SEQuential trial emulation

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 preform 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 preform; 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

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. SEQ 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())
#> Non-required columns provided, pruning for efficiency
#> Pruned
#> Expanding Data...
#> Expansion Successful
#> Moving forward with ITT analysis
#> ITT model created successfully
#> Completed
# }