lmmModel_estimates: Get estimates from a linear mixed model of tumor growth data

In SynergyLMM: Statistical Framework for in Vivo Drug Combination Studies

Get estimates from a linear mixed model of tumor growth data

Description

lmmModel_estimates allows the user to easily extract some of the interesting model estimates for further use in other functions, such as for power calculation.

Usage

lmmModel_estimates(model)

Arguments

model

An object of class "lme" representing the linear mixed-effects model fitted by lmmModel().

Details

The model estimates provided by lmmModel_estimates include:

• Fixed effect coefficients: \hat{\beta}_C, \hat{\beta}_A, \hat{\beta}_B, \hat{\beta}_{AB}, which represent the estimated specific growth rates for the Control, Drug A, Drug B and Combination groups, respectively. These are shown in columns control, drug_a, drug_b, and combination, respectively.

• Standard deviation of the random effects (between-subject variance). Column sd_ranef.

• Standard deviation of the residuals (within-subject variance). Column sd_resid.

Value

A data frame with the estimated values for the coefficients of the tumor growth for each treatment, the standard deviation of the random effects, and the standard deviation of the residuals of the model. These values can be useful for the power analysis of the model using APrioriPwr().

Examples

data("grwth_data")
# Fit example model
lmm <- lmmModel(
  data = grwth_data,
  sample_id = "subject",
  time = "Time",
  treatment = "Treatment",
  tumor_vol = "TumorVolume",
  trt_control = "Control",
  drug_a = "DrugA",
  drug_b = "DrugB",
  combination = "Combination"
  ) 
# Get the estimates
lmmModel_estimates(lmm)

SynergyLMM documentation built on April 4, 2025, 4:13 a.m.