more_status = rehab_it )
# 3.
# augmenting hosp and returning a data.frame
hosp_augmented = augment( data = hosp, data_key = subj, n_events = adm_number,
                          pattern = label_3, t_start = dateIN, t_end = dateOUT,
                          t_cens = dateCENS, convert = TRUE )
class( hosp_augmented )

Synthetic Hospital Admissions

Description

A dataset containing synthetic hospital admissions in the classic longitudinal format. The dataset counts imaginary 10 patients who undergo different (re)admission into a hospital. Some demographic and clinical variables are also included.

Usage

hosp
hosp

Format

A data.table with 53 rows and 12 variables:

subj: Subject ID (integer)
adm_number: Hospital admissions counter (integer)
gender: Gender of patient (factor with 2 levels: "F" = females, "M" = males)
age: Age of patient in years at the given observation (integer)
rehab: Rehabilitation flag: if the admission has been in rehabilitation, then rehab = 1, else = 0 (integer)
it: Intensive Therapy flag: if the admission has been in intensive therapy, then it = 1, else = 0 (integer)
rehab_it: String which in one place marks the hospital admission types based on rehab and it. The standard admission is coded as "df" (default). If admission was in rehabilitation or in intensive therapy, rehab_it = "rehab" or "it", respectively (character)
label_2: Subject status at the end of the study. It takes 2 values: "alive" and "dead" (character)
label_3: Subject status at the end of the study. It takes 3 values: "alive" and "dead_in" and "dead_out" (character)
dateIN: Exact admission date (date)
dateOUT: Exact discharge date (date)
dateCENS: Either censoring time or exact death time (date)

Building augmented data for multi-state models: the `msmtools` package

Description

msmtools introduces a fast and general method for restructuring classical longitudinal datasets into augmented ones. Augmented data enhances longitudinal datasets and allow to model each transition under a multi-state framework. msmtools works in symbiosis with the msm package. It also provides two graphical goodness-of-fit tools to inspect the model performances using survival curves and prevalences under the Markov assumption. msmtools comes with 4 functions: augment, polish, prevplot, and survplot.

Remove observations with different states occurring at the same time

Description

Fast algorithm to get rid of transitions to different states occurring at the same exact time in an augmented data structure as computed by augment (see 'Details').

Usage

polish(
  data,
  data_key,
  pattern,
  time,
  check_NA = FALSE,
  convert = FALSE,
  verbose = TRUE
)
polish(
  data,
  data_key,
  pattern,
  time,
  check_NA = FALSE,
  convert = FALSE,
  verbose = TRUE
)

Arguments

`data`	A `data.table` or `data.frame` object in longitudinal format where each row represents an observation in which the exact starting and ending time of the process are known and recorded. If `data` is a `data.frame`, then `augment` internally casts it to a `data.table`.
`data_key`	A keying variable which `augment` uses to define a key for `data`. This represents the subject ID (see `setkey`).
`pattern`	Either an integer, a factor or a character with 2 or 3 unique values which provides the ID status at the end of the study. `pattern` has a predefined structure. When 2 values are detected, they must be in the format: 0 = "alive", 1 = "dead". When 3 values are detected, then the format must be: 0 = "alive", 1 = "dead during a transition", 2 = "dead after a transition has ended" (see 'Details').
`time`	The target time variable to check duplicates. By default it is set to 'augmented_int'.
`check_NA`	If `TRUE`, then arguments `data_key`, `pattern`, and `time` are looked up for any missing data and if the function finds any, it stops with error. Default is `FALSE`.
`convert`	If `TRUE`, then the returned object is automatically converted to the class `data.frame`. This is done in place and comes at very low cost both from running time and memory consumption (see `setDF`).
`verbose`	If `FALSE`, all information produced by `print`, `cat` and `message` are suppressed. Default is `TRUE`.

Details

The function finds all those cases where two subsequent events for a given subject land on different states but occur at the same time. When this happens, the whole subject, as identified by data_key, is removed from the data. The total number of subjects to be removed is printed out in order to be more informative.

Author(s)

Francesco Grossetti [email protected].

Examples


# loading data
data( hosp )

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

# cleaning any targeted occurrence
hosp_aug_clean = polish( data = hosp_aug, data_key = subj, pattern = label_3 )

# loading data
data( hosp )

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

# cleaning any targeted occurrence
hosp_aug_clean = polish( data = hosp_aug, data_key = subj, pattern = label_3 )

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)
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.

Author(s)

Francesco Grossetti [email protected].

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/.

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 )

## End(Not run)

## 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 )

## End(Not run)

Plot and get survival data from a multi-state model

Description

Plot the fitted survival probability computed over a msm model and compare it with the Kaplan-Meier. Fast build and return the underlying data structures.

