getgamma1cont: Find Optimal Go/NoGo Thresholds for a Single Continuous...
In BayesianQDM: Bayesian Quantitative Decision-Making Framework for Binary and Continuous Endpoints
View source: R/getgamma1cont.R
getgamma1cont R Documentation
Find Optimal Go/NoGo Thresholds for a Single Continuous Endpoint
Description
Computes the optimal Go threshold \gamma_{\mathrm{go}} and NoGo
threshold \gamma_{\mathrm{nogo}} for a single continuous endpoint by
searching over a grid of candidate values. The two thresholds are
calibrated independently under separate scenarios:
-
\gamma_{\mathrm{go}} is the smallest value in
gamma_grid such that the marginal Go probability
\Pr(g_{\mathrm{Go}} \ge \gamma_{\mathrm{go}}) is strictly
less than target_go under the Go-calibration scenario
(mu_t_go, mu_c_go, sigma_t_go,
sigma_c_go); typically the Null scenario.
-
\gamma_{\mathrm{nogo}} is the smallest value in
gamma_grid such that the marginal NoGo probability
\Pr(g_{\mathrm{NoGo}} \ge \gamma_{\mathrm{nogo}}) is
strictly less than target_nogo under the NoGo-calibration
scenario (mu_t_nogo, mu_c_nogo, sigma_t_nogo,
sigma_c_nogo); typically the Alternative scenario.
Here g_{\mathrm{Go}} = P(\theta > \theta_{\mathrm{TV}} \mid \bar{y}_t, s_t, \bar{y}_c, s_c)
and g_{\mathrm{NoGo}} = P(\theta \le \theta_{\mathrm{MAV}} \mid \bar{y}_t, s_t, \bar{y}_c, s_c)
for prob = 'posterior', consistent with the decision rule in
pbayesdecisionprob1cont.
Usage
getgamma1cont(
nsim,
prob = "posterior",
design = "controlled",
prior = "vague",
CalcMethod = "NI",
theta_TV = NULL,
theta_MAV = NULL,
theta_NULL = NULL,
nMC = NULL,
mu_t_go,
mu_c_go = NULL,
sigma_t_go,
sigma_c_go = NULL,
mu_t_nogo,
mu_c_nogo = NULL,
sigma_t_nogo,
sigma_c_nogo = NULL,
target_go,
target_nogo,
n_t,
n_c = NULL,
m_t = NULL,
m_c = NULL,
kappa0_t = NULL,
kappa0_c = NULL,
nu0_t = NULL,
nu0_c = NULL,
mu0_t = NULL,
mu0_c = NULL,
sigma0_t = NULL,
sigma0_c = NULL,
r = NULL,
ne_t = NULL,
ne_c = NULL,
alpha0e_t = NULL,
alpha0e_c = NULL,
bar_ye_t = NULL,
bar_ye_c = NULL,
se_t = NULL,
se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01),
seed
)
Arguments
nsim
A positive integer giving the number of Monte Carlo simulation
replicates used to approximate the operating characteristics.
prob
A character string specifying the probability type.
Must be 'posterior' or 'predictive'.
design
A character string specifying the trial design.
Must be 'controlled', 'uncontrolled', or
'external'.
prior
A character string specifying the prior distribution.
Must be 'vague' or 'N-Inv-Chisq'.
CalcMethod
A character string specifying the computation method
for pbayespostpred1cont.
Must be 'NI', 'MC', or 'MM'.
theta_TV
A numeric scalar giving the Target Value (TV) threshold
for the treatment effect. Required when prob = 'posterior';
set to NULL otherwise.
theta_MAV
A numeric scalar giving the Minimum Acceptable Value (MAV)
threshold. Must satisfy theta_TV > theta_MAV. Required when
prob = 'posterior'; set to NULL otherwise.
theta_NULL
A numeric scalar giving the null hypothesis threshold
for the predictive probability. Required when
prob = 'predictive'; set to NULL otherwise.
nMC
A positive integer giving the number of inner Monte Carlo draws
passed to pbayespostpred1cont. Required when
CalcMethod = 'MC'; set to NULL otherwise.
mu_t_go
A numeric scalar giving the true mean for the treatment group
under the Go-calibration scenario (typically Null).
mu_c_go
A numeric scalar giving the true mean for the control group
under the Go-calibration scenario. Set to NULL for
design = 'uncontrolled'.
sigma_t_go
A positive numeric scalar giving the true standard
deviation for the treatment group under the Go-calibration scenario.
sigma_c_go
A positive numeric scalar giving the true standard
deviation for the control group under the Go-calibration scenario.
