Package 'BayesianQDM'

Description

Specifies external (historical) data for one group together with the power prior weight $a_0$. The likelihood of the external data is raised to the power $a_0$, so that $a_0 = 0$ ignores the external data and $a_0 = 1$ pools it with the current data. For the conjugate models used in this package the resulting posterior distribution has a closed form, so no Markov chain Monte Carlo sampling is needed.

Usage

borrow(data, a0)

Arguments

Value

An object of class "qdm_borrow" with elements data and a0.

Examples

borrow(data_binary(n = 20, y = 8), a0 = 0.5)

Description

Converts the marginal response rates of two binary endpoints and their correlation into the probabilities of the four joint response patterns.

Usage

cellprob_binary2(pi1, pi2, rho, tol = 1e-10)

Arguments

Details

For two Bernoulli variables with response rates $\pi_1$ and $\pi_2$, the Pearson correlation $\rho$ determines

$p_{11} = \pi_1 \pi_2 + \rho \sqrt{\pi_1 (1 - \pi_1) \pi_2 (1 - \pi_2)},$

and $p_{10} = \pi_1 - p_{11}$, $p_{01} = \pi_2 - p_{11}$, $p_{00} = 1 - p_{10} - p_{01} - p_{11}$. All four probabilities are non-negative only if $\max(0, \pi_1 + \pi_2 - 1) \le p_{11} \le \min(\pi_1, \pi_2)$, which bounds the feasible range of $\rho$. An error is raised if rho is outside this range by more than tol.

Value

A named numeric vector of the probabilities of the cells "00", "01", "10" and "11" (first digit: endpoint 1; second digit: endpoint 2), summing to 1.

See Also

Other distributions: pbetabinomdiff(), pbetadiff(), phazardratio(), psurvdiff(), ptdiff(), rdirichlet()

Examples

cellprob_binary2(pi1 = 0.3, pi2 = 0.4, rho = 0.3)

Description

Specifies how subjects are enrolled, how they drop out and when the analysis takes place. The operating characteristics of a design with a time-to-event endpoint (design_tte) are computed by simulating studies conducted in this way, and the posterior predictive probabilities by simulating future studies.

Usage

conduct_tte(
  accrual_time,
  accrual_rate = rep(1, length(accrual_time) - 1L),
  dropout_time = 0,
  dropout_hazard = 0,
  cut_time = NULL,
  cut_events = NULL
)

Arguments

Details

The subjects of all groups are enrolled at independent times with a piecewise uniform distribution: the enrollment period is divided by accrual_time, and the probability of an interval is proportional to its rate in accrual_rate times its length (only the relative rates matter). The dropout time of a subject, measured from enrollment, has a piecewise exponential distribution with hazard dropout_hazard[j] from time dropout_time[j] (the last hazard applies until infinity), independently of the event time. The analysis takes place at the calendar time cut_time after the start of enrollment, or at the calendar time of the cut_events-th event among all subjects of the study (if fewer events occur, all subjects are followed until their event or dropout). A subject enrolled at time $e$ with event time $T$ and dropout time $C$ contributes the observed time $\min(T, C, \textrm{cut} - e)$ (0 if enrolled after the analysis) and an event if $T \le C$ and $e + T \le \textrm{cut}$.

Value

An object of class "qdm_conduct" with the arguments and the element accrual_cum, the cumulative probabilities of enrollment at the ends of the intervals.

See Also

design_tte

Examples

# Enrollment over 15 months with increasing rates, dropout hazard 0.00226
# per month, analysis 15.44 months after the start of enrollment
conduct_tte(accrual_time = c(0, 5, 10, 15), accrual_rate = c(0.4, 2, 4.5),
            dropout_hazard = 0.00226, cut_time = 15.44)

# Uniform enrollment over 12 months, analysis at the 20th event
conduct_tte(accrual_time = c(0, 12), cut_events = 20)

Description

Specifies a Go/NoGo decision criterion based on the posterior probability of the treatment effect $\theta$ (the difference between the treatment and control groups, or their hazard ratio for a time-to-event endpoint) given the proof-of-concept data $D$.

Usage

criterion_posterior(
  theta_tv,
  theta_mav,
  gamma_go,
  gamma_nogo,
  go_regions = 1L,
  nogo_regions = 9L,
  rule = c("joint", "and", "or"),
  direction = "greater"
)

Arguments

Details

For one endpoint, the Go region is $\theta > \theta_{TV}$ and the NoGo region is $\theta \le \theta_{MAV}$. The Go criterion is $\Pr(\theta > \theta_{TV} \mid D) \ge \gamma_{go}$ and the NoGo criterion is $\Pr(\theta \le \theta_{MAV} \mid D) \ge \gamma_{nogo}$. In the notation of Kang et al. (2026), where the NoGo criterion is $\Pr(\theta > \theta_{MAV} \mid D) \le \gamma_2$, this corresponds to $\gamma_{nogo} = 1 - \gamma_2$.

By default, larger effects are favorable (direction = "greater", with $\theta_{TV} > \theta_{MAV}$), as for most efficacy endpoints. For an endpoint on which smaller effects are favorable, such as the rate of an adverse event or a hazard ratio, direction = "less" reverses the inequalities (with $\theta_{TV} < \theta_{MAV}$): the Go region is $\theta < \theta_{TV}$, the NoGo region is $\theta \ge \theta_{MAV}$, and the criteria are $\Pr(\theta < \theta_{TV} \mid D) \ge \gamma_{go}$ and $\Pr(\theta \ge \theta_{MAV} \mid D) \ge \gamma_{nogo}$.

For two endpoints, the target values and minimum acceptable values divide the plane of $(\theta_1, \theta_2)$ into nine regions (see qdm_regions). The Go criterion is that the posterior probability of the union of go_regions is at least $\gamma_{go}$, and the NoGo criterion is that the posterior probability of the union of nogo_regions is at least $\gamma_{nogo}$ (Yamaguchi et al., submitted, equations 2 and 3). With rule = "and" or "or", the criteria are instead based on the marginal posterior probabilities of each endpoint (equations 4 and 5): the Go criterion is $\Pr(\theta_1 > \theta_{TV1} \mid D) \ge \gamma_{go1}$ and (or) $\Pr(\theta_2 > \theta_{TV2} \mid D) \ge \gamma_{go2}$, and the NoGo criterion is $\Pr(\theta_1 \le \theta_{MAV1} \mid D) \ge \gamma_{nogo1}$ and (or) $\Pr(\theta_2 \le \theta_{MAV2} \mid D) \ge \gamma_{nogo2}$.

With two endpoints, direction can differ between the endpoints, for example an efficacy endpoint ("greater") and a safety endpoint ("less"). The regions are then defined on the favorable side of each endpoint: region 1 has both effects beyond their target values in the favorable direction (see qdm_regions).

The decision is Go if only the Go criterion is met, NoGo if only the NoGo criterion is met, Gray if neither is met, and Miss if both are met. For one endpoint, Miss can occur only when $\gamma_{go} + \gamma_{nogo} \le 1$.

