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Forceful estimation of terminal slope

Source: R/force_find_lambdaz.R
force_find_lambdaz.Rd

Estimates the terminal elimination rate constant (lambda_z) of a pharmacokinetic profile. The function first attempts to use the find_best_lambdaz method. If no valid estimate is obtained, it falls back to a simplified log-linear regression using progressively fewer data points to enforce a negative slope.

Usage

force_find_lambdaz(time, conc, ...)

Arguments

time

Numeric vector of time points.

conc

Numeric vector of concentration values corresponding to time.

...

Additional arguments passed to find_best_lambdaz (e.g., nlastpoints).

Value

A list containing:

  • lambdaz: Estimated terminal elimination rate constant (1/time)

  • intercept: Intercept of the log-linear regression, used to extrapolate concentration at time zero

  • method: Method used (find_best_lambdaz or fallback regression)

  • UsedPoints: Number of time-concentration points used for estimation

  • adj.r.squared: Adjusted R-squared (available only when using find_best_lambdaz)

  • message: Diagnostic message summarizing the outcome

  • slopefit: Fitted linear model object

Details

This function implements a two-step strategy to ensure estimation of the terminal elimination slope:

  • First, it applies find_best_lambdaz to automatically select the best fitting terminal phase segment based on adjusted R-squared optimization.

  • If find_best_lambdaz fails (e.g., limited data), the function forcibly fits simplified linear models using progressively fewer points (starting from n-1 down to 2) until a negative slope is identified. In fallback mode, adjusted R-squared is not considered.

See also

find_best_lambdaz

Author

Zhonghui Huang

Examples

time <- c(0.5, 1, 2, 4, 6, 8, 10)
conc <- c(12, 8, 5, 3, 2, 1.5, 1)
force_find_lambdaz(time, conc)
#> $lambdaz
#> [1] 0.1791759
#> 
#> $intercept
#> [1] 1.803538
#> 
#> $method
#> [1] "find_best_lambdaz"
#> 
#> $UsedPoints
#> [1] 4
#> 
#> $adj.r.squared
#> [1] 0.9935461
#> 
#> $message
#> [1] "Selected 4 points (higher Rsquare) Rsquare=0.9935 lambdaz=0.1792"
#> 
#> $slopefit
#> 
#> Call:
#> lm(formula = log(conc[subset]) ~ time[subset])
#> 
#> Coefficients:
#>  (Intercept)  time[subset]  
#>       1.8035       -0.1792  
#> 
#>