Create a list containing predicted transition probabilities at discrete times.
Since the transition probabilities have presumably already been predicted
based on covariate values, no input data is required for
simulation. The class can be instantiated from either an array,
a data.table, a data.frame, or a tpmatrix. This is the object in
hesim used to specify the transition probabilities required to simulate
Markov chains with the CohortDtstmTrans class.
tparams_transprobs(object, ...)
# S3 method for class 'array'
tparams_transprobs(
object,
tpmatrix_id = NULL,
times = NULL,
grp_id = NULL,
patient_wt = NULL,
...
)
# S3 method for class 'data.table'
tparams_transprobs(object, ...)
# S3 method for class 'data.frame'
tparams_transprobs(object, ...)
# S3 method for class 'tpmatrix'
tparams_transprobs(object, tpmatrix_id, ...)An object of the appropriate class.
Further arguments passed to or from other methods. Currently unused.
An object of class tpmatrix_id (or an equivalent
data.table with the same ID columns as returned by tpmatrix_id()).
An optional numeric vector of distinct times to pass to time_intervals representing time intervals indexed by the 4th dimension of the array. May either be the start or the end of intervals. This argument is not required if there is only one time interval.
An optional numeric vector of integers denoting the subgroups. Must be the same length as the 3rd dimension of the array.
An optional numeric vector denoting the weight to apply to each patient within a subgroup. Must be the same length as the 3rd dimension of the array.
An object of class tparams_transprobs,
which is a list containing value and relevant ID attributes. The element
value is an array of predicted transition probability matrices from the
probability distribution of the underlying statistical model. Each matrix in
value is a prediction for a sample, strategy_id, patient_id, and
optionally time_id combination.
The format of object depends on its class:
Either a 3D or a 6D array is possible.
If a 3D array, then each slice is a
square transition probability matrix. In this case
tpmatrix_id is required and each matrix slice corresponds to the same
numbered row in tpmatrix_id. The number of matrix slices must equal the number
of rows in tpmatrix_id.
If a 6D array, then the dimensions of the array should be indexed as follows:
1st (sample), 2nd (strategy_id), 3rd (patient_id),
4th (time_id), 5th (rows of transition matrix), and
6th (columns of transition matrix). In other words, an index of
[s, k, i, t] represents the transition matrix for the sth
sample, kth treatment strategy, ith patient, and tth
time interval.
Must contain the following:
ID columns for the parameter sample (sample),
treatment strategy (strategy_id), and patient (patient_id).
If the number of time intervals is greater than 1 it must also contain the
column time_start denoting the starting time of a time interval. A column
patient_wt may also be used to denote the weight to apply to each
patient.
Columns for each element of the transition probability matrix.
They should be prefixed with "prob_" and ordered rowwise.
For example, the following columns would be used for a 2x2 transition
probability matrix:
prob_1 (1st row, 1st column),
prob_2 (1st row, 2nd column),
prob_3 (2nd row, 1st column), and
prob_4 (2nd row, 2nd column).
Same as data.table.
An object of class tpmatrix.
A tparams_transprobs object is also instantiated when creating a
cohort discrete time state transition model using define_model().
A tparams_transprobs object is used to store the "parameters" of
the transition component of a cohort discrete time state transition
model (cDTSTM). You can create such an object with CohortDtstmTran$new().
tpmatrix() and tpmatrix_id() provide a convenient way to construct a
tparams_transprobs object in a flexible way. define_model() is, in turn,
a convenient way to construct a tpmatrix object using mathematical
expressions; in this case, an entire cDTSTM can be instantiated from a model
definition using create_CohortDtstm.model_def(). Detailed examples
are provided in vignette("markov-cohort") and
vignette("markov-inhomogeneous-cohort")
The output of a tparams_transprobs object is rather verbose. It can be
helpful to check the output by converting it to a data.table (containing
both the ID variables and flattened transition probability matrices)
with as.data.table.tparams_transprobs(). Transition probabilities can
also be summarized (across parameter samples) using
summary.tparams_transprobs().
