SEQuential | R Documentation |
'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/
SEQuential(
data,
id.col,
time.col,
eligible.col,
treatment.col,
outcome.col,
time_varying.cols = list(),
fixed.cols = list(),
method,
options,
verbose = TRUE
)
data |
data.frame or data.table, if not already expanded with |
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 |
verbose |
Logical: if TRUE, cats progress to console |
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.
An S4 object of class SEQoutput
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())
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