Value

An object of class c("qdm_criterion_posterior", "qdm_criterion").

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

Yamaguchi, Y., Hu, H., Huang, Y., Han, C., Homma, G. and Wang, A. (submitted). Bivariate Bayesian quantitative decision-making for two endpoints in rare disease drug development.

See Also

Other criteria: criterion_predictive()

Examples

# One endpoint
criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
                    gamma_go = 0.5, gamma_nogo = 0.8)

# Two endpoints
criterion_posterior(theta_tv = c(0.2, 0.15), theta_mav = c(0.1, 0.05),
                    gamma_go = 0.3, gamma_nogo = 0.4)

# Two endpoints, marginal criteria combined with AND
criterion_posterior(theta_tv = c(0.2, 0.15), theta_mav = c(0.1, 0.05),
                    gamma_go = c(0.6, 0.5), gamma_nogo = 0.7, rule = "and")

# Rate of an adverse event: Go if it is likely to be reduced by more than
# 0.1, NoGo if it is likely not to be reduced
criterion_posterior(theta_tv = -0.1, theta_mav = 0, gamma_go = 0.6,
                    gamma_nogo = 0.7, direction = "less")

# Efficacy (larger is better) and safety (smaller is better) endpoints
criterion_posterior(theta_tv = c(0.2, -0.1), theta_mav = c(0.1, 0),
                    gamma_go = 0.5, gamma_nogo = 0.5,
                    direction = c("greater", "less"))

Description

Specifies a Go/NoGo decision criterion based on the posterior predictive probability of the treatment effect $\tilde\theta$ that will be observed in a future study with m_t and m_c subjects in the treatment and control groups, given the proof-of-concept data $D$. The future effect is the difference between the future sample proportions (binary endpoints) or sample means (continuous endpoints), or, for a time-to-event endpoint, the difference of the estimated milestone survival probabilities or the ratio of the estimated hazards (see design_tte).

Usage

criterion_predictive(
  theta_null,
  m_t,
  m_c = m_t,
  gamma_go,
  gamma_nogo,
  go_regions = 1L,
  nogo_regions = 4L,
  rule = c("joint", "and", "or"),
  direction = "greater"
)

Arguments

Details

For one endpoint, the Go region is $\tilde\theta > \theta_{NULL}$ and the NoGo region is $\tilde\theta \le \theta_{NULL}$. The Go criterion is $\Pr(\tilde\theta > \theta_{NULL} \mid D) \ge \gamma_{go}$ and the NoGo criterion is $\Pr(\tilde\theta \le \theta_{NULL} \mid D) \ge \gamma_{nogo}$. Because the two probabilities add up to 1, a Gray decision is possible only when $\gamma_{go} + \gamma_{nogo} > 1$, and Miss decisions can occur only when $\gamma_{go} + \gamma_{nogo} \le 1$.

With direction = "less", for an endpoint on which smaller effects are favorable, the Go region is $\tilde\theta < \theta_{NULL}$ and the NoGo region is $\tilde\theta \ge \theta_{NULL}$.

For two endpoints, the null values divide the plane of $(\tilde\theta_1, \tilde\theta_2)$ into four regions (see qdm_regions), and the criteria are based on the predictive probabilities of the unions of go_regions and nogo_regions (Yamaguchi et al., submitted, equations 7 and 8). With rule = "and" or "or", they are based on the marginal predictive probabilities of each endpoint, $\Pr(\tilde\theta_k > \theta_{NULLk} \mid D)$ for Go and $\Pr(\tilde\theta_k \le \theta_{NULLk} \mid D)$ for NoGo, combined with AND or OR (equations 9 and 10). With two endpoints, direction can differ between the endpoints, and the regions are defined on the favorable side of each endpoint.

Value

An object of class c("qdm_criterion_predictive", "qdm_criterion").

References

Yamaguchi, Y., Hu, H., Huang, Y., Han, C., Homma, G. and Wang, A. (submitted). Bivariate Bayesian quantitative decision-making for two endpoints in rare disease drug development.

See Also

Other criteria: criterion_posterior()

Examples

criterion_predictive(theta_null = 0, m_t = 50, gamma_go = 0.9,
                     gamma_nogo = 0.7)

# Smaller effects are favorable
criterion_predictive(theta_null = 0, m_t = 50, gamma_go = 0.9,
                     gamma_nogo = 0.7, direction = "less")

Description

Summarizes the data of one group for a single binary endpoint as the number of subjects and the number of responders. Used for observed proof-of-concept data, external data and hypothetical control data.

Usage

data_binary(n, y)

Arguments

Value

An object of class c("qdm_data_binary", "qdm_data") with integer elements n and y.

See Also

Other data: data_binary2(), data_continuous(), data_continuous2(), data_tte()

Examples

data_binary(n = 12, y = 7)

Description

Summarizes the data of one group for two binary endpoints by the counts of the four joint response patterns.

Usage

data_binary2(counts)

Arguments

Details

The cells are ordered as "00", "01", "10" and "11", where the first digit is the response on endpoint 1 and the second digit is the response on endpoint 2 (1 = response, 0 = no response).

Value

An object of class c("qdm_data_binary2", "qdm_data") with elements n (the total count) and counts (a named integer vector of length 4).

See Also

Other data: data_binary(), data_continuous(), data_continuous2(), data_tte()

Examples

data_binary2(c(4, 2, 3, 6))

Description

Summarizes the data of one group for a single continuous endpoint by its sufficient statistics: the number of subjects, the sample mean and the sample standard deviation (with divisor n - 1).

Usage

data_continuous(n, mean, sd)

Arguments

Value

An object of class c("qdm_data_continuous", "qdm_data") with elements n, mean and sd.

See Also

Other data: data_binary(), data_binary2(), data_continuous2(), data_tte()

Examples

data_continuous(n = 12, mean = 3.2, sd = 2.0)

Description

Summarizes the data of one group for two continuous endpoints by their sufficient statistics: the number of subjects, the vector of sample means and the sample covariance matrix (with divisor n - 1).

Usage

data_continuous2(n, mean, cov)

Arguments

Value

An object of class c("qdm_data_continuous2", "qdm_data") with elements n, mean and cov.

See Also

Other data: data_binary(), data_binary2(), data_continuous(), data_tte()

Examples

data_continuous2(n = 12, mean = c(3.2, 1.5),
                 cov = matrix(c(4, 1.2, 1.2, 2), nrow = 2))

Description

Summarizes the data of one group for a time-to-event endpoint by the sufficient statistics of the exponential model: the number of events and the total observed time (the sum over subjects of the time from entry to the event or censoring). Used for observed proof-of-concept data, external data and hypothetical control data.

Usage

data_tte(events, exposure)

Arguments

Details

With exponential event times and independent censoring, the likelihood of the hazard $\lambda$ is proportional to $\lambda^d \exp(-\lambda E)$ for $d$ events and total observed time $E$, so these two numbers are all that the analysis needs.