Usage

survplot(
  x,
  from = 1,
  to = NULL,
  range = NULL,
  covariates = "mean",
  exacttimes = TRUE,
  times,
  grid = 100L,
  km = FALSE,
  out = c("none", "fitted", "km", "all"),
  ci = c("none", "normal", "bootstrap"),
  interp = c("start", "midpoint"),
  B = 100L,
  ci_km = c("none", "plain", "log", "log-log", "logit", "arcsin")
)
survplot(
  x,
  from = 1,
  to = NULL,
  range = NULL,
  covariates = "mean",
  exacttimes = TRUE,
  times,
  grid = 100L,
  km = FALSE,
  out = c("none", "fitted", "km", "all"),
  ci = c("none", "normal", "bootstrap"),
  interp = c("start", "midpoint"),
  B = 100L,
  ci_km = c("none", "plain", "log", "log-log", "logit", "arcsin")
)

Arguments

`x`	A `msm` object.
`from`	State from which to compute the estimated survival. Default to state 1.
`to`	The absorbing state to which compute the estimated survival. Default to the highest state found by `absorbing.msm`.
`range`	A numeric vector of two elements which gives the time range of the plot.
`covariates`	Covariate values for which to evaluate the expected probabilities. These can either be: the string `"mean"`, denoting the means of the covariates in the data (default), the number 0, indicating that all the covariates should be set to zero, or a list of values, with optional names. For example: `list (75, 1)` where the order of the list follows the order of the covariates originally given in the model formula, or a named list: `list (age = 75, gender = "M")`.
`exacttimes`	If `TRUE` (default) then transition times are known and exact. This is inherited from `msm` and should be set the same way.
`times`	An optional numeric vector giving the times at which to compute the fitted survival.
`grid`	An integer specifying the grid points at which to compute the fitted survival (see 'Details'). If `times` is passed, `grid` is ignored. Default to 100 points.
`km`	If `TRUE`, then the Kaplan-Meier curve is plotted. Default is `FALSE`.
`out`	A character vector specifying what the function has to return. Accepted values are `"none"` (default) to return just the plot, `"fitted"` to return the fitted survival curve only, `"km"` to return the Kaplan-Meier only, `"all"` to return all of the above.
`ci`	A character vector with the type of confidence intervals to compute for the fitted survival curve. Specify either `"none"` (default), for no confidence intervals, `"normal"` or `"bootstrap"`, for confidence intervals computed with the respective method in `pmatrix.msm`. This is very computationally-intensive, since intervals must be computed at a series of times.
`interp`	If `"start"` (default), then the entry time into the absorbing state is assumed to be the time it is first observed in the data. If `"midpoint"`, then the entry time into the absorbing state is assumed to be halfway between the time it is first observed and the previous observation time. This is generally more reasonable for "progressive" models with observations at arbitrary times.
`B`	Number of bootstrap or normal replicates for the confidence interval. The default is 100 rather than the usual 1000, since these plots are for rough diagnostic purposes.
`ci_km`	A character vector with the type of confidence intervals to compute for the Kaplan-Meier curve. Specify either `"none"`, `"plain"`, `"log"`, `"log-log"`, `"logit"`, or `"arcsin"`, as coded in `survfit`.

Details

The function is a wrapper of plot.survfit.msm and does more things. survplot manages correctly the plot of a fitted survival in an exact times framework (when exacttimes = TRUE) by just resetting the time scale and looking at the follow-up time. It can quickly build and return to the user the data structures used to compute the Kaplan-Meier and the fitted survival probability by specifying out = "all".

The user can defined custom times (through times) or let survplot choose them on its own (through grid). In the latter case, survplot looks for the follow-up time and divides it by grid. The higher it is, the finer the grid will be so that computing the fitted survival will take longer, but will be more precise.

Value

When out = "none", a gg/ggplot object is returned. If out is anything else, then a named list is returned. The Kaplan-Meier data can be accessed with $km while the estimated survival data with $fitted. If out = "all", the plot, the Kaplan-Meier and the estimated curve are returned.

Author(s)

Francesco Grossetti [email protected].

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.

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/.

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 ) )

# plotting the fitted and empirical survival from state = 1
theplot = survplot( x = msm_model, km = TRUE )

# plotting the fitted and empirical survival from state = 2 and
and returning both the fitted and the empirical curve
out_all = survplot( msm_model, from = 2, km = TRUE, out = "all" )


## End(Not run)

## 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 ) )

# plotting the fitted and empirical survival from state = 1
theplot = survplot( x = msm_model, km = TRUE )

# plotting the fitted and empirical survival from state = 2 and
and returning both the fitted and the empirical curve
out_all = survplot( msm_model, from = 2, km = TRUE, out = "all" )


## End(Not run)

Package 'msmtools'

Help Index

A fast and general method for building augmented data

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Synthetic Hospital Admissions

Description

Usage

Format

Building augmented data for multi-state models: the msmtools package

Description

Remove observations with different states occurring at the same time

Description

Usage

Arguments

Details

Author(s)

See Also

Examples

Plot observed and expected prevalences for a multi-state model

Description

Usage

Arguments

Details

Author(s)

References

See Also

Examples

Plot and get survival data from a multi-state model

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Building augmented data for multi-state models: the `msmtools` package