sepmodel_adj_bin {NCC}

Separate analysis for binary data adjusted for periods

Description

This function performs separate analysis (only taking into account concurrent controls) using a logistic model and adjusting for periods, if the treatment arm stays in the trial for more than one period.

Usage

sepmodel_adj_bin(data, arm, alpha = 0.025, check = TRUE, ...)

Arguments

data

Data frame with trial data, e.g. result from the datasim_bin() function. Must contain columns named 'treatment', 'response' and 'period'.

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 adjusted separate analysis takes into account only the data from the evaluated experimental treatment arm and its concurrent controls and adjusts for the time effect by including the factor period (defined as a time interval bounded by any treatment arm entering or leaving the platform). The time is then modelled as a step-function with jumps at the beginning of each period. Denoting by y_j the response probability 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:

g(E(y_j)) = \eta_0 + \theta_M \cdot I(k_j=M) + \sum_{s=S_{M_1}+1}^{S_{M_2}} \tau_s \cdot I(t_j \in T_{S_s})

where g(\cdot) denotes the logit link function and \eta_0 is the log odds in the concurrent controls; \theta_M represents the log odds ratio of treatment M and control; \tau_s indicates the stepwise period effect in terms of the log odds ratio between periods S_{M_1} and s (s = S_{M_1}+1, \ldots, S_{M_2}), where S_{M_1} and S_{M_2} denote the periods, in which the investigated treatment arm joined and left the trial, respectively.

If the data consists of only one period, the period in not used as covariate.

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 log-odds ratio

• 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_bin(num_arms = 3, n_arm = 100, d = c(0, 100, 250),
p0 = 0.7, OR = rep(1.8, 3), lambda = rep(0.15, 4), trend="stepwise")

sepmodel_adj_bin(data = trial_data, arm = 3)