Value

An object of class c("qdm_data_tte", "qdm_data") with elements events and exposure.

See Also

Other data: data_binary(), data_binary2(), data_continuous(), data_continuous2()

Examples

data_tte(events = 5, exposure = 40)

Description

Specifies a proof-of-concept study with one binary endpoint: the sample sizes, the prior distributions, the external data, the form of the control group and the decision criterion. The design is used by qdm_decide, qdm_oc and qdm_calibrate.

Usage

design_binary(
  n_t,
  n_c,
  prior_t,
  prior_c = NULL,
  criterion,
  external_t = NULL,
  external_c = NULL,
  hypothetical_c = NULL,
  fixed_c = NULL
)

Arguments

Details

The response rates $\pi_t$ and $\pi_c$ of the treatment and control groups have Beta prior distributions, and the treatment effect is $\theta = \pi_t - \pi_c$. The control group takes one of four forms.

Concurrent control

n_c is at least 1. External control data can be added through external_c.

External control only

n_c = 0 and external_c is given. The distribution of $\pi_c$ is the posterior distribution given the external control data.

Hypothetical control

n_c = 0 and hypothetical_c is given. The distribution of $\pi_c$ is the posterior distribution given the hypothetical control data. The hypothetical control $\pi_c \sim \textrm{Beta}(\alpha_t + z, \beta_t + n_t - z)$ of Kang et al. (2026, Section 4.1.4) is obtained with prior_c equal to prior_t and hypothetical(data_binary(n_t, z)).

Fixed control

n_c = 0 and fixed_c is given: the control response rate is a known value $\pi_c$, as in a single-arm study compared with a benchmark. The posterior probabilities are $\Pr(\pi_t > \theta_{TV} + \pi_c \mid D)$ and $\Pr(\pi_t \le \theta_{MAV} + \pi_c \mid D)$, computed from the Beta posterior distribution of $\pi_t$ (with the inequalities reversed for direction = "less"). For the posterior predictive criterion the future effect is $Y_t / m_t - \pi_c$, where $Y_t$ follows the Beta-binomial posterior predictive distribution (a future effect that differs from the null value by less than $10^{-10}$ is treated as equal to it); m_c is not used. prior_c is not used and must be NULL.

External treatment data (external_t) can be used with any form of the control group. External data enter through a power prior: with external data of $n_e$ subjects and $y_e$ responders and weight $a_0$, the posterior distribution of the response rate is $\textrm{Beta}(\alpha + a_0 y_e + y, \beta + a_0 (n_e - y_e) + n - y)$.

Value

An object of class c("qdm_design_binary", "qdm_design"), a list with the arguments and the element control ("concurrent", "external", "hypothetical" or "fixed").

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

See Also

Other designs: design_binary2(), design_continuous(), design_continuous2(), design_tte()

Examples

# Concurrent control with external control data
design_binary(
  n_t = 12, n_c = 12,
  prior_t = prior_beta(0.5, 0.5), prior_c = prior_beta(0.5, 0.5),
  criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
                                  gamma_go = 0.5, gamma_nogo = 0.8),
  external_c = borrow(data_binary(n = 20, y = 8), a0 = 0.5)
)

# Uncontrolled study with a hypothetical control
design_binary(
  n_t = 12, n_c = 0,
  prior_t = prior_beta(0.5, 0.5), prior_c = prior_beta(0.5, 0.5),
  criterion = criterion_predictive(theta_null = 0, m_t = 50,
                                   gamma_go = 0.9, gamma_nogo = 0.7),
  hypothetical_c = hypothetical(data = data_binary(n = 12, y = 4))
)

# Single-arm study compared with a fixed control response rate of 0.2
design_binary(
  n_t = 20, n_c = 0, prior_t = prior_beta(1, 1),
  criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.05,
                                  gamma_go = 0.8, gamma_nogo = 0.8),
  fixed_c = 0.2
)

Description

Specifies a proof-of-concept study with two binary endpoints: the sample sizes, the Dirichlet prior distributions, the external data, the form of the control group, the decision criterion for two endpoints and the computation settings. The design is used by qdm_decide, qdm_oc and qdm_calibrate.

Usage

design_binary2(
  n_t,
  n_c,
  prior_t,
  prior_c = NULL,
  criterion,
  external_t = NULL,
  external_c = NULL,
  hypothetical_c = NULL,
  fixed_c = NULL,
  nMC = 10000L,
  enumerate = TRUE
)

Arguments

Details

The responses of a subject on the two endpoints fall into one of the four cells "00", "01", "10" and "11" (first digit: endpoint 1), whose probabilities have Dirichlet prior distributions in each group (Yamaguchi et al., submitted, Appendix A.1). The treatment effects are the differences of the marginal response rates, $\theta_k = \pi_{tk} - \pi_{ck}$ with $\pi_1 = p_{10} + p_{11}$ and $\pi_2 = p_{01} + p_{11}$. External data with counts $x_e$ enter through a power prior with weight $a_0$, which adds $a_0 x_e$ to the Dirichlet parameters. The control group takes one of four forms, as in design_binary: a concurrent control group (n_c of at least 1), external control data only (n_c = 0 and external_c), a hypothetical control (n_c = 0 and hypothetical_c created from data_binary2, whose counts are added to prior_c; the hypothetical control distribution Dir(beta) of Yamaguchi et al., Appendix A.1.6, is obtained with prior_c = prior_dirichlet(beta - counts)), or a fixed control (n_c = 0 and fixed_c, the known response rates of the two endpoints in the control group; prior_c must then be NULL). With a fixed control, the posterior probabilities are estimated from draws of the treatment distribution only, and the future effects of the predictive criterion are $S_{tk} / m_t - \pi_{ck}$ (a future effect that differs from the null value by less than $10^{-10}$ is treated as equal to it; m_c is not used).

The posterior probabilities of the regions (posterior criteria) are estimated by Monte Carlo simulation with nMC draws from the Dirichlet posterior distributions. The posterior predictive probabilities (predictive criteria) are computed exactly: the future effects depend only on the numbers of responders on each endpoint, whose joint distribution in each group is obtained from the Dirichlet-multinomial distribution, and a future effect equal to a null value is not counted as larger.

For qdm_oc and qdm_calibrate, with enumerate = TRUE all outcomes of the study are enumerated: there are choose(n + 3, 3) outcomes in a group of n subjects (286 for n = 10), and the probabilities of the regions are computed for every pair of treatment and control outcomes, with the same Monte Carlo draws for all pairs. For a posterior criterion the computing time is proportional to the number of pairs times nMC, and enumeration is limited to 1e7 pairs (for example, 24 subjects in each group). With enumerate = FALSE, nsim studies are simulated under each scenario, which is faster for large sample sizes.

Value

An object of class c("qdm_design_binary2", "qdm_design"), a list with the arguments and the element control ("concurrent", "external", "hypothetical" or "fixed").

References

Yamaguchi, Y., Hu, H., Huang, Y., Han, C., Homma, G. and Wang, A. (submitted). Bivariate Bayesian quantitative decision-making for two endpoints in rare disease drug development.

