SEQuential is an all-in-one API to SEQuential analysis, returning a SEQoutput object of results. More specific examples can be found on pages at https://causalinference.github.io/SEQTaRget/
Arguments
- data
data.frame or data.table, will perform expansion according to arguments passed through the
optionsargument- id.col
String: column name of the id column
- time.col
String: column name of the time column
- eligible.col
String: column name of the eligibility column
- treatment.col
String: column name of the treatment column
- outcome.col
String: column name of the outcome column
- time_varying.cols
List: column names for time varying columns
- fixed.cols
List: column names for fixed columns
- method
String: method of analysis to perform; should be one of
"ITT","dose-response", or"censoring"- options
List: optional list of parameters from
SEQopts()- verbose
Logical: if TRUE, cats progress to console, default is
TRUE
Value
An S4 object of class SEQoutput. If options = SEQopts(expand.only = TRUE), returns the expanded data.table directly, with analysis steps skipped.
Details
Implementation of sequential trial emulation for the analysis of observational databases.
The SEQuential software accommodates time-varying treatments and confounders, as well as binary
and failure time outcomes. SEQuential allows to compare both static and dynamic strategies,
can be used to estimate observational analogs of intention-to-treat
and per-protocol effects, and can adjust for potential selection bias induced by losses-to-follow-up.
Examples
# \donttest{
data <- SEQdata
# Intention-to-treat (ITT) effect: subjects are assigned to the treatment
# arm defined by their baseline treatment and followed regardless of any later
# treatment changes, so no weighting is required.
SEQuential(data, id.col = "ID",
time.col = "time",
eligible.col = "eligible",
treatment.col = "tx_init",
outcome.col = "outcome",
time_varying.cols = c("N", "L", "P"),
fixed.cols = "sex",
method = "ITT",
options = SEQopts())
#>
#> Full dataset: 12,180 observations, 11 variables
#>
#> Non-required columns provided, pruning for efficiency
#>
#> Pruned
#>
#> Original dataset (eligible subjects): 9,203 observations, 9 variables
#>
#> Expanding Data...
#>
#> Pre-filter expansion: 310,080 observations
#>
#> Expanded dataset: 248,485 observations, 13 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 248,485 observations, 13 variables
#>
#> Moving forward with ITT analysis
#>
#> ITT model created successfully
#>
#> Completed
#> SEQuential process completed in 1.59 seconds :
#> Initialized with:
#> Outcome covariates: outcome~tx_init_bas+followup+followup_sq+trial+trial_sq+sex+N_bas+L_bas+P_bas
#> Numerator covariates: NA
#> Denominator covariates: NA
#>
#> Full Model Information ==========================================
#>
#> Outcome Model ====================================================
#> Coefficients and Weighting:
#> Estimate Std. Error
#> (Intercept) -6.828506036 5.668367e-01
#> tx_init_bas1 0.189350031 3.005120e-02
#> followup 0.033715157 3.276751e-03
#> followup_sq -0.000146912 6.466141e-05
#> trial 0.044566166 1.124702e-02
#> trial_sq 0.000578777 1.001866e-04
#> sex1 0.127172410 2.595630e-02
#> N_bas 0.003290667 2.589182e-03
#> L_bas -0.013392420 1.428081e-02
#> P_bas 0.200724099 5.956440e-02
#>
#> Diagnostic Tables ==================================================
#> Unique Outcome Table (distinct subjects who had the outcome):
#>
#> |tx_init_bas | outcome| n|
#> |:-----------|-------:|---:|
#> |0 | 1| 203|
#> |1 | 1| 185|
#>
#> Non-Unique Outcome Table (total outcome events):
#>
#> |tx_init_bas | outcome| n|
#> |:-----------|-------:|----:|
#> |0 | 1| 1928|
#> |1 | 1| 4432|
#>
#> Unique Follow-up Table (distinct subjects contributing follow-up):
#>
#> |tx_init_bas | n|
#> |:-----------|---:|
#> |0 | 300|
#> |1 | 273|
#>
#> Non-Unique Follow-up Table (person-time intervals):
#>
#> |tx_init_bas | n|
#> |:-----------|------:|
#> |0 | 93151|
#> |1 | 155334|
# Per-protocol effect via artificial censoring: subjects are censored when they
# deviate from their assigned strategy, and inverse-probability-of-censoring
# weights adjust for the resulting selection bias. The denominator models the
# probability of remaining uncensored given the time-varying confounders, while
# the numerator uses only the baseline covariates to stabilize the weights (so
# the two formulas must differ - identical formulas give weights of 1).
SEQuential(data, id.col = "ID",
time.col = "time",
eligible.col = "eligible",
treatment.col = "tx_init",
outcome.col = "outcome",
time_varying.cols = c("N", "L", "P"),
fixed.cols = "sex",
method = "censoring",
options = SEQopts(weighted = TRUE,
numerator = "sex",
denominator = "N + L + P + sex"))
#>
#> Full dataset: 12,180 observations, 11 variables
#>
#> Non-required columns provided, pruning for efficiency
#>
#> Pruned
#>
#> Original dataset (eligible subjects): 9,203 observations, 9 variables
#>
#> Expanding Data...