Set to NULL for design = 'uncontrolled'.
mu_t_nogo
A numeric scalar giving the true mean for the treatment
group under the NoGo-calibration scenario (typically Alternative).
mu_c_nogo
A numeric scalar giving the true mean for the control
group under the NoGo-calibration scenario. Set to NULL for
design = 'uncontrolled'.
sigma_t_nogo
A positive numeric scalar giving the true standard
deviation for the treatment group under the NoGo-calibration scenario.
sigma_c_nogo
A positive numeric scalar giving the true standard
deviation for the control group under the NoGo-calibration scenario.
Set to NULL for design = 'uncontrolled'.
target_go
A numeric scalar in (0, 1) giving the upper bound
on the marginal Go probability under the Go-calibration scenario.
The optimal \gamma_{\mathrm{go}} is the smallest grid value
satisfying \Pr(\mathrm{Go}) < \code{target\_go}.
target_nogo
A numeric scalar in (0, 1) giving the upper bound
on the marginal NoGo probability under the NoGo-calibration scenario.
The optimal \gamma_{\mathrm{nogo}} is the smallest grid value
satisfying \Pr(\mathrm{NoGo}) < \code{target\_nogo}.
n_t
A positive integer giving the number of patients in the
treatment group in the PoC trial.
n_c
A positive integer giving the number of patients in the
control group in the PoC trial. Required for
design = 'controlled' or 'external'; set to
NULL for design = 'uncontrolled'.
m_t
A positive integer giving the future sample size for the
treatment group. Required when prob = 'predictive';
set to NULL otherwise.
m_c
A positive integer giving the future sample size for the
control group. Required when prob = 'predictive';
set to NULL otherwise.
kappa0_t
A positive numeric scalar giving the prior precision
parameter for the treatment group under the N-Inv-Chi-squared prior.
Required when prior = 'N-Inv-Chisq'; set to NULL otherwise.
kappa0_c
A positive numeric scalar giving the prior precision
parameter for the control group. Required when
prior = 'N-Inv-Chisq' and design != 'uncontrolled';
set to NULL otherwise.
nu0_t
A positive numeric scalar giving the prior degrees of freedom
for the treatment group under the N-Inv-Chi-squared prior. Required
when prior = 'N-Inv-Chisq'; set to NULL otherwise.
nu0_c
A positive numeric scalar giving the prior degrees of freedom
for the control group. Required when prior = 'N-Inv-Chisq'
and design != 'uncontrolled'; set to NULL otherwise.
mu0_t
A numeric scalar giving the prior mean for the treatment group
under the N-Inv-Chi-squared prior. Required when
prior = 'N-Inv-Chisq'; set to NULL otherwise.
mu0_c
A numeric scalar giving the prior mean for the control group
(or the fixed hypothetical control mean for uncontrolled design).
Required when prior = 'N-Inv-Chisq'; set to NULL
otherwise.
sigma0_t
A positive numeric scalar giving the prior scale parameter
for the treatment group under the N-Inv-Chi-squared prior. Required
when prior = 'N-Inv-Chisq'; set to NULL otherwise.
sigma0_c
A positive numeric scalar giving the prior scale parameter
for the control group. Required when prior = 'N-Inv-Chisq'
and design != 'uncontrolled'; set to NULL otherwise.
r
A positive numeric scalar giving the variance ratio
\sigma_c^2 / \sigma_t^2 used for uncontrolled design.
