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SEQuential trial emulation

Source: R/SEQuential.R
SEQuential.Rd

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/

Usage

SEQuential(
  data,
  id.col,
  time.col,
  eligible.col,
  treatment.col,
  outcome.col,
  time_varying.cols = list(),
  fixed.cols = list(),
  method,
  options,
  verbose = TRUE
)

Arguments

data

data.frame or data.table, will perform expansion according to arguments passed through the options argument

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
model <- 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
# }