Mixed regression model analysis for continuous data using the covariates treatment and calendar time unit as fixed effects and the interaction between them as a random effect

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.

Usage

mixmodel_int_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


# \donttest{
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_int_cal_cont(data = trial_data, arm = 3, ci = TRUE)
#> boundary (singular) fit: see help('isSingular')
#> Computing profile confidence intervals ...
#> Computing profile confidence intervals ...
#> $p_val
#> [1] 0.01628808
#> 
#> $treat_effect
#> [1] 0.2860109
#> 
#> $lower_ci
#> [1] 0.03009963
#> 
#> $upper_ci
#> [1] 0.5419223
#> 
#> $reject_h0
#> [1] TRUE
#> 
#> $model
#> Linear mixed model fit by REML ['lmerModLmerTest']
#> Formula: response ~ as.factor(treatment) + as.factor(cal_time) + (1 |  
#>     treatment_aux:cal_time)
#>    Data: data_new
#> REML criterion at convergence: 1404.058
#> Random effects:
#>  Groups                 Name        Std.Dev.
#>  treatment_aux:cal_time (Intercept) 0.0000  
#>  Residual                           0.9759  
#> Number of obs: 499, groups:  treatment_aux:cal_time, 42
#> Fixed Effects:
#>           (Intercept)  as.factor(treatment)1  as.factor(treatment)2  
#>               0.09716                0.29135                0.23416  
#> as.factor(treatment)3   as.factor(cal_time)2   as.factor(cal_time)3  
#>               0.28601               -0.16013               -0.21577  
#>  as.factor(cal_time)4   as.factor(cal_time)5   as.factor(cal_time)6  
#>               0.11338               -0.16546                0.27937  
#>  as.factor(cal_time)7   as.factor(cal_time)8   as.factor(cal_time)9  
#>              -0.27597                0.06038               -0.34488  
#> as.factor(cal_time)10  as.factor(cal_time)11  as.factor(cal_time)12  
#>              -0.71329               -0.08104               -0.13804  
#> as.factor(cal_time)13  as.factor(cal_time)14  as.factor(cal_time)15  
#>               0.59036                0.51443                0.21746  
#> as.factor(cal_time)16  as.factor(cal_time)17  as.factor(cal_time)18  
#>               0.04312                0.06533               -0.06903  
#> as.factor(cal_time)19  as.factor(cal_time)20  
#>               0.02600                0.22503  
#> optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warnings 
#> 
# }