Required when design = 'uncontrolled'; set to NULL
otherwise.
ne_t
A positive integer giving the number of patients in the
treatment group of the external data set. Required when
design = 'external' and external treatment data are
available; otherwise set to NULL.
ne_c
A positive integer giving the number of patients in the
control group of the external data set. Required when
design = 'external' and external control data are available;
otherwise set to NULL.
alpha0e_t
A numeric scalar in (0, 1] giving the power prior
weight for external treatment group data. Required when
design = 'external' and ne_t is non-NULL; set to
NULL otherwise.
alpha0e_c
A numeric scalar in (0, 1] giving the power prior
weight for external control group data. Required when
design = 'external' and ne_c is non-NULL; set to
NULL otherwise.
bar_ye_t
A numeric scalar giving the sample mean of the external
treatment group data. Required when ne_t is non-NULL; set to
NULL otherwise.
bar_ye_c
A numeric scalar giving the sample mean of the external
control group data. Required when ne_c is non-NULL; set to
NULL otherwise.
se_t
A positive numeric scalar giving the sample standard deviation
of the external treatment group data. Required when ne_t is
non-NULL; set to NULL otherwise.
se_c
A positive numeric scalar giving the sample standard deviation
of the external control group data. Required when ne_c is
non-NULL; set to NULL otherwise.
gamma_grid
A numeric vector of candidate threshold values in
(0, 1) to search over. Defaults to
seq(0.01, 0.99, by = 0.01).
seed
A numeric scalar for reproducible random number generation.
The Go-calibration simulation uses seed and the
NoGo-calibration simulation uses seed + 1 to ensure
independence between the two scenarios.
Details
The function uses a two-stage simulate-then-sweep strategy:
-
Simulation: nsim datasets are generated
independently for each calibration scenario. For the
Go-calibration scenario, standardised residuals are shifted by
mu_t_go and mu_c_go; for the NoGo-calibration
scenario, by mu_t_nogo and mu_c_nogo.
pbayespostpred1cont is called once per scenario to
obtain g_{\mathrm{Go}} (lower.tail = FALSE at
theta_TV) and g_{\mathrm{NoGo}} (lower.tail
= TRUE at theta_MAV).
-
Gamma sweep: Marginal probabilities are estimated as the
proportion of simulated datasets satisfying the respective
indicator: g_{\mathrm{Go}} \ge \gamma for
PrGo_grid, and g_{\mathrm{NoGo}} \ge \gamma for
PrNoGo_grid. Both are monotone non-increasing in
\gamma.
The optimal \gamma_{\mathrm{go}} and \gamma_{\mathrm{nogo}}
are each the smallest grid value crossing below the respective
target probability.
Value
A list of class getgamma1cont with the following elements:
- gamma_go
Optimal Go threshold: the smallest value in
gamma_grid for which \Pr(\mathrm{Go}) < \code{target\_go}
under the Go-calibration scenario.
NA if no such value exists.
- gamma_nogo
Optimal NoGo threshold: the smallest value in
gamma_grid for which \Pr(\mathrm{NoGo}) < \code{target\_nogo}
under the NoGo-calibration scenario.
NA if no such value exists.
- PrGo_opt
Marginal \Pr(g_{\mathrm{Go}} \ge \gamma_{\mathrm{go}})
at the optimal \gamma_{\mathrm{go}} under the Go-calibration
scenario. NA if gamma_go is NA.
- PrNoGo_opt
Marginal \Pr(g_{\mathrm{NoGo}} \ge \gamma_{\mathrm{nogo}})
at the optimal \gamma_{\mathrm{nogo}} under the NoGo-calibration
scenario. NA if gamma_nogo is NA.
- target_go
The value of target_go supplied by the user.
- target_nogo
The value of target_nogo supplied by the user.
- grid_results
A data frame with columns gamma_grid,
PrGo_grid (marginal Go probability under the Go-calibration
scenario), and PrNoGo_grid (marginal NoGo probability under
the NoGo-calibration scenario).
