Fit QTc Linear Model — fit_qtc_linear

Fits QT(c) data to linear mixed effects model with fixed effects of intercept and RR slope, with random effects on intercept and slope.

Usage

fit_qtc_linear_model(
  data,
  qt_col,
  rr_col,
  id_col,
  method = "REML",
  remove_rr_iiv = FALSE
)

Arguments

data: A data frame containing C-QT analysis dataset
qt_col: An unquoted column name for QT measurements
rr_col: An unquoted column name for RR measurements
id_col: An unquoted column name for subject ID
method: Method for nlme::lme fitting (ML or REML)
remove_rr_iiv: Logical, whether to remove IIV on slope

Value

An nlme::lme model object with QT ~ RR relationship, including random effects on intercept and optionally slope

Examples

bl <- compute_qtcb_qtcf(cqtkit_data_bl_verapamil, qtbl_col = NULL, rrbl_col = NULL)

qt_mod <- fit_qtc_linear_model(bl, QT, RR, ID)
qtcb_mod <- fit_qtc_linear_model(bl, QTCB, RR, ID)
#> variance-covariance issues detected, try running again with method = 'REML'
#> If issues persist, try removing IIV.
qtcf_mod <- fit_qtc_linear_model(bl, QTCF, RR, ID)
#> variance-covariance issues detected, try running again with method = 'REML'
#> If issues persist, try removing IIV.