Specifies the distribution of a hypothetical control group, used when the proof-of-concept study has no concurrent control group (Kang et al., 2026).
Arguments
- data
Hypothetical control data created by
data_binary,data_binary2(binary endpoints) ordata_tte(time-to-event endpoint).- mean
Location of the hypothetical control mean, a number for one continuous endpoint or a numeric vector of length 2 for two continuous endpoints.
- r
Positive ratio of the squared scale of the hypothetical control to that of the treatment group (continuous endpoints).
Value
An object of class "qdm_hypothetical" with elements
type ("data" or "location"), data,
mean and r.
Details
For binary and time-to-event endpoints, give data: the
hypothetical control data are combined with the prior of the control
group, for example \(\pi_c \sim \textrm{Beta}(a_c + y, b_c + n - y)\)
for one binary endpoint, or a Gamma distribution of the hazard with shape
\(a_c + d\) and rate \(b_c + E\) for \(d\) events in the total
observed time \(E\). For continuous endpoints, give mean and r: the
control mean has the same t distribution as the posterior distribution of
the treatment mean, with location mean and squared scale multiplied
by r, that is
\(\mu_c \sim t_{\nu_{nt}}(\mu_{0c}, r \sigma_{nt}^2 / \kappa_{nt})\).
References
Kang, R., Yamaguchi, Y. and Han, C. (2026). Bayesian quantitative decision-making framework in cell and gene therapy development for rare diseases. Journal of Biopharmaceutical Statistics. doi:10.1080/10543406.2026.2655410
Examples
# Binary endpoint: 5 responders out of 15 hypothetical control subjects
hypothetical(data = data_binary(n = 15, y = 5))
#> Hypothetical control: data (binary, n = 15, y = 5)
# Continuous endpoint: control mean located at 1 with the same scale
hypothetical(mean = 1, r = 1)
#> Hypothetical control: mean = (1), r = 1
# Time-to-event endpoint: 8 events in a total observed time of 60
hypothetical(data = data_tte(events = 8, exposure = 60))
#> Hypothetical control: data (time-to-event, events = 8, exposure = 60)