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Mixed regression model analysis for continuous data adjusting for calendar time units as a random factor

Source: R/mixmodel_cal_cont.R
mixmodel_cal_cont.Rd

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_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_cal_cont(data = trial_data, arm = 3, ci = TRUE)
#> Computing profile confidence intervals ...
#> Warning: unexpected decrease in profile: using minstep
#> Warning: Last two rows have identical or NA .zeta values: using minstep
#> Warning: non-monotonic profile for .sig01
#> Warning: bad spline fit for .sig01: falling back to linear interpolation
#> Warning: collapsing to unique 'x' values
#> Computing profile confidence intervals ...
#> Warning: unexpected decrease in profile: using minstep
#> Warning: Last two rows have identical or NA .zeta values: using minstep
#> Warning: non-monotonic profile for .sig01
#> Warning: bad spline fit for .sig01: falling back to linear interpolation
#> Warning: collapsing to unique 'x' values
#> $p_val
#> [1] 0.2132513
#> 
#> $treat_effect
#> [1] 0.09970409
#> 
#> $lower_ci
#> [1] -0.1445281
#> 
#> $upper_ci
#> [1] 0.3442075
#> 
#> $reject_h0
#> [1] FALSE
#> 
#> $model
#> Linear mixed model fit by REML ['lmerModLmerTest']
#> Formula: response ~ as.factor(treatment) + (1 | cal_time)
#>    Data: data_new
#> REML criterion at convergence: 1446.46
#> Random effects:
#>  Groups   Name        Std.Dev.
#>  cal_time (Intercept) 0.04535 
#>  Residual             1.01920 
#> Number of obs: 500, groups:  cal_time, 20
#> Fixed Effects:
#>           (Intercept)  as.factor(treatment)1  as.factor(treatment)2  
#>               0.09596                0.30427                0.11185  
#> as.factor(treatment)3  
#>               0.09970  
#>