Computes the statistical information \(\mathcal{I}\) for the log rate ratio \(\theta = \log(\lambda_2/\lambda_1)\) at a given calendar analysis time, accounting for staggered enrollment, dropout, maximum follow-up, and event gaps.
Usage
compute_info_at_time(
analysis_time,
accrual_rate,
accrual_duration,
lambda1,
lambda2,
dispersion,
ratio = 1,
dropout_rate = 0,
event_gap = 0,
max_followup = Inf
)Arguments
- analysis_time
Calendar time of the analysis.
- accrual_rate
Enrollment rate (subjects per time unit).
- accrual_duration
Duration of the enrollment period.
- lambda1
Event rate \(\lambda_1\) for group 1 (control).
- lambda2
Event rate \(\lambda_2\) for group 2 (treatment).
- dispersion
Dispersion parameter \(k\) such that \(\mathrm{Var}(Y) = \mu + k\mu^2\). Can be a vector of length 2.
- ratio
Allocation ratio \(r = n_2/n_1\). Default is 1.
- dropout_rate
Dropout hazard rate. Default is 0. Can be a vector of length 2 for group-specific rates (control, treatment).
- event_gap
Gap duration after each event. Default is 0.
- max_followup
Maximum follow-up time per subject. Default is
Inf. Can be a vector of length 2.
Details
This function delegates to sample_size_nbinom() with power = NULL and
returns \(\mathcal{I} = 1/\mathrm{Var}(\hat\theta)\) from the resulting
variance. This ensures full consistency with package design calculations,
including piecewise accrual, dropout, max follow-up truncation, event-gap
correction, and follow-up variability inflation (\(Q_g\)).