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

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

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

Boolean for removing IIV on slope

Value

nlme::lme model

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)
#> variance-covariance issues detected, try running again with method = 'REML'
#> If issues persist, try removing IIV.
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.