Compute and tabulate estimates for log-log regression

Usage

df_doseprop(
  data,
  metrics,
  metric_var = "PPTESTCD",
  exp_var = "PPORRES",
  dose_var = "DOSE",
  method = "normal",
  ci = 0.95,
  sigdigits = 3
)

Arguments

data: Input dataset for log-log regression. Default expected format is output from PKNCA::pk.nca() (i.e., SDTM PP formatting)
metrics: character vector of exposure metrics in data to plot
metric_var: character string of variable in data containing the values provided in metrics. Default is "PPTESTCD".
exp_var: Character string specifying the variable in data containing the exposure metric (dependent variable) Default is "PPORRES".
dose_var: Character string specifying the variable in data containing the dose (independent variable) Default is "DOSE".
method: character string specifying the distribution to be used to derived the confidence interval. Options are "normal" (default) and "tdist"
ci: confidence interval to be calculated. Options are 0.95 (default) and 0.90
sigdigits: number of significant digits for rounding

Value

data.frame

Examples

df_doseprop(data_sad_nca, metrics = c("aucinf.obs", "cmax"))
#>   Intercept StandardError  CI Power   LCL  UCL Proportional
#> 1      4.04        0.0663 95% 0.997 0.867 1.13         TRUE
#> 2      1.09        0.0616 95% 1.070 0.947 1.19         TRUE
#>                            PowerCI    Interpretation   PPTESTCD
#> 1 Power: 0.997 (95% CI 0.867-1.13) Dose-proportional aucinf.obs
#> 2  Power: 1.07 (95% CI 0.947-1.19) Dose-proportional       cmax