#>
#> Pre-filter expansion: 310,080 observations
#>
#> Expanded dataset (pre-censoring): 248,485 observations, 15 variables
#>
#> Expanded dataset (post-censoring): 102,749 observations, 15 variables
#> entering outcome model (uncensored): 96,251
#> artificially censored (treatment switch): 6,498
#>
#> Expansion Successful
#>
#> Final analysis dataset: 102,749 observations, 15 variables
#>
#> Moving forward with censoring analysis
#>
#> censoring model created successfully
#>
#> Completed
#> SEQuential process completed in 1.02 seconds :
#> Initialized with:
#> Outcome covariates: outcome~tx_init_bas+followup+followup_sq+trial+trial_sq+sex
#> Numerator covariates: tx_init~sex
#> Denominator covariates: tx_init~N+L+P+sex
#>
#> Full Model Information ==========================================
#>
#> Outcome Model ====================================================
#> Coefficients and Weighting:
#> Estimate Std. Error
#> (Intercept) -4.9134317258 0.0925102561
#> tx_init_bas1 0.5051368699 0.0741355065
#> followup 0.0281212245 0.0058811408
#> followup_sq 0.0001084173 0.0001476455
#> trial -0.0132858466 0.0057384370
#> trial_sq 0.0011377569 0.0001049305
#> sex1 0.0660151461 0.0422912172
#>
#> Weight Information =============================================
#> Treatment Lag = 0 Treatment = 0 Model ====================================
#> Numerator ==========================
#> Estimate Std. Error
#> (Intercept) -1.63699131 0.06159802
#> sex1 0.03738753 0.08644011
#> Denominator ==========================
#> Estimate Std. Error
#> (Intercept) -1.332290880 0.247265065
#> N 0.009930812 0.008667517
#> L -0.011516315 0.038851934
#> P -0.056782905 0.026287074
#> sex1 0.033299544 0.087312037
#> Treatment Lag = 1 Treatment = 1 Model ====================================
#> Numerator ==========================
#> Estimate Std. Error
#> (Intercept) 3.0276301 0.07259087
#> sex1 -0.1467371 0.10125464
#> Denominator ==========================
#> Estimate Std. Error
#> (Intercept) 2.654431030 0.29534161
#> N 0.001573135 0.01033761
#> L 0.050900387 0.03934269
#> P 0.046316350 0.03824053
#> sex1 -0.136773596 0.10169194
#> Weights:
#> Min: 0.7359596
#> Max: 1.823553
#> StDev: 0.06287385
#> P01: 0.8731989
#> P25: 0.984536
#> P50: 0.9995707
#> P75: 1.021913
#> P99: 1.257297
#>
#>
#> Diagnostic Tables ==================================================
#> Unique Outcome Table (distinct subjects who had the outcome):
#>
#> |tx_init_bas | outcome| n|
#> |:-----------|-------:|---:|
#> |1 | 1| 153|
#> |0 | 1| 50|
#>
#> Non-Unique Outcome Table (total outcome events):
#>
#> |tx_init_bas | outcome| n|
#> |:-----------|-------:|----:|
#> |1 | 1| 2157|
#> |0 | 1| 222|
#>
#> Unique Follow-up Table (distinct subjects contributing follow-up):
#>
#> |tx_init_bas | n|
#> |:-----------|---:|
#> |0 | 300|
#> |1 | 273|
#>
#> Non-Unique Follow-up Table (person-time intervals):
#>
#> |tx_init_bas | n|
#> |:-----------|-----:|
#> |0 | 16260|
#> |1 | 79991|
#>
#> Unique Switch Table:
#>
#> |switch | n|
#> |:------|----:|
#> |FALSE | 8399|
#> |TRUE | 804|
#>
#> Non-Unique Switch Table:
#>
#> |switch | n|
#> |:------|-----:|
#> |FALSE | 96251|
#> |TRUE | 6498|
# }