hesim_dat <- hesim_data(strategies = data.frame(strategy_id = 1:2),
patients = data.frame(patient_id = 1:3))
input_data <- expand(hesim_dat, by = c("strategies", "patients"))
# tpmatrix objects provide a convenient way to construct
# tparams_transprobs() objects
tpmat_id <- tpmatrix_id(input_data, n_samples = 2)
p_12 <- runif(nrow(tpmat_id), .6, .7) +
.05 * (tpmat_id$strategy_id == 2)
tpmat <- tpmatrix(
C, p_12,
0, 1
)
tprobs <- tparams_transprobs(tpmat, tpmat_id)
names(tprobs) # Names of list elements
#> [1] "value" "strategy_id" "n_strategies" "patient_id"
#> [5] "n_patients" "time_id" "time_intervals" "n_times"
#> [9] "sample" "n_samples" "grp_id"
# Convert to data.table in wide format
as.data.table(tprobs)
#> sample strategy_id patient_id time_id time_start time_stop prob_s1_s1
#> <int> <int> <int> <num> <num> <num> <num>
#> 1: 1 1 1 1 0 Inf 0.3573288
#> 2: 1 1 2 1 0 Inf 0.3976430
#> 3: 1 1 3 1 0 Inf 0.3899896
#> 4: 1 2 1 1 0 Inf 0.2951765
#> 5: 1 2 2 1 0 Inf 0.2647194
#> 6: 1 2 3 1 0 Inf 0.3103232
#> 7: 2 1 1 1 0 Inf 0.3301205
#> 8: 2 1 2 1 0 Inf 0.3916309
#> 9: 2 1 3 1 0 Inf 0.3103174
#> 10: 2 2 1 1 0 Inf 0.2870937
#> 11: 2 2 2 1 0 Inf 0.2700455
#> 12: 2 2 3 1 0 Inf 0.2631438
#> prob_s1_s2 prob_s2_s1 prob_s2_s2
#> <num> <num> <num>
#> 1: 0.6426712 0 1
#> 2: 0.6023570 0 1
#> 3: 0.6100104 0 1
#> 4: 0.7048235 0 1
#> 5: 0.7352806 0 1
#> 6: 0.6896768 0 1
#> 7: 0.6698795 0 1
#> 8: 0.6083691 0 1
#> 9: 0.6896826 0 1
#> 10: 0.7129063 0 1
#> 11: 0.7299545 0 1
#> 12: 0.7368562 0 1
# Convert to data.table in long format
as.data.table(tprobs, long = TRUE)
#> sample strategy_id patient_id time_id time_start time_stop from to
#> <int> <int> <int> <num> <num> <num> <char> <char>
#> 1: 1 1 1 1 0 Inf s1 s1
#> 2: 1 1 1 1 0 Inf s1 s2
#> 3: 1 1 1 1 0 Inf s2 s1
#> 4: 1 1 1 1 0 Inf s2 s2
#> 5: 1 1 2 1 0 Inf s1 s1
#> 6: 1 1 2 1 0 Inf s1 s2
#> 7: 1 1 2 1 0 Inf s2 s1
#> 8: 1 1 2 1 0 Inf s2 s2
#> 9: 1 1 3 1 0 Inf s1 s1
#> 10: 1 1 3 1 0 Inf s1 s2
#> 11: 1 1 3 1 0 Inf s2 s1
#> 12: 1 1 3 1 0 Inf s2 s2
#> 13: 1 2 1 1 0 Inf s1 s1
#> 14: 1 2 1 1 0 Inf s1 s2
#> 15: 1 2 1 1 0 Inf s2 s1
#> 16: 1 2 1 1 0 Inf s2 s2
#> 17: 1 2 2 1 0 Inf s1 s1
#> 18: 1 2 2 1 0 Inf s1 s2
#> 19: 1 2 2 1 0 Inf s2 s1
#> 20: 1 2 2 1 0 Inf s2 s2
#> 21: 1 2 3 1 0 Inf s1 s1
#> 22: 1 2 3 1 0 Inf s1 s2
#> 23: 1 2 3 1 0 Inf s2 s1
#> 24: 1 2 3 1 0 Inf s2 s2
#> 25: 2 1 1 1 0 Inf s1 s1
#> 26: 2 1 1 1 0 Inf s1 s2
#> 27: 2 1 1 1 0 Inf s2 s1
#> 28: 2 1 1 1 0 Inf s2 s2
#> 29: 2 1 2 1 0 Inf s1 s1
#> 30: 2 1 2 1 0 Inf s1 s2
#> 31: 2 1 2 1 0 Inf s2 s1
#> 32: 2 1 2 1 0 Inf s2 s2
#> 33: 2 1 3 1 0 Inf s1 s1
#> 34: 2 1 3 1 0 Inf s1 s2
#> 35: 2 1 3 1 0 Inf s2 s1
#> 36: 2 1 3 1 0 Inf s2 s2
#> 37: 2 2 1 1 0 Inf s1 s1
#> 38: 2 2 1 1 0 Inf s1 s2
#> 39: 2 2 1 1 0 Inf s2 s1
#> 40: 2 2 1 1 0 Inf s2 s2
#> 41: 2 2 2 1 0 Inf s1 s1
#> 42: 2 2 2 1 0 Inf s1 s2
#> 43: 2 2 2 1 0 Inf s2 s1
#> 44: 2 2 2 1 0 Inf s2 s2
#> 45: 2 2 3 1 0 Inf s1 s1
#> 46: 2 2 3 1 0 Inf s1 s2
#> 47: 2 2 3 1 0 Inf s2 s1
#> 48: 2 2 3 1 0 Inf s2 s2
#> sample strategy_id patient_id time_id time_start time_stop from to
#> prob
#> <num>
#> 1: 0.3573288
#> 2: 0.6426712
#> 3: 0.0000000
#> 4: 1.0000000
#> 5: 0.3976430
#> 6: 0.6023570
#> 7: 0.0000000
#> 8: 1.0000000
#> 9: 0.3899896
#> 10: 0.6100104
#> 11: 0.0000000
#> 12: 1.0000000
#> 13: 0.2951765
#> 14: 0.7048235
#> 15: 0.0000000
#> 16: 1.0000000
#> 17: 0.2647194
#> 18: 0.7352806
#> 19: 0.0000000
#> 20: 1.0000000
#> 21: 0.3103232
#> 22: 0.6896768
#> 23: 0.0000000
#> 24: 1.0000000
#> 25: 0.3301205
#> 26: 0.6698795
#> 27: 0.0000000
#> 28: 1.0000000
#> 29: 0.3916309
#> 30: 0.6083691
#> 31: 0.0000000
#> 32: 1.0000000
#> 33: 0.3103174
#> 34: 0.6896826
#> 35: 0.0000000
#> 36: 1.0000000
#> 37: 0.2870937
#> 38: 0.7129063
#> 39: 0.0000000
#> 40: 1.0000000
#> 41: 0.2700455
#> 42: 0.7299545
#> 43: 0.0000000
#> 44: 1.0000000
#> 45: 0.2631438
#> 46: 0.7368562
#> 47: 0.0000000
#> 48: 1.0000000
#> prob
# Summary where each column is a vector
summary(tprobs)
#> strategy_id patient_id time_id time_start time_stop from to mean
#> <int> <int> <num> <num> <num> <char> <char> <num>
#> 1: 1 1 1 0 Inf s1 s1 0.3437247
#> 2: 1 1 1 0 Inf s1 s2 0.6562753
#> 3: 1 1 1 0 Inf s2 s1 0.0000000
#> 4: 1 1 1 0 Inf s2 s2 1.0000000
#> 5: 1 2 1 0 Inf s1 s1 0.3946369
#> 6: 1 2 1 0 Inf s1 s2 0.6053631
#> 7: 1 2 1 0 Inf s2 s1 0.0000000
#> 8: 1 2 1 0 Inf s2 s2 1.0000000
#> 9: 1 3 1 0 Inf s1 s1 0.3501535
#> 10: 1 3 1 0 Inf s1 s2 0.6498465
#> 11: 1 3 1 0 Inf s2 s1 0.0000000
#> 12: 1 3 1 0 Inf s2 s2 1.0000000
#> 13: 2 1 1 0 Inf s1 s1 0.2911351
#> 14: 2 1 1 0 Inf s1 s2 0.7088649
#> 15: 2 1 1 0 Inf s2 s1 0.0000000
#> 16: 2 1 1 0 Inf s2 s2 1.0000000
#> 17: 2 2 1 0 Inf s1 s1 0.2673824
#> 18: 2 2 1 0 Inf s1 s2 0.7326176
#> 19: 2 2 1 0 Inf s2 s1 0.0000000
#> 20: 2 2 1 0 Inf s2 s2 1.0000000
#> 21: 2 3 1 0 Inf s1 s1 0.2867335
#> 22: 2 3 1 0 Inf s1 s2 0.7132665
#> 23: 2 3 1 0 Inf s2 s1 0.0000000
#> 24: 2 3 1 0 Inf s2 s2 1.0000000
#> strategy_id patient_id time_id time_start time_stop from to mean
#> sd
#> <num>
#> 1: 0.019239113
#> 2: 0.019239113
#> 3: 0.000000000
#> 4: 0.000000000
#> 5: 0.004251159
#> 6: 0.004251159
#> 7: 0.000000000
#> 8: 0.000000000
#> 9: 0.056336744
#> 10: 0.056336744
#> 11: 0.000000000
#> 12: 0.000000000
#> 13: 0.005715383
#> 14: 0.005715383
#> 15: 0.000000000
#> 16: 0.000000000
#> 17: 0.003766132
#> 18: 0.003766132
#> 19: 0.000000000
#> 20: 0.000000000
#> 21: 0.033360839
#> 22: 0.033360839
#> 23: 0.000000000
#> 24: 0.000000000
#> sd
summary(tprobs, probs = c(.025, .975))
#> strategy_id patient_id time_id time_start time_stop from to mean
#> <int> <int> <num> <num> <num> <char> <char> <num>
#> 1: 1 1 1 0 Inf s1 s1 0.3437247
#> 2: 1 1 1 0 Inf s1 s2 0.6562753
#> 3: 1 1 1 0 Inf s2 s1 0.0000000
#> 4: 1 1 1 0 Inf s2 s2 1.0000000
#> 5: 1 2 1 0 Inf s1 s1 0.3946369
#> 6: 1 2 1 0 Inf s1 s2 0.6053631
#> 7: 1 2 1 0 Inf s2 s1 0.0000000
#> 8: 1 2 1 0 Inf s2 s2 1.0000000
#> 9: 1 3 1 0 Inf s1 s1 0.3501535
#> 10: 1 3 1 0 Inf s1 s2 0.6498465
#> 11: 1 3 1 0 Inf s2 s1 0.0000000
#> 12: 1 3 1 0 Inf s2 s2 1.0000000
#> 13: 2 1 1 0 Inf s1 s1 0.2911351
#> 14: 2 1 1 0 Inf s1 s2 0.7088649
#> 15: 2 1 1 0 Inf s2 s1 0.0000000
#> 16: 2 1 1 0 Inf s2 s2 1.0000000
#> 17: 2 2 1 0 Inf s1 s1 0.2673824
#> 18: 2 2 1 0 Inf s1 s2 0.7326176
#> 19: 2 2 1 0 Inf s2 s1 0.0000000
#> 20: 2 2 1 0 Inf s2 s2 1.0000000
#> 21: 2 3 1 0 Inf s1 s1 0.2867335
#> 22: 2 3 1 0 Inf s1 s2 0.7132665
#> 23: 2 3 1 0 Inf s2 s1 0.0000000
#> 24: 2 3 1 0 Inf s2 s2 1.0000000
#> strategy_id patient_id time_id time_start time_stop from to mean
#> sd 2.5% 97.5%
#> <num> <num> <num>
#> 1: 0.019239113 0.3308008 0.3566486
#> 2: 0.019239113 0.6433514 0.6691992
#> 3: 0.000000000 0.0000000 0.0000000
#> 4: 0.000000000 1.0000000 1.0000000
#> 5: 0.004251159 0.3917812 0.3974927
#> 6: 0.004251159 0.6025073 0.6082188
#> 7: 0.000000000 0.0000000 0.0000000
#> 8: 0.000000000 1.0000000 1.0000000
#> 9: 0.056336744 0.3123092 0.3879978
#> 10: 0.056336744 0.6120022 0.6876908
#> 11: 0.000000000 0.0000000 0.0000000
#> 12: 0.000000000 1.0000000 1.0000000
#> 13: 0.005715383 0.2872958 0.2949744
#> 14: 0.005715383 0.7050256 0.7127042
#> 15: 0.000000000 0.0000000 0.0000000
#> 16: 0.000000000 1.0000000 1.0000000
#> 17: 0.003766132 0.2648525 0.2699123
#> 18: 0.003766132 0.7300877 0.7351475
#> 19: 0.000000000 0.0000000 0.0000000
#> 20: 0.000000000 1.0000000 1.0000000
#> 21: 0.033360839 0.2643233 0.3091437
#> 22: 0.033360839 0.6908563 0.7356767
#> 23: 0.000000000 0.0000000 0.0000000
#> 24: 0.000000000 1.0000000 1.0000000
#> sd 2.5% 97.5%
# Summary where each column is a matrix
ps <- summary(tprobs, id = tpmat_id, unflatten = TRUE)
ps
#> strategy_id patient_id time_id time_start time_stop
#> <int> <int> <num> <num> <num>
#> 1: 1 1 1 0 Inf
#> 2: 1 2 1 0 Inf
#> 3: 1 3 1 0 Inf
#> 4: 2 1 1 0 Inf
#> 5: 2 2 1 0 Inf
#> 6: 2 3 1 0 Inf
#> mean
#> <list>
#> 1: 0.3437247,0.0000000,0.6562753,1.0000000
#> 2: 0.3946369,0.0000000,0.6053631,1.0000000
#> 3: 0.3501535,0.0000000,0.6498465,1.0000000
#> 4: 0.2911351,0.0000000,0.7088649,1.0000000
#> 5: 0.2673824,0.0000000,0.7326176,1.0000000
#> 6: 0.2867335,0.0000000,0.7132665,1.0000000
#> sd
#> <list>
#> 1: 0.01923911,0.00000000,0.01923911,0.00000000
#> 2: 0.004251159,0.000000000,0.004251159,0.000000000
#> 3: 0.05633674,0.00000000,0.05633674,0.00000000
#> 4: 0.005715383,0.000000000,0.005715383,0.000000000
#> 5: 0.003766132,0.000000000,0.003766132,0.000000000
#> 6: 0.03336084,0.00000000,0.03336084,0.00000000
ps$mean
#> [[1]]
#> s1 s2
#> s1 0.3437247 0.6562753
#> s2 0.0000000 1.0000000
#>
#> [[2]]
#> s1 s2
#> s1 0.3946369 0.6053631
#> s2 0.0000000 1.0000000
#>
#> [[3]]
#> s1 s2
#> s1 0.3501535 0.6498465
#> s2 0.0000000 1.0000000
#>
#> [[4]]
#> s1 s2
#> s1 0.2911351 0.7088649
#> s2 0.0000000 1.0000000
#>
#> [[5]]
#> s1 s2
#> s1 0.2673824 0.7326176
#> s2 0.0000000 1.0000000
#>
#> [[6]]
#> s1 s2
#> s1 0.2867335 0.7132665
#> s2 0.0000000 1.0000000
#>