piecewise_cal_cont {NCC} | R Documentation |
Model-based analysis for continuous data using discontinuous piecewise polynomials per calendar time unit
Description
This function performs linear regression taking into account all trial data until the arm under study leaves the trial and adjusting for time using discontinuous piecewise polynomials in each calendar time unit.
Usage
piecewise_cal_cont(
data,
arm,
alpha = 0.025,
unit_size = 25,
ncc = TRUE,
poly_degree = 3,
check = TRUE,
...
)
Arguments
data |
Data frame with 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. |
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. |
poly_degree |
Integer. Degree of the piecewise polynomial. Default=3. |
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(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")
piecewise_cal_cont(data = trial_data, arm = 3)