See Also

Other designs: design_binary(), design_continuous(), design_continuous2(), design_tte()

Examples

# Case study 1 of Yamaguchi et al. (submitted), with a small nMC for speed
des <- design_binary2(
  n_t = 10, n_c = 10,
  prior_t = prior_dirichlet(rep(0.25, 4)),
  prior_c = prior_dirichlet(rep(0.25, 4)),
  criterion = criterion_posterior(theta_tv = c(0.15, 0.15),
                                  theta_mav = c(0.10, 0.10),
                                  gamma_go = 0.263, gamma_nogo = 0.347),
  nMC = 2000
)
des
qdm_decide(des, data_t = data_binary2(c(3, 2, 2, 3)),
           data_c = data_binary2(c(6, 2, 1, 1)), seed = 1)

Description

Specifies a proof-of-concept study with one normally distributed endpoint: the sample sizes, the prior distributions, the external data, the form of the control group, the decision criterion and the method used to compute the probabilities. The design is used by qdm_decide, qdm_oc and qdm_calibrate.

Usage

design_continuous(
  n_t,
  n_c,
  prior_t,
  prior_c = NULL,
  criterion,
  external_t = NULL,
  external_c = NULL,
  hypothetical_c = NULL,
  fixed_c = NULL,
  method = c("NI", "MM", "MC"),
  nMC = 10000L
)

Arguments

Details

The outcomes of group $k$ ($k = t, c$) follow $N(\mu_k, \sigma_k^2)$, and the treatment effect is $\theta = \mu_t - \mu_c$. With the Normal-Inverse-chi-squared prior (prior_nix), the posterior distribution of $(\mu_k, \sigma_k^2)$ is Normal-Inverse-chi-squared with parameters $(\mu_{nk}, \kappa_{nk}, \nu_{nk}, \sigma_{nk}^2)$, and the posterior distribution of the mean is $t_{\nu_{nk}}(\mu_{nk}, \sigma_{nk}^2 / \kappa_{nk})$. With the vague prior (prior_vague) and $n_k$ subjects with sample mean $\bar y_k$ and standard deviation $s_k$, it is $t_{n_k - 1}(\bar y_k, s_k^2 / n_k)$.

External data enter through a power prior with weight $a_0$: data with $n_e$ subjects, mean $\bar y_e$ and standard deviation $s_e$ are combined with the prior as if they were $a_0 n_e$ subjects with the same mean and the sum of squares $a_0 (n_e - 1) s_e^2$. The posterior distribution then has a closed form (Huang et al., submitted). With the vague prior and external data, the posterior distribution of the mean has $a_0 n_e + n_k - 1$ degrees of freedom.

The control group takes one of four forms.

Concurrent control

n_c is at least 2. External control data can be added through external_c.

External control only

n_c = 0 and external_c is given. The distribution of $\mu_c$ is the posterior distribution given the external control data. With the vague prior, $a_0 n_e$ must be greater than 1.

Hypothetical control

n_c = 0 and hypothetical_c is given by hypothetical(mean = mu0c, r = r). Following Kang et al. (2026, Section 4.2.4), the control mean has the same t distribution as the posterior distribution of the treatment mean, with location $\mu_{0c}$ and squared scale multiplied by $r$: $\mu_c \sim t_{\nu_{nt}}(\mu_{0c}, r \sigma_{nt}^2 / \kappa_{nt})$. prior_c is not used and must be NULL.

Fixed control

n_c = 0 and fixed_c is given: the control mean is a known value $\mu_c$, as in a single-arm study compared with a benchmark. The probabilities of the Go and NoGo regions are computed exactly from the t distribution of the treatment mean (posterior) or of the mean of $m_t$ future subjects (posterior predictive), shifted by $\mu_c$; method, nMC and m_c are not used. prior_c is not used and must be NULL.

For the posterior predictive criterion (criterion_predictive), the future effect is the difference between the sample means of $m_t$ and $m_c$ future subjects. The posterior predictive distribution of the mean of $m$ future subjects of a group is $t_{\nu_{n}}(\mu_{n}, \sigma_{n}^2 (1 / \kappa_{n} + 1 / m))$; for a hypothetical control it is $t_{\nu_{nt}}(\mu_{0c}, r \sigma_{nt}^2 (1 / \kappa_{nt} + 1 / m_c))$.

The probabilities of the Go and NoGo regions are probabilities of the difference of two t variables, computed by ptdiff with the chosen method. With method = "MC", the probabilities of the two regions are estimated from the same nMC draws.

Value

An object of class c("qdm_design_continuous", "qdm_design"), a list with the arguments and the element control ("concurrent", "external", "hypothetical" or "fixed").

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

Huang, Y., Yamaguchi, Y., Homma, G., Takeda, K. and Maruo, K. (submitted). Conjugate representation of power priors for efficient Bayesian analysis of normal data.

See Also

Other designs: design_binary(), design_binary2(), design_continuous2(), design_tte()

Examples

# Concurrent control with external treatment and control data
design_continuous(
  n_t = 12, n_c = 12,
  prior_t = prior_vague(), prior_c = prior_vague(),
  criterion = criterion_posterior(theta_tv = 30, theta_mav = 15,
                                  gamma_go = 0.6, gamma_nogo = 0.7),
  external_t = borrow(data_continuous(n = 6, mean = 20, sd = 30), a0 = 0.5),
  external_c = borrow(data_continuous(n = 30, mean = -12, sd = 20),
                      a0 = 0.4)
)

# Uncontrolled study with a hypothetical control
design_continuous(
  n_t = 12, n_c = 0, prior_t = prior_vague(),
  criterion = criterion_predictive(theta_null = 0, m_t = 50,
                                   gamma_go = 0.9, gamma_nogo = 0.7),
  hypothetical_c = hypothetical(mean = -10, r = 1),
  method = "MM"
)

# Single-arm study compared with a fixed control mean of -10
design_continuous(
  n_t = 12, n_c = 0, prior_t = prior_vague(),
  criterion = criterion_posterior(theta_tv = 30, theta_mav = 15,
                                  gamma_go = 0.6, gamma_nogo = 0.7),
  fixed_c = -10
)

Description

Specifies a proof-of-concept study with two continuous endpoints: the sample sizes, the prior distributions, the external data, the form of the control group, the decision criterion for two endpoints and the method used to compute the probabilities. The design is used by qdm_decide, qdm_oc and qdm_calibrate.