Examples
# Example 1: Controlled design, vague prior, posterior probability
# gamma_go : smallest gamma s.t. Pr(Go) < 0.05 under Null (mu_t = mu_c = 1.0)
# gamma_nogo: smallest gamma s.t. Pr(NoGo) < 0.20 under Alt (mu_t = 2.5, mu_c = 1.0)
getgamma1cont(
nsim = 1000L, prob = 'posterior', design = 'controlled',
prior = 'vague', CalcMethod = 'MM',
theta_TV = 1.5, theta_MAV = 0.0, theta_NULL = NULL, nMC = NULL,
mu_t_go = 1.0, mu_c_go = 1.0, sigma_t_go = 2.0, sigma_c_go = 2.0,
mu_t_nogo = 2.5, mu_c_nogo = 1.0, sigma_t_nogo = 2.0, sigma_c_nogo = 2.0,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = 15L, m_t = NULL, m_c = NULL,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = NULL, sigma0_t = NULL, sigma0_c = NULL,
r = NULL, ne_t = NULL, ne_c = NULL, alpha0e_t = NULL, alpha0e_c = NULL,
bar_ye_t = NULL, bar_ye_c = NULL, se_t = NULL, se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 1L
)
# Example 2: Uncontrolled design, N-Inv-Chisq prior, posterior probability
getgamma1cont(
nsim = 1000L, prob = 'posterior', design = 'uncontrolled',
prior = 'N-Inv-Chisq', CalcMethod = 'NI',
theta_TV = 1.0, theta_MAV = 0.0, theta_NULL = NULL, nMC = NULL,
mu_t_go = 1.5, mu_c_go = NULL, sigma_t_go = 1.5, sigma_c_go = NULL,
mu_t_nogo = 3.0, mu_c_nogo = NULL, sigma_t_nogo = 1.5, sigma_c_nogo = NULL,
target_go = 0.05, target_nogo = 0.20,
n_t = 20L, n_c = NULL, m_t = NULL, m_c = NULL,
kappa0_t = 2, kappa0_c = NULL, nu0_t = 5, nu0_c = NULL,
mu0_t = 3.0, mu0_c = 1.5, sigma0_t = 1.5, sigma0_c = NULL,
r = 1.0, ne_t = NULL, ne_c = NULL, alpha0e_t = NULL, alpha0e_c = NULL,
bar_ye_t = NULL, bar_ye_c = NULL, se_t = NULL, se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 2L
)
# Example 3: External design, vague prior, posterior probability
getgamma1cont(
nsim = 1000L, prob = 'posterior', design = 'external',
prior = 'vague', CalcMethod = 'MM',
theta_TV = 1.0, theta_MAV = 0.0, theta_NULL = NULL, nMC = NULL,
mu_t_go = 1.0, mu_c_go = 1.0, sigma_t_go = 1.5, sigma_c_go = 1.5,
mu_t_nogo = 2.5, mu_c_nogo = 1.0, sigma_t_nogo = 1.5, sigma_c_nogo = 1.5,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = 15L, m_t = NULL, m_c = NULL,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = NULL, sigma0_t = NULL, sigma0_c = NULL,
r = NULL, ne_t = NULL, ne_c = 20L, alpha0e_t = NULL, alpha0e_c = 0.5,
bar_ye_t = NULL, bar_ye_c = 0.0, se_t = NULL, se_c = 1.5,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 4L
)
# Example 4: Controlled design, vague prior, predictive probability
getgamma1cont(
nsim = 1000L, prob = 'predictive', design = 'controlled',
prior = 'vague', CalcMethod = 'MM',
theta_TV = NULL, theta_MAV = NULL, theta_NULL = 1.0, nMC = NULL,
mu_t_go = 1.0, mu_c_go = 1.0, sigma_t_go = 2.0, sigma_c_go = 2.0,
mu_t_nogo = 2.5, mu_c_nogo = 1.0, sigma_t_nogo = 2.0, sigma_c_nogo = 2.0,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = 15L, m_t = 50L, m_c = 50L,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = NULL, sigma0_t = NULL, sigma0_c = NULL,
r = NULL, ne_t = NULL, ne_c = NULL, alpha0e_t = NULL, alpha0e_c = NULL,
bar_ye_t = NULL, bar_ye_c = NULL, se_t = NULL, se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 3L
)
# Example 5: Uncontrolled design, vague prior, predictive probability
getgamma1cont(
nsim = 1000L, prob = 'predictive', design = 'uncontrolled',
prior = 'vague', CalcMethod = 'MM',
theta_TV = NULL, theta_MAV = NULL, theta_NULL = 1.0, nMC = NULL,
