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/SEQuential/

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, if not already expanded with SEQexpand, will preform expansion according to arguments passed to either params or ...

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

options

List: optional list of parameters from SEQopts

verbose

Logical: if TRUE, cats progress to console

Value

An S4 object of class SEQoutput

Details

Implemention 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())
#> Expanding Data...
#> Expansion Successful
#> Moving forward with ITT analysis
#> ITT model created successfully
#> Completed
# }