Usage

design_continuous2(
  n_t,
  n_c,
  prior_t,
  prior_c = NULL,
  criterion,
  external_t = NULL,
  external_c = NULL,
  hypothetical_c = NULL,
  fixed_c = NULL,
  method = c("MM", "MC"),
  nMC = 10000L
)

Arguments

Details

The responses of a subject on the two endpoints have a bivariate normal distribution with mean vector $\mu_k$ and covariance matrix $\Sigma_k$ in group $k$ (Yamaguchi et al., submitted, Appendix A.2). The prior distribution of $(\mu_k, \Sigma_k)$ is Normal-Inverse-Wishart (prior_niw) or vague (prior_vague, the density $|\Sigma_k|^{-3/2}$). External data enter through a power prior with weight $a_0$, which has the conjugate form, and the posterior parameters are updated with the external data and then with the current data. The vague prior corresponds to $\kappa_0 = 0$, $\nu_0 = -1$ and $\Lambda_0 = 0$, so the posterior distribution of the mean is $t_{n - 2}(\bar y, S / (n (n - 2)))$ without external data, where $S$ is the matrix of sums of squares and cross-products, $n - 1$ times the sample covariance matrix (Yamaguchi et al., submitted, equation A.9).

The treatment effects are the differences of the means, $\theta_k = \mu_{tk} - \mu_{ck}$. The mean of each group has a bivariate t distribution with $\nu_n - 1$ degrees of freedom, location $\mu_n$ and scale matrix $\Lambda_n / (\kappa_n (\nu_n - 1))$. For a predictive criterion, the mean of $m$ future subjects has the same distribution with the scale matrix $(1 / \kappa_n + 1 / m) \Lambda_n / (\nu_n - 1)$, because the future subjects share the same mean and covariance matrix (version 0.1.0 and Yamaguchi et al. used the scale matrix $(1 + \kappa_n) \Lambda_n / (\kappa_n (\nu_n - 1) m)$, which treats them as independent draws from the predictive distribution of one subject). A hypothetical control (hypothetical(mean = c(m1, m2), r = r)) gives the control mean the distribution of the treatment mean with location mean and the scale matrix multiplied by r (Yamaguchi et al., Section A.2.6, with the hyperparameters of the treatment group). With external control data only (n_c = 0 and external_c) and the vague prior, $a_0 n_e$ must be greater than 2. A fixed control (n_c = 0 and fixed_c, the known control means of the two endpoints) shifts the distribution of the treatment mean, so the effect has a bivariate t distribution and the probabilities of the regions are computed exactly from its distribution function; method, nMC and m_c are then not used, and prior_c must be NULL.

The probabilities of the regions are computed by one of two methods:

"MM"

moment-matching approximation: the effect is approximated by a bivariate t distribution with the same mean, covariance matrix and radial fourth moment (Yamaguchi, Homma, Takeda and Maruo, submitted, Theorem 3), whose rectangle probabilities are computed by numerical integration for real degrees of freedom. It requires more than 4 degrees of freedom for both groups and is the fastest method.

"MC"

Monte Carlo simulation with nMC draws from each distribution; the probabilities of all regions are estimated from the same draws. The computing time is proportional to nMC, so "MM" is much faster for operating characteristics, which need the probabilities for many simulated studies.

The operating characteristics are computed by simulating the sample means and the sums of squares and cross-products of nsim studies (see qdm_oc).

Value

An object of class c("qdm_design_continuous2", "qdm_design"), a list with the arguments and the element control ("concurrent", "external", "hypothetical" or "fixed").

References

Yamaguchi, Y., Hu, H., Huang, Y., Han, C., Homma, G. and Wang, A. (submitted). Bivariate Bayesian quantitative decision-making for two endpoints in rare disease drug development.

Yamaguchi, Y., Homma, G., Takeda, K. and Maruo, K. (submitted). Moment-matching approximation for t-differences in clinical trials with continuous endpoints.

See Also

Other designs: design_binary(), design_binary2(), design_continuous(), design_tte()

Examples

# Case study 2 of Yamaguchi et al. (submitted)
L0 <- matrix(c(80^2, 15, 15, 6^2), 2, 2)
des <- design_continuous2(
  n_t = 30, n_c = 30,
  prior_t = prior_niw(mu0 = c(0, 1), kappa0 = 0.1, nu0 = 4, Lambda0 = L0),
  prior_c = prior_niw(mu0 = c(-10, -1), kappa0 = 0.1, nu0 = 4,
                      Lambda0 = L0),
  criterion = criterion_posterior(theta_tv = c(10, 2),
                                  theta_mav = c(5, 1),
                                  gamma_go = 0.296, gamma_nogo = 0.244)
)
des
qdm_decide(des,
           data_t = data_continuous2(30, c(5, 2),
                                     matrix(c(6400, 190, 190, 36), 2, 2)),
           data_c = data_continuous2(30, c(-10, -1),
                                     matrix(c(5600, 150, 150, 42), 2, 2)))

Description

Specifies a proof-of-concept study with one time-to-event endpoint evaluated by the milestone survival probability or by the hazard ratio: the sample sizes, the prior distributions, the external data, the form of the control group, the decision criterion, the effect measure and the conduct of the study. The design is used by qdm_decide, qdm_oc, qdm_calibrate and qdm_boundary.

Usage

design_tte(
  n_t,
  n_c,
  prior_t,
  prior_c = NULL,
  criterion,
  milestone = NULL,
  effect = c("milestone", "hazard_ratio"),
  conduct = NULL,
  external_t = NULL,
  external_c = NULL,
  hypothetical_c = NULL,
  fixed_c = NULL,
  conduct_future = conduct,
  nMC = 10000L
)

Arguments

Details

The event times of group $k$ ($k = t, c$) are exponential with hazard $\lambda_k$, which has a Gamma prior distribution (prior_gamma). The data of a group are summarized by the number of events $d$ and the total observed time $E$ (data_tte), and the posterior distribution of the hazard is Gamma with shape $a + d$ and rate $b + E$. External data enter through a power prior with weight $a_0$, which adds $a_0 d_e$ to the shape and $a_0 E_e$ to the rate. The treatment effect is either

effect = "milestone"

the difference of the milestone survival probabilities at time $t_0 =$ milestone, $\theta = S_t(t_0) - S_c(t_0)$ with $S_k(t_0) = \exp(-\lambda_k t_0)$, or

effect = "hazard_ratio"

the hazard ratio $\theta = \lambda_t / \lambda_c$, as in Huang et al. (2019), whose decision rules are based on the normal approximation of the estimated log hazard ratio. Smaller hazard ratios are favorable, so the criterion is usually specified with direction = "less", for example theta_tv = 0.67 and theta_mav = 0.8. Under the exponential model the hazard ratio is also the ratio of the median survival times of the control and treatment groups.

The control group takes one of four forms.

Concurrent control

n_c is at least 1. External control data can be added through external_c.

External control only

n_c = 0 and external_c is given. The distribution of $\lambda_c$ is the posterior distribution given the external control data.

Hypothetical control

n_c = 0 and hypothetical_c is given by hypothetical(data_tte(events, exposure)), whose events and total observed time are added to prior_c.

Fixed control

n_c = 0 and fixed_c is given: the control milestone survival probability (effect = "milestone") or the control median survival time (effect = "hazard_ratio", control hazard $\log(2)$ / fixed_c) is a known value, as in a single-arm study compared with a benchmark (with the milestone survival, fixed_c = 0 makes the effect the treatment milestone survival itself). prior_c is not used and must be NULL.

