Mixed regression model analysis for continuous data adjusting for calendar time units as a random factor with AR1 correlation structure

This function performs linear mixed model regression taking into account all trial data until the arm under study leaves the trial and adjusting for calendar time units as random factors with AR1 correlation structure.

Usage

mixmodel_AR1_cal_cont(
  data,
  arm,
  alpha = 0.025,
  ci = FALSE,
  unit_size = 25,
  ncc = TRUE,
  check = TRUE,
  ...
)

Arguments

data: Data frame with trial data, e.g. result from the datasim_cont() function. Must contain columns named 'treatment' and 'response'.
arm: Integer. Index of the treatment arm under study to perform inference on (vector of length 1). This arm is compared to the control group.
alpha: Double. Significance level (one-sided). Default=0.025.
ci: Logical. Indicates whether confidence intervals should be computed. Default=FALSE.
unit_size: Integer. Number of patients per calendar time unit. Default=25.
ncc: Logical. Indicates whether to include non-concurrent data into the analysis. Default=TRUE.
check: Logical. Indicates whether the input parameters should be checked by the function. Default=TRUE, unless the function is called by a simulation function, where the default is FALSE.
...: Further arguments passed by wrapper functions when running simulations.

Value

List containing the following elements regarding the results of comparing arm to control:

p-val - p-value (one-sided)
treat_effect - estimated treatment effect in terms of the difference in means
lower_ci - lower limit of the (1-2*alpha)*100% confidence interval
upper_ci - upper limit of the (1-2*alpha)*100% confidence interval
reject_h0 - indicator of whether the null hypothesis was rejected or not (p_val < alpha)
model - fitted model

Author

Pavla Krotka

Examples


trial_data <- datasim_cont(num_arms = 3, n_arm = 100, d = c(0, 100, 250),
theta = rep(0.25, 3), lambda = rep(0.15, 4), sigma = 1, trend = "linear")

mixmodel_AR1_cal_cont(data = trial_data, arm = 3, ci = TRUE)
#> $p_val
#> [1] 0.07248115
#> 
#> $treat_effect
#> [1] 0.1804342
#> 
#> $lower_ci
#> [1] -0.06229116
#> 
#> $upper_ci
#> [1] 0.4231589
#> 
#> $reject_h0
#> [1] FALSE
#> 
#> $model
#> formula: response ~ as.factor(treatment) + AR1(1 | cal_time)
#> ML: Estimation of corrPars, lambda and phi by ML.
#>     Estimation of fixed effects by ML.
#> Estimation of lambda and phi by 'outer' ML, maximizing logL.
#> family: gaussian( link = identity ) 
#>  ------------ Fixed effects (beta) ------------
#>                       Estimate Cond. SE t-value
#> (Intercept)             0.1119  0.07136  1.5680
#> as.factor(treatment)1   0.1039  0.12360  0.8403
#> as.factor(treatment)2   0.4034  0.12360  3.2637
#> as.factor(treatment)3   0.1804  0.12360  1.4599
#>  --------------- Random effects ---------------
#> Family: gaussian( link = identity ) 
#>                    --- Correlation parameters:
#>   1.ARphi 
#> -0.280271 
#>            --- Variance parameters ('lambda'):
#> lambda = var(u) for u ~ Gaussian; 
#>    cal_time  :  3.137e-06  
#> # of obs: 500; # of groups: cal_time, 20 
#>  -------------- Residual variance  ------------
#> phi estimate was 1.01841 
#>  ------------- Likelihood values  -------------
#>                         logLik
#> logL       (p_v(h)): -714.0304
#>