mu_t_go = 1.0, mu_c_go = NULL, sigma_t_go = 2.0, sigma_c_go = NULL,
mu_t_nogo = 2.5, mu_c_nogo = NULL, sigma_t_nogo = 2.0, sigma_c_nogo = NULL,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = NULL, m_t = 50L, m_c = 50L,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = 0.0, sigma0_t = NULL, sigma0_c = NULL,
r = 1.0, ne_t = NULL, ne_c = NULL, alpha0e_t = NULL, alpha0e_c = NULL,
bar_ye_t = NULL, bar_ye_c = NULL, se_t = NULL, se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 5L
)
# Example 6: External design, vague prior, predictive probability
getgamma1cont(
nsim = 1000L, prob = 'predictive', design = 'external',
prior = 'vague', CalcMethod = 'MM',
theta_TV = NULL, theta_MAV = NULL, theta_NULL = 1.0, nMC = NULL,
mu_t_go = 1.0, mu_c_go = 1.0, sigma_t_go = 1.5, sigma_c_go = 1.5,
mu_t_nogo = 2.5, mu_c_nogo = 1.0, sigma_t_nogo = 1.5, sigma_c_nogo = 1.5,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = 15L, m_t = 50L, m_c = 50L,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = NULL, sigma0_t = NULL, sigma0_c = NULL,
r = NULL, ne_t = NULL, ne_c = 20L, alpha0e_t = NULL, alpha0e_c = 0.5,
bar_ye_t = NULL, bar_ye_c = 0.0, se_t = NULL, se_c = 1.5,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 6L
)
BayesianQDM documentation built on April 22, 2026, 1:09 a.m.
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View source: R/getgamma1cont.R
| getgamma1cont | R Documentation |
Find Optimal Go/NoGo Thresholds for a Single Continuous Endpoint
Description
Computes the optimal Go threshold \gamma_{\mathrm{go}} and NoGo
threshold \gamma_{\mathrm{nogo}} for a single continuous endpoint by
searching over a grid of candidate values. The two thresholds are
calibrated independently under separate scenarios:
-
\gamma_{\mathrm{go}}is the smallest value ingamma_gridsuch that the marginal Go probability\Pr(g_{\mathrm{Go}} \ge \gamma_{\mathrm{go}})is strictly less thantarget_gounder the Go-calibration scenario (mu_t_go,mu_c_go,sigma_t_go,sigma_c_go); typically the Null scenario. -
\gamma_{\mathrm{nogo}}is the smallest value ingamma_gridsuch that the marginal NoGo probability\Pr(g_{\mathrm{NoGo}} \ge \gamma_{\mathrm{nogo}})is strictly less thantarget_nogounder the NoGo-calibration scenario (mu_t_nogo,mu_c_nogo,sigma_t_nogo,sigma_c_nogo); typically the Alternative scenario.
Here g_{\mathrm{Go}} = P(\theta > \theta_{\mathrm{TV}} \mid \bar{y}_t, s_t, \bar{y}_c, s_c)
and g_{\mathrm{NoGo}} = P(\theta \le \theta_{\mathrm{MAV}} \mid \bar{y}_t, s_t, \bar{y}_c, s_c)
for prob = 'posterior', consistent with the decision rule in
pbayesdecisionprob1cont.
Usage
getgamma1cont(
nsim,
prob = "posterior",
design = "controlled",
prior = "vague",
CalcMethod = "NI",
theta_TV = NULL,
theta_MAV = NULL,
theta_NULL = NULL,
nMC = NULL,
mu_t_go,
mu_c_go = NULL,
sigma_t_go,
sigma_c_go = NULL,
mu_t_nogo,
mu_c_nogo = NULL,
sigma_t_nogo,
sigma_c_nogo = NULL,
target_go,
target_nogo,
n_t,
n_c = NULL,
m_t = NULL,
m_c = NULL,
kappa0_t = NULL,
kappa0_c = NULL,
nu0_t = NULL,
nu0_c = NULL,
mu0_t = NULL,
mu0_c = NULL,
sigma0_t = NULL,
sigma0_c = NULL,
r = NULL,
ne_t = NULL,
ne_c = NULL,
alpha0e_t = NULL,
alpha0e_c = NULL,
bar_ye_t = NULL,
bar_ye_c = NULL,
se_t = NULL,
se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01),
seed
)
Arguments
nsim |
A positive integer giving the number of Monte Carlo simulation replicates used to approximate the operating characteristics. |
prob |
A character string specifying the probability type.