Posterior probabilities of the Go and NoGo regions are computed exactly for a fixed control (from the Gamma distribution of $\lambda_t$). With another control, they are computed by numerical integration for the milestone survival (see psurvdiff) and exactly from the F distribution for the hazard ratio (see phazardratio).

For the posterior predictive criterion (criterion_predictive), a future study with m_t treatment and m_c control subjects (no control group for a fixed control) is conducted as specified by conduct_future. The hazard of each group in the future study is estimated by $(a + d) / (b + E)$, the posterior mean of the hazard given the future data alone with the prior $(a, b)$ of the group (prior_t or prior_c); with a weak prior this is close to the maximum likelihood estimate $d / E$ and, unlike it, is positive when a group has no events, so that the hazard ratio is defined. The milestone survival is estimated by $\exp(-t_0 (a + d) / (b + E))$. The future effect is the difference of the two milestone survival estimates (or the treatment estimate minus the fixed value), or the ratio of the two hazard estimates (or the treatment estimate divided by the fixed control hazard). The predictive probabilities are estimated by simulating nMC future studies, each with hazards drawn from the posterior distributions given the proof-of-concept data (see conduct_tte for the simulation of a study). They are therefore Monte Carlo estimates, and their computing time is proportional to nMC times the number of future subjects.

The operating characteristics (qdm_oc, qdm_calibrate) are computed by simulating nsim studies conducted as specified by conduct, with exponential event times whose hazards are given by the median survival times of the scenarios. With a predictive criterion, every simulated study requires nMC simulated future studies, so small values of nsim and nMC are advisable for exploration.

Value

An object of class c("qdm_design_tte", "qdm_design"), a list with the arguments and the element control ("concurrent", "external", "hypothetical" or "fixed").

References

Huang, B., Talukder, E., Han, L. and Kuan, P.-F. (2019). Quantitative decision-making in randomized Phase II studies with a time-to-event endpoint. Journal of Biopharmaceutical Statistics, 29(1), 189–202. doi:10.1080/10543406.2018.1489400

See Also

Other designs: design_binary(), design_binary2(), design_continuous(), design_continuous2()

Examples

# Single-arm study compared with a fixed control 3-month survival of 0.72
des <- design_tte(
  n_t = 30, n_c = 0, prior_t = prior_gamma(0.1, 1),
  criterion = criterion_posterior(theta_tv = 0.15, theta_mav = 0,
                                  gamma_go = 0.6, gamma_nogo = 0.8),
  milestone = 3,
  conduct = conduct_tte(accrual_time = c(0, 5, 10, 15),
                        accrual_rate = c(0.4, 2, 4.5),
                        dropout_hazard = 0.00226, cut_time = 15.44),
  fixed_c = 0.72
)
des
qdm_decide(des, data_t = data_tte(events = 6, exposure = 150))

# Concurrent control
design_tte(
  n_t = 30, n_c = 30, prior_t = prior_gamma(0.1, 1),
  prior_c = prior_gamma(0.1, 1),
  criterion = criterion_posterior(theta_tv = 0.15, theta_mav = 0,
                                  gamma_go = 0.6, gamma_nogo = 0.8),
  milestone = 3,
  conduct = conduct_tte(accrual_time = c(0, 12), cut_events = 25)
)

# Hazard ratio in the randomized study of the example of Huang et al.
# (2019) (2:1 randomization, analysis at 127 events): Go if HR < 0.67
# with probability at least 0.5, NoGo if HR >= 0.8 with probability at
# least 0.6
design_tte(
  n_t = 112, n_c = 56, prior_t = prior_gamma(0.01, 0.01),
  prior_c = prior_gamma(0.01, 0.01),
  criterion = criterion_posterior(theta_tv = 0.67, theta_mav = 0.8,
                                  gamma_go = 0.5, gamma_nogo = 0.6,
                                  direction = "less"),
  effect = "hazard_ratio",
  conduct = conduct_tte(accrual_time = c(0, 13), dropout_hazard = 0.003,
                        cut_events = 127)
)

Description

Format an external data specification

Usage

## S3 method for class 'qdm_borrow'
format(x, digits = 4L, ...)

Arguments

Value

A character string describing the external data and the power prior weight.

Examples

format(borrow(data_binary(n = 20, y = 8), a0 = 0.5))

Description

Formats the decision intervals of qdm_boundary as tables with one row per condition and one column per decision: the range of the observed quantity (numbers of responders or events, or observed means), and the corresponding observed response rates (binary endpoint) or milestone survival probabilities (time-to-event endpoint) and observed treatment effects.

Usage

## S3 method for class 'qdm_boundary'
format(x, digits = 4L, n = 20L, ...)

Arguments

Value

A character vector with one element per line of the tables (print.qdm_boundary prints them). For rows subset from the object, which no longer have its attributes, the formatted data frame.

Examples

des <- design_binary(
  n_t = 20, n_c = 0, prior_t = prior_beta(1, 1),
  criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.05,
                                  gamma_go = 0.8, gamma_nogo = 0.8),
  fixed_c = 0.2
)
format(qdm_boundary(des, n_t = c(10, 20)))

Description

Format a calibration

Usage

## S3 method for class 'qdm_calibration'
format(x, digits = 4L, ...)

Arguments

Value

A character vector with one element per line of the description (without the table of decision probabilities).

Examples

des <- design_binary(
  n_t = 12, n_c = 12,
  prior_t = prior_beta(0.5, 0.5), prior_c = prior_beta(0.5, 0.5),
  criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
                                  gamma_go = 0.5, gamma_nogo = 0.8)
)
format(qdm_calibrate(des, data.frame(pi_t = 0.4, pi_c = 0.4),
                     data.frame(pi_t = 0.7, pi_c = 0.4),
                     target_go = 0.05, target_nogo = 0.2))

Description

Format the conduct of a study

Usage

## S3 method for class 'qdm_conduct'
format(x, digits = 4L, ...)

Arguments

Value

A character vector with one element per line of the description.

Examples

format(conduct_tte(accrual_time = c(0, 12), cut_events = 20))

Description

Format a decision criterion

Usage

## S3 method for class 'qdm_criterion'
format(x, digits = 4L, ...)

Arguments

Value

A character vector with one element per line of the description.

Examples

format(criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
                           gamma_go = 0.5, gamma_nogo = 0.8))

Description

Format a data specification

Usage

## S3 method for class 'qdm_data'
format(x, digits = 4L, ...)

Arguments

Value

A character string describing the data.

Examples

format(data_binary(n = 12, y = 7))

Description

Format a decision

Usage

## S3 method for class 'qdm_decision'
format(x, digits = 4L, ...)

Arguments

Value

A character vector with one element per line of the description.

