| sepmodel_cont {NCC} | R Documentation |
Separate analysis for continuous data
Description
This function performs separate analysis (only taking into account concurrent controls) using a linear model.
Usage
sepmodel_cont(data, arm, alpha = 0.025, check = TRUE, ...)
Arguments
data |
Data frame trial data, e.g. result from the |
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. |
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. |
Details
The separate analysis takes into account only the data from the evaluated experimental treatment arm and its concurrent controls and uses a linear regression model to evaluate the given treatment arm.
Denoting by y_j the continuous response for patient j, by k_j the arm patient j was allocated to, and by M the treatment arm under evaluation, the regression model is given by:
E(y_j) = \eta_0 + \theta_M \cdot I(k_j=M)
where \eta_0 is the response in the concurrent controls;
\theta_M represents the treatment effect of treatment M as compared to control.
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(s)
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")
sepmodel_cont(data = trial_data, arm = 3)