Must be |
design |
A character string specifying the trial design.
Must be |
prior |
A character string specifying the prior distribution.
Must be |
CalcMethod |
A character string specifying the computation method
for |
theta_TV |
A numeric scalar giving the Target Value (TV) threshold
for the treatment effect. Required when |
theta_MAV |
A numeric scalar giving the Minimum Acceptable Value (MAV)
threshold. Must satisfy |
theta_NULL |
A numeric scalar giving the null hypothesis threshold
for the predictive probability. Required when
|
nMC |
A positive integer giving the number of inner Monte Carlo draws
passed to |
mu_t_go |
A numeric scalar giving the true mean for the treatment group under the Go-calibration scenario (typically Null). |
mu_c_go |
A numeric scalar giving the true mean for the control group
under the Go-calibration scenario. Set to |
sigma_t_go |
A positive numeric scalar giving the true standard deviation for the treatment group under the Go-calibration scenario. |
sigma_c_go |
A positive numeric scalar giving the true standard
deviation for the control group under the Go-calibration scenario.
Set to |
mu_t_nogo |
A numeric scalar giving the true mean for the treatment group under the NoGo-calibration scenario (typically Alternative). |
mu_c_nogo |
A numeric scalar giving the true mean for the control
group under the NoGo-calibration scenario. Set to |
sigma_t_nogo |
A positive numeric scalar giving the true standard deviation for the treatment group under the NoGo-calibration scenario. |
sigma_c_nogo |
A positive numeric scalar giving the true standard
deviation for the control group under the NoGo-calibration scenario.
Set to |
target_go |
A numeric scalar in |
target_nogo |
A numeric scalar in |
n_t |
A positive integer giving the number of patients in the treatment group in the PoC trial. |
n_c |
A positive integer giving the number of patients in the
control group in the PoC trial. Required for
|
m_t |
A positive integer giving the future sample size for the
treatment group. Required when |
m_c |
A positive integer giving the future sample size for the
control group. Required when |
kappa0_t |
A positive numeric scalar giving the prior precision
parameter for the treatment group under the N-Inv-Chi-squared prior.
Required when |
kappa0_c |
A positive numeric scalar giving the prior precision
parameter for the control group. Required when
|
nu0_t |
A positive numeric scalar giving the prior degrees of freedom
for the treatment group under the N-Inv-Chi-squared prior. Required
when |
nu0_c |
A positive numeric scalar giving the prior degrees of freedom
for the control group. Required when |
mu0_t |
A numeric scalar giving the prior mean for the treatment group
under the N-Inv-Chi-squared prior. Required when
|
mu0_c |
A numeric scalar giving the prior mean for the control group
(or the fixed hypothetical control mean for uncontrolled design).
Required when |
sigma0_t |
A positive numeric scalar giving the prior scale parameter
for the treatment group under the N-Inv-Chi-squared prior. Required
when |
sigma0_c |
A positive numeric scalar giving the prior scale parameter
for the control group. Required when |
r |
A positive numeric scalar giving the variance ratio
|
ne_t |
A positive integer giving the number of patients in the
treatment group of the external data set. Required when
|
ne_c |
A positive integer giving the number of patients in the
control group of the external data set. Required when
|
alpha0e_t |
A numeric scalar in |
alpha0e_c |
A numeric scalar in |
bar_ye_t |
A numeric scalar giving the sample mean of the external
treatment group data. Required when |
bar_ye_c |
A numeric scalar giving the sample mean of the external
control group data. Required when |
se_t |
A positive numeric scalar giving the sample standard deviation
of the external treatment group data. Required when |
se_c |
A positive numeric scalar giving the sample standard deviation
of the external control group data. Required when |
gamma_grid |
A numeric vector of candidate threshold values in
|
seed |
A numeric scalar for reproducible random number generation.
The Go-calibration simulation uses |
Details
The function uses a two-stage simulate-then-sweep strategy:
-
Simulation:
nsimdatasets are generated independently for each calibration scenario. For the Go-calibration scenario, standardised residuals are shifted bymu_t_goandmu_c_go; for the NoGo-calibration scenario, bymu_t_nogoandmu_c_nogo.pbayespostpred1contis called once per scenario to obtaing_{\mathrm{Go}}(lower.tail = FALSEattheta_TV) andg_{\mathrm{NoGo}}(lower.tail = TRUEattheta_MAV). -
Gamma sweep: Marginal probabilities are estimated as the proportion of simulated datasets satisfying the respective indicator:
g_{\mathrm{Go}} \ge \gammaforPrGo_grid, andg_{\mathrm{NoGo}} \ge \gammaforPrNoGo_grid. Both are monotone non-increasing in\gamma.
The optimal \gamma_{\mathrm{go}} and \gamma_{\mathrm{nogo}}
are each the smallest grid value crossing below the respective
target probability.
Value
A list of class getgamma1cont with the following elements:
- gamma_go
Optimal Go threshold: the smallest value in
gamma_gridfor which\Pr(\mathrm{Go}) < \code{target\_go}under the Go-calibration scenario.NAif no such value exists.- gamma_nogo
Optimal NoGo threshold: the smallest value in
gamma_gridfor which\Pr(\mathrm{NoGo}) < \code{target\_nogo}under the NoGo-calibration scenario.NAif no such value exists.- PrGo_opt
Marginal
\Pr(g_{\mathrm{Go}} \ge \gamma_{\mathrm{go}})at the optimal\gamma_{\mathrm{go}}under the Go-calibration scenario.NAifgamma_goisNA.- PrNoGo_opt
Marginal
\Pr(g_{\mathrm{NoGo}} \ge \gamma_{\mathrm{nogo}})at the optimal\gamma_{\mathrm{nogo}}under the NoGo-calibration scenario.NAifgamma_nogoisNA.- target_go
The value of
target_gosupplied by the user.- target_nogo
The value of
target_nogosupplied by the user.- grid_results
A data frame with columns
gamma_grid,PrGo_grid(marginal Go probability under the Go-calibration scenario), andPrNoGo_grid(marginal NoGo probability under the NoGo-calibration scenario).
Examples
# Example 1: Controlled design, vague prior, posterior probability
# gamma_go : smallest gamma s.t. Pr(Go) < 0.05 under Null (mu_t = mu_c = 1.0)
# gamma_nogo: smallest gamma s.t. Pr(NoGo) < 0.20 under Alt (mu_t = 2.5, mu_c = 1.0)
getgamma1cont(
nsim = 1000L, prob = 'posterior', design = 'controlled',
prior = 'vague', CalcMethod = 'MM',
theta_TV = 1.5, theta_MAV = 0.0, theta_NULL = NULL, nMC = NULL,
mu_t_go = 1.0, mu_c_go = 1.0, sigma_t_go = 2.0, sigma_c_go = 2.0,
mu_t_nogo = 2.5, mu_c_nogo = 1.0, sigma_t_nogo = 2.0, sigma_c_nogo = 2.0,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = 15L, m_t = NULL, m_c = NULL,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = NULL, sigma0_t = NULL, sigma0_c = NULL,
r = NULL, ne_t = NULL, ne_c = NULL, alpha0e_t = NULL, alpha0e_c = NULL,
bar_ye_t = NULL, bar_ye_c = NULL, se_t = NULL, se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 1L
)
# Example 2: Uncontrolled design, N-Inv-Chisq prior, posterior probability
getgamma1cont(
nsim = 1000L, prob = 'posterior', design = 'uncontrolled',
prior = 'N-Inv-Chisq', CalcMethod = 'NI',
theta_TV = 1.0, theta_MAV = 0.0, theta_NULL = NULL, nMC = NULL,
mu_t_go = 1.5, mu_c_go = NULL, sigma_t_go = 1.5, sigma_c_go = NULL,
mu_t_nogo = 3.0, mu_c_nogo = NULL, sigma_t_nogo = 1.5, sigma_c_nogo = NULL,
target_go = 0.05, target_nogo = 0.20,
n_t = 20L, n_c = NULL, m_t = NULL, m_c = NULL,
kappa0_t = 2, kappa0_c = NULL, nu0_t = 5, nu0_c = NULL,
mu0_t = 3.0, mu0_c = 1.5, sigma0_t = 1.5, sigma0_c = NULL,
r = 1.0, ne_t = NULL, ne_c = NULL, alpha0e_t = NULL, alpha0e_c = NULL,
bar_ye_t = NULL, bar_ye_c = NULL, se_t = NULL, se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 2L
)
# Example 3: External design, vague prior, posterior probability
getgamma1cont(
nsim = 1000L, prob = 'posterior', design = 'external',
prior = 'vague', CalcMethod = 'MM',
theta_TV = 1.0, theta_MAV = 0.0, theta_NULL = NULL, nMC = NULL,
mu_t_go = 1.0, mu_c_go = 1.0, sigma_t_go = 1.5, sigma_c_go = 1.5,
mu_t_nogo = 2.5, mu_c_nogo = 1.0, sigma_t_nogo = 1.5, sigma_c_nogo = 1.5,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = 15L, m_t = NULL, m_c = NULL,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = NULL, sigma0_t = NULL, sigma0_c = NULL,
r = NULL, ne_t = NULL, ne_c = 20L, alpha0e_t = NULL, alpha0e_c = 0.5,
bar_ye_t = NULL, bar_ye_c = 0.0, se_t = NULL, se_c = 1.5,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 4L
)
# Example 4: Controlled design, vague prior, predictive probability
getgamma1cont(
nsim = 1000L, prob = 'predictive', design = 'controlled',
prior = 'vague', CalcMethod = 'MM',
theta_TV = NULL, theta_MAV = NULL, theta_NULL = 1.0, nMC = NULL,
mu_t_go = 1.0, mu_c_go = 1.0, sigma_t_go = 2.0, sigma_c_go = 2.0,
mu_t_nogo = 2.5, mu_c_nogo = 1.0, sigma_t_nogo = 2.0, sigma_c_nogo = 2.0,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = 15L, m_t = 50L, m_c = 50L,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = NULL, sigma0_t = NULL, sigma0_c = NULL,
r = NULL, ne_t = NULL, ne_c = NULL, alpha0e_t = NULL, alpha0e_c = NULL,
bar_ye_t = NULL, bar_ye_c = NULL, se_t = NULL, se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 3L
)
# Example 5: Uncontrolled design, vague prior, predictive probability
getgamma1cont(
nsim = 1000L, prob = 'predictive', design = 'uncontrolled',
prior = 'vague', CalcMethod = 'MM',
theta_TV = NULL, theta_MAV = NULL, theta_NULL = 1.0, nMC = NULL,
mu_t_go = 1.0, mu_c_go = NULL, sigma_t_go = 2.0, sigma_c_go = NULL,
mu_t_nogo = 2.5, mu_c_nogo = NULL, sigma_t_nogo = 2.0, sigma_c_nogo = NULL,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = NULL, m_t = 50L, m_c = 50L,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = 0.0, sigma0_t = NULL, sigma0_c = NULL,
r = 1.0, ne_t = NULL, ne_c = NULL, alpha0e_t = NULL, alpha0e_c = NULL,
bar_ye_t = NULL, bar_ye_c = NULL, se_t = NULL, se_c = NULL,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 5L
)
# Example 6: External design, vague prior, predictive probability
getgamma1cont(
nsim = 1000L, prob = 'predictive', design = 'external',
prior = 'vague', CalcMethod = 'MM',
theta_TV = NULL, theta_MAV = NULL, theta_NULL = 1.0, nMC = NULL,
mu_t_go = 1.0, mu_c_go = 1.0, sigma_t_go = 1.5, sigma_c_go = 1.5,
mu_t_nogo = 2.5, mu_c_nogo = 1.0, sigma_t_nogo = 1.5, sigma_c_nogo = 1.5,
target_go = 0.05, target_nogo = 0.20,
n_t = 15L, n_c = 15L, m_t = 50L, m_c = 50L,
kappa0_t = NULL, kappa0_c = NULL, nu0_t = NULL, nu0_c = NULL,
mu0_t = NULL, mu0_c = NULL, sigma0_t = NULL, sigma0_c = NULL,
r = NULL, ne_t = NULL, ne_c = 20L, alpha0e_t = NULL, alpha0e_c = 0.5,
bar_ye_t = NULL, bar_ye_c = 0.0, se_t = NULL, se_c = 1.5,
gamma_grid = seq(0.01, 0.99, by = 0.01), seed = 6L
)
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