Examples

des <- design_binary(
  n_t = 12, n_c = 12,
  prior_t = prior_beta(0.5, 0.5), prior_c = prior_beta(0.5, 0.5),
  criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
                                  gamma_go = 0.5, gamma_nogo = 0.8)
)
format(qdm_decide(des, data_binary(12, 9), data_binary(12, 4)))

Description

Format a design

Usage

## S3 method for class 'qdm_design'
format(x, digits = 4L, ...)

Arguments

Value

A character vector with one element per line of the description.

Examples

des <- design_binary(
  n_t = 12, n_c = 12,
  prior_t = prior_beta(0.5, 0.5), prior_c = prior_beta(0.5, 0.5),
  criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
                                  gamma_go = 0.5, gamma_nogo = 0.8)
)
format(des)

Description

Format a hypothetical control specification

Usage

## S3 method for class 'qdm_hypothetical'
format(x, digits = 4L, ...)

Arguments

Value

A character string describing the hypothetical control.

Examples

format(hypothetical(mean = 1, r = 1))

Description

Format a prior specification

Usage

## S3 method for class 'qdm_prior'
format(x, digits = 4L, ...)

Arguments

Value

A character string describing the prior distribution.

Examples

format(prior_beta(0.5, 0.5))

Description

Specifies the distribution of a hypothetical control group, used when the proof-of-concept study has no concurrent control group (Kang et al., 2026).

Usage

hypothetical(data = NULL, mean = NULL, r = NULL)

Arguments

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 $\mu_c \sim t_{\nu_{nt}}(\mu_{0c}, r \sigma_{nt}^2 / \kappa_{nt})$.

Value

An object of class "qdm_hypothetical" with elements type ("data" or "location"), data, mean and r.

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))

# Continuous endpoint: control mean located at 1 with the same scale
hypothetical(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))

Description

Computes $\Pr(Y_t / m_t - Y_c / m_c \le q)$ (or the upper tail $\Pr(Y_t / m_t - Y_c / m_c > q)$) for independent $Y_t \sim \textrm{Beta-binomial}(m_t, a_t, b_t)$ and $Y_c \sim \textrm{Beta-binomial}(m_c, a_c, b_c)$. This is the posterior predictive probability for the difference in response proportions of a future study with $m_t$ and $m_c$ subjects.

Usage

pbetabinomdiff(q, m_t, a_t, b_t, m_c, a_c, b_c, lower.tail = TRUE)

Arguments

Details

The probability is an exact finite sum. The probability mass functions are computed on the log scale, so large values of $m_t$ and $m_c$ do not cause overflow. Each difference is compared with q after a single division of the exact integer numerator $m_c Y_t - m_t Y_c$ by $m_t m_c$, so differences that are mathematically equal to a decimal q are classified correctly (they belong to the lower tail).

Value

A numeric vector of probabilities. The arguments q, a_t, b_t, a_c and b_c are recycled to a common length; each must have length 1 or the common length.

See Also

Other distributions: cellprob_binary2(), pbetadiff(), phazardratio(), psurvdiff(), ptdiff(), rdirichlet()

Examples

# Predictive probability that the future difference exceeds 0, with
# Jeffreys priors, 7/12 versus 4/12 responders and 20 future subjects per
# group
pbetabinomdiff(0, m_t = 20, a_t = 7.5, b_t = 5.5, m_c = 20, a_c = 4.5,
               b_c = 8.5, lower.tail = FALSE)

Description

Computes $\Pr(\pi_t - \pi_c \le q)$ (or the upper tail $\Pr(\pi_t - \pi_c > q)$) for independent $\pi_t \sim \textrm{Beta}(a_t, b_t)$ and $\pi_c \sim \textrm{Beta}(a_c, b_c)$. This is the posterior probability used for a single binary endpoint.

Usage

pbetadiff(q, a_t, b_t, a_c, b_c, lower.tail = TRUE)

Arguments

Details

The upper tail is computed as

$\Pr(\pi_t - \pi_c > q) = \Pr(\pi_c < -q) + \int f_c(x) \Pr(\pi_t > x + q) \, dx,$

where the integral runs over the values of $x$ with $0 \le x + q \le 1$, and the first term is zero for $q \ge 0$. The integral is evaluated by adaptive Gauss-Kronrod quadrature (the QUADPACK routine used by integrate), with the range split at quantiles of both Beta distributions so that concentrated distributions are integrated accurately. When the density of $\pi_c$ is unbounded at 0 ($a_c < 1$) or at 1 ($b_c < 1$), the integral on that side of the median of $\pi_c$ is evaluated in the variable $s = x^{a_c}$ or $s = (1 - x)^{b_c}$, in which the integrand is bounded. The lower tail is computed directly from the same formula with the roles of the two groups exchanged, so both tails are accurate near 0.

Value

A numeric vector of probabilities. All arguments except lower.tail are recycled to a common length; each must have length 1 or the common length.

See Also

Other distributions: cellprob_binary2(), pbetabinomdiff(), phazardratio(), psurvdiff(), ptdiff(), rdirichlet()

Examples

# Posterior probability that the response rate difference exceeds 0.1,
# with Jeffreys priors and 7/12 versus 4/12 responders
pbetadiff(0.1, a_t = 7.5, b_t = 5.5, a_c = 4.5, b_c = 8.5,
          lower.tail = FALSE)

Description

Computes $\Pr(\lambda_t / \lambda_c \le q)$ (or the upper tail $\Pr(\lambda_t / \lambda_c > q)$) for hazards with independent Gamma distributions, $\lambda_t \sim \textrm{Gamma}(a_t, b_t)$ and $\lambda_c \sim \textrm{Gamma}(a_c, b_c)$ (shape and rate). This is the posterior probability used for a time-to-event endpoint evaluated by the hazard ratio with a concurrent, external or hypothetical control.

Usage

phazardratio(q, shape_t, rate_t, shape_c, rate_c, lower.tail = TRUE)

Arguments

Details

Because $2 b_k \lambda_k$ has the chi-squared distribution with $2 a_k$ degrees of freedom, $(b_t \lambda_t / a_t) / (b_c \lambda_c / a_c)$ has the F distribution with $2 a_t$ and $2 a_c$ degrees of freedom, and

$\Pr(\lambda_t / \lambda_c \le q) = F_{2 a_t, 2 a_c}\left(\frac{q \, a_c \, b_t}{a_t \, b_c}\right),$

which is computed exactly by pf.

Value

A numeric vector of probabilities. All arguments except lower.tail are recycled to a common length; each must have length 1 or the common length.

See Also

Other distributions: cellprob_binary2(), pbetabinomdiff(), pbetadiff(), psurvdiff(), ptdiff(), rdirichlet()

Examples

# Posterior probability that the hazard ratio is below 0.7 with 20 events
# in 300 months of follow-up versus 30 events in 250 months (Gamma(0.1, 1)
# priors)
phazardratio(0.7, shape_t = 20.1, rate_t = 301, shape_c = 30.1,
             rate_c = 251)

Description

Draws the decision intervals computed by qdm_boundary as regions filled by the decision (Go, NoGo, Gray, or Miss if it occurs): the horizontal axis is the condition (for example, the number of control responders or the treatment group size) and the vertical axis is the observed quantity (numbers of responders or events, or the observed treatment mean) or, for continuous endpoints with a concurrent control group, the observed difference in means. The boundary values are written on the Go and NoGo regions next to the boundaries, for example the smallest number of responders that leads to a Go decision. With a concurrent control group and a binary endpoint, the observed response rate of the treatment group is shown on a secondary axis.

