optimal_multitrial_normal {drugdevelopR} | R Documentation |
Optimal phase II/III drug development planning where several phase III trials are performed
Description
The optimal_multitrial_normal
function enables planning of phase II/III
drug development programs with several phase III trials for
the same normally distributed endpoint. Its main output values are optimal
sample size allocation and go/no-go decision rules. For normally distributed
endpoints, the treatment effect is measured by the standardized difference in
means (Delta). The assumed true treatment effects can be assumed fixed or
modelled by a prior distribution.
Usage
optimal_multitrial_normal(
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
n2min,
n2max,
stepn2,
kappamin,
kappamax,
stepkappa,
alpha,
beta,
c2,
c3,
c02,
c03,
K = Inf,
N = Inf,
S = -Inf,
b1,
b2,
b3,
case,
strategy = TRUE,
fixed = FALSE,
num_cl = 1
)
Arguments
w |
weight for mixture prior distribution |
Delta1 |
assumed true prior treatment effect measured as the standardized difference in means, see here for details |
Delta2 |
assumed true prior treatment effect measured as the standardized difference in means, see here for details |
in1 |
amount of information for |
in2 |
amount of information for |
a |
lower boundary for the truncation of the prior distribution |
b |
upper boundary for the truncation of the prior distribution |
n2min |
minimal total sample size for phase II; must be an even number |
n2max |
maximal total sample size for phase II, must be an even number |
stepn2 |
step size for the optimization over n2; must be an even number |
kappamin |
minimal threshold value kappa for the go/no-go decision rule |
kappamax |
maximal threshold value kappa for the go/no-go decision rule |
stepkappa |
step size for the optimization over the threshold value kappa |
alpha |
one-sided significance level |
beta |
type II error rate; i.e. |
c2 |
variable per-patient cost for phase II in 10^5 $ |
c3 |
variable per-patient cost for phase III in 10^5 $ |
c02 |
fixed cost for phase II in 10^5 $ |
c03 |
fixed cost for phase III in 10^5 $ |
K |
constraint on the costs of the program, default: Inf, e.g. no constraint |
N |
constraint on the total expected sample size of the program, default: Inf, e.g. no constraint |
S |
constraint on the expected probability of a successful program, default: -Inf, e.g. no constraint |
b1 |
expected gain for effect size category "small" in 10^5 $ |
b2 |
expected gain for effect size category "medium" in 10^5 $ |
b3 |
expected gain for effect size category "large" in 10^5 $ |
case |
choose case: "at least 1, 2 or 3 significant trials needed for approval" |
strategy |
choose strategy: "conduct 1, 2, 3 or 4 trials in order to achieve the case's goal"; TRUE calculates all strategies of the selected |
fixed |
choose if true treatment effects are fixed or following a prior distribution, if TRUE |
num_cl |
number of clusters used for parallel computing, default: 1 |
Details
The R Shiny application prior visualizes the prior distributions used in this package. Fast computing is enabled by parallel programming.
Value
The output of the function is a data.frame
object containing the optimization results:
- Case
Case: "number of significant trials needed"
- Strategy
Strategy: "number of trials to be conducted in order to achieve the goal of the case"
- u
maximal expected utility under the optimization constraints, i.e. the expected utility of the optimal sample size and threshold value
- Kappa
optimal threshold value for the decision rule to go to phase III
- n2
total sample size for phase II; rounded to the next even natural number
- n3
total sample size for phase III; rounded to the next even natural number
- n
total sample size in the program; n = n2 + n3
- K
maximal costs of the program (i.e. the cost constraint, if it is set or the sum K2+K3 if no cost constraint is set)
- pgo
probability to go to phase III
- sProg
probability of a successful program
- sProg1
probability of a successful program with "small" treatment effect in phase III (lower boundary in HR scale is set to 0, as proposed by Cohen (1988))
- sProg2
probability of a successful program with "medium" treatment effect in phase III (lower boundary in HR scale is set to 0.5, as proposed Cohen (1988))
- sProg3
probability of a successful program with "large" treatment effect in phase III (lower boundary in HR scale is set to 0.8, as proposed Cohen (1988))
- K2
expected costs for phase II
- K3
expected costs for phase III
and further input parameters. Taking cat(comment())
of the
data frