Method to read, subset and sample expanded data
Source:R/generics.R, R/trial_sequence.R
load_expanded_data.Rd![[Experimental]](figures/lifecycle-experimental.svg)
Usage
load_expanded_data(
object,
p_control = NULL,
period = NULL,
subset_condition = NULL,
seed = NULL
)
# S4 method for class 'trial_sequence'
load_expanded_data(
object,
p_control = NULL,
period = NULL,
subset_condition = NULL,
seed = NULL
)Arguments
- object
An object of class trial_sequence.
- p_control
Probability of selecting a control,
NULLfor no sampling (default).- period
An integerish vector of non-zero length to select trial period(s) or
NULL(default) to select all trial periods.- subset_condition
A string or
NULL(default).subset_conditionwill be translated to a call (in case the expanded data is saved as a data.table or in the csv format) or to a SQL-query (in case the expanded data is saved as a duckdb file).The operators
"==", "!=", ">", ">=", "<", "<=", %in%", "&", "|"are supported. Numeric vectors can be written asc(1, 2, 3)or1:3. Variables are not supported.Note: Make sure numeric vectors written as
1:3are surrounded by spaces, e.g.a %in% c( 1:4 , 6:9 ), otherwise the code will fail.- seed
An integer seed or
NULL(default).Note: The same seed will return a different result depending on the class of the te_datastore object contained in the trial_sequence object.
Value
An updated trial_sequence object, the data is stored in slot @outcome_data
as a te_outcome_data object.
Details
This method is used on trial_sequence objects to read, subset and sample expanded data.
Examples
# create a trial_sequence-class object
trial_itt_dir <- file.path(tempdir(), "trial_itt")
dir.create(trial_itt_dir)
trial_itt <- trial_sequence(estimand = "ITT") |>
set_data(data = data_censored) |>
set_outcome_model(adjustment_terms = ~ x1 + x2)
trial_itt_csv <- set_expansion_options(
trial_itt,
output = save_to_csv(file.path(trial_itt_dir, "trial_csvs")),
chunk_size = 500
) |>
expand_trials()
# load_expanded_data default behaviour returns all trial_periods and doesn't sample
load_expanded_data(trial_itt_csv)
#> Trial Sequence Object
#> Estimand: Intention-to-treat
#>
#> Data:
#> - N: 725 observations from 89 patients
#> id period treatment x1 x2 x3 x4 age age_s
#> <int> <int> <num> <num> <num> <int> <num> <num> <num>
#> 1: 1 0 1 1 1.146148362 0 0.7342030 36 0.08333333
#> 2: 1 1 1 1 0.002200337 0 0.7342030 37 0.16666667
#> ---
#> 724: 99 6 1 1 -0.033762356 1 0.5752681 71 3.00000000
#> 725: 99 7 0 0 -1.340496520 1 0.5752681 72 3.08333333
#> outcome censored eligible time_on_regime wt
#> <num> <int> <num> <num> <num>
#> 1: 0 0 1 0 1
#> 2: 0 0 0 1 1
#> ---
#> 724: 0 0 0 1 1
#> 725: 1 0 0 2 1
#>
#> IPW for informative censoring:
#> - No weight model specified
#>
#> Sequence of Trials Data:
#> - Chunk size: 500
#> - Censor at switch: FALSE
#> - First period: 0 | Last period: Inf
#>
#> A TE Datastore CSV object
#> N: 1558 observations
#> Periods: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#> Path: /tmp/RtmpRXqWy2/trial_itt/trial_csvs
#> Columns: id, trial_period, followup_time, outcome, weight, treatment, x1, x2, assigned_treatment
#>
#> Outcome model:
#> - Formula: outcome ~ assigned_treatment + x1 + x2 + followup_time + I(followup_time^2) + trial_period + I(trial_period^2)
#> - Treatment variable: assigned_treatment
#> - Adjustment variables: x1 x2
#> - Model fitter type: te_stats_glm_logit
#>
#> Use fit_msm() to fit the outcome model
#>
#> Outcome data
#> N: 1558 observations from 89 patients in 18 trial periods
#> Periods: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#> id trial_period followup_time outcome weight treatment x1 x2
#> <int> <int> <int> <int> <int> <int> <int> <num>
#> 1: 1 0 0 0 1 1 1 1.146148
#> 2: 1 0 1 0 1 1 1 1.146148
#> ---
#> 1557: 54 17 1 0 1 0 0 1.846423
#> 1558: 54 17 2 0 1 0 0 1.846423
#> assigned_treatment sample_weight
#> <int> <num>
#> 1: 1 1
#> 2: 1 1
#> ---
#> 1557: 1 1
#> 1558: 1 1
# load_expanded_data can subset the data before sampling
load_expanded_data(
trial_itt_csv,
p_control = 0.2,
period = 1:20,
subset_condition = "followup_time %in% 1:20 & x2 < 1",
)
#> Warning: The following periods don't exist in the data and were omitted: 18, 19, 20
#> Trial Sequence Object
#> Estimand: Intention-to-treat
#>
#> Data:
#> - N: 725 observations from 89 patients
#> id period treatment x1 x2 x3 x4 age age_s
#> <int> <int> <num> <num> <num> <int> <num> <num> <num>
#> 1: 1 0 1 1 1.146148362 0 0.7342030 36 0.08333333
#> 2: 1 1 1 1 0.002200337 0 0.7342030 37 0.16666667
#> ---
#> 724: 99 6 1 1 -0.033762356 1 0.5752681 71 3.00000000
#> 725: 99 7 0 0 -1.340496520 1 0.5752681 72 3.08333333
#> outcome censored eligible time_on_regime wt
#> <num> <int> <num> <num> <num>
#> 1: 0 0 1 0 1
#> 2: 0 0 0 1 1
#> ---
#> 724: 0 0 0 1 1
#> 725: 1 0 0 2 1
#>
#> IPW for informative censoring:
#> - No weight model specified
#>
#> Sequence of Trials Data:
#> - Chunk size: 500
#> - Censor at switch: FALSE
#> - First period: 0 | Last period: Inf
#>
#> A TE Datastore CSV object
#> N: 1558 observations
#> Periods: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#> Path: /tmp/RtmpRXqWy2/trial_itt/trial_csvs
#> Columns: id, trial_period, followup_time, outcome, weight, treatment, x1, x2, assigned_treatment
#>
#> Outcome model:
#> - Formula: outcome ~ assigned_treatment + x1 + x2 + followup_time + I(followup_time^2) + trial_period + I(trial_period^2)
#> - Treatment variable: assigned_treatment
#> - Adjustment variables: x1 x2
#> - Model fitter type: te_stats_glm_logit
#>
#> Use fit_msm() to fit the outcome model
#>
#> Outcome data
#> N: 123 observations from 23 patients in 15 trial periods
#> Periods: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
#> Subset condition: followup_time %in% 1:20 & x2 < 1
#> Sampling control observations with probability: 0.2
#> id trial_period followup_time outcome weight treatment x1 x2
#> <int> <int> <int> <int> <int> <int> <int> <num>
#> 1: 2 1 1 0 1 1 1 -0.9830300
#> 2: 33 1 1 0 1 1 0 -0.5514144
#> ---
#> 122: 54 15 2 0 1 1 0 0.6850722
#> 123: 54 15 3 0 1 0 0 0.6850722
#> assigned_treatment sample_weight
#> <int> <num>
#> 1: 1 5
#> 2: 1 5
#> ---
#> 122: 0 5
#> 123: 0 5
# delete after use
unlink(trial_itt_dir, recursive = TRUE)