Usage

## S3 method for class 'qdm_boundary'
plot(
  x,
  col_go = "#658D1B",
  col_nogo = "#D91E49",
  col_gray = "#939597",
  col_miss = "#E69F00",
  labels = TRUE,
  label_size = NULL,
  base_size = 11,
  legend_position = "bottom",
  ...
)

Arguments

Details

The plot is a ggplot object, so it can be changed further by adding ggplot2 functions, for example ggplot2::labs(), ggplot2::theme() and ggplot2::scale_fill_manual().

Value

A ggplot object.

Examples

des <- design_binary(
  n_t = 12, n_c = 12,
  prior_t = prior_beta(0.5, 0.5), prior_c = prior_beta(0.5, 0.5),
  criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
                                  gamma_go = 0.5, gamma_nogo = 0.8)
)
b <- qdm_boundary(des)
plot(b)
plot(b, labels = c(0, 4, 8), col_gray = "gray80", base_size = 14,
     legend_position = "right")

Description

Draws the probability that the Go criterion is met under scenario_go and the probability that the NoGo criterion is met under scenario_nogo against the threshold, with the targets (horizontal lines) and the chosen thresholds (vertical lines).

Usage

## S3 method for class 'qdm_calibration'
plot(
  x,
  col_go = "#658D1B",
  col_nogo = "#D91E49",
  linewidth = 1,
  base_size = 11,
  legend_position = "bottom",
  ...
)

Arguments

Details

The plot is a ggplot object, so it can be changed further by adding ggplot2 functions, for example ggplot2::labs() and ggplot2::theme().

Value

A ggplot object.

Examples

des <- design_binary(
  n_t = 12, n_c = 12,
  prior_t = prior_beta(0.5, 0.5), prior_c = prior_beta(0.5, 0.5),
  criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
                                  gamma_go = 0.5, gamma_nogo = 0.8)
)
cal <- qdm_calibrate(des, data.frame(pi_t = 0.4, pi_c = 0.4),
                     data.frame(pi_t = 0.7, pi_c = 0.4),
                     target_go = 0.05, target_nogo = 0.2)
plot(cal)
plot(cal, linewidth = 0.6, base_size = 14, legend_position = "top")

Description

Draws the probabilities of Go, NoGo and Gray decisions (and Miss decisions if they have positive probability) against the true treatment effect of the scenarios. For a time-to-event endpoint, the effect is the difference of the milestone survival probabilities $\exp(-\log(2) t_0 / m)$ given by the median survival times $m$ of the scenarios. For a design without a concurrent control group, the horizontal axis is the true response rate (binary endpoint), mean (continuous endpoint) or milestone survival probability (time-to-event endpoint) of the treatment group, and the thresholds of the criterion are shifted by the fixed control value, or otherwise by the mean (binary and time-to-event) or location (continuous) of the distribution of the control group. The scenarios must have distinct values on the horizontal axis, so scenarios with the same effect (for example, with several standard deviations or control parameters) are plotted separately.

Usage

## S3 method for class 'qdm_oc'
plot(
  x,
  col_go = "#658D1B",
  col_nogo = "#D91E49",
  col_gray = "#939597",
  col_miss = "#E69F00",
  linewidth = 1,
  point_size = 1.5,
  label_size = NULL,
  base_size = 11,
  legend_position = "bottom",
  overlay = FALSE,
  cell_values = TRUE,
  ...
)

Arguments

Details

For two endpoints, the plot has one heat map per decision, with the true treatment effects of the two endpoints (or the true parameters of the treatment group without a concurrent control group) on the axes and the probability of the decision in each cell; scenarios that are not evaluable are marked "NE". The scenarios must have distinct values on the axes, so scenarios with several correlations (or several parameters of the control group) are plotted separately. Each panel is shaded from white (probability 0) to the color of its decision (probability 1); the heat maps have no legend. With overlay = TRUE, a single heat map is drawn instead: each cell has the color of the decision with the highest probability (Go, Gray or NoGo, or Miss if it occurs; ties go to the first in this order), shaded in five steps of that probability (below 0.5, 0.5 to 0.6, 0.6 to 0.7, 0.7 to 0.8, and 0.8 or more), with legends for the decisions and the steps (shown in gray whatever the colors of the decisions), as in the heat maps of Yamaguchi et al. (submitted, Figure 2).

The plot is a ggplot object, so it can be changed further by adding ggplot2 functions, for example ggplot2::labs() for the title and the axis labels, ggplot2::theme() for the text and the legend, and ggplot2::scale_colour_manual() or ggplot2::scale_linetype_manual() for the curves of one endpoint. The colors of the heat maps are set by the color arguments.

Value

A ggplot object.

References

Yamaguchi, Y., Hu, H., Huang, Y., Han, C., Homma, G. and Wang, A. (submitted). Bivariate Bayesian quantitative decision-making for two endpoints in rare disease drug development.

Examples

des <- design_binary(
  n_t = 12, n_c = 12,
  prior_t = prior_beta(0.5, 0.5), prior_c = prior_beta(0.5, 0.5),
  criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
                                  gamma_go = 0.5, gamma_nogo = 0.8)
)
oc <- qdm_oc(des, data.frame(pi_t = seq(0.2, 0.9, by = 0.05), pi_c = 0.4))
plot(oc)

# Other colors, thinner lines, larger text and the legend on the right,
# with a title added by ggplot2
plot(oc, col_go = "darkgreen", col_nogo = "firebrick", linewidth = 0.6,
     base_size = 14, legend_position = "right") +
  ggplot2::labs(title = "Operating characteristics")

# Two binary endpoints: one heat map per decision, or a single heat map
# of the decision with the highest probability in each cell
prior <- prior_dirichlet(rep(0.25, 4))
des2 <- design_binary2(
  n_t = 6, n_c = 6, prior_t = prior, prior_c = prior,
  criterion = criterion_posterior(theta_tv = c(0.15, 0.15),
                                  theta_mav = c(0.1, 0.1),
                                  gamma_go = 0.6, gamma_nogo = 0.6),
  nMC = 500
)
eff <- expand.grid(theta1 = c(0, 0.2, 0.4), theta2 = c(0, 0.2, 0.4))
oc2 <- qdm_oc(des2, data.frame(pi_t1 = 0.2 + eff$theta1,
                               pi_t2 = 0.2 + eff$theta2, rho_t = 0.3,
                               pi_c1 = 0.2, pi_c2 = 0.2, rho_c = 0.3),
              seed = 1)
plot(oc2)
plot(oc2, overlay = TRUE)
plot(oc2, overlay = TRUE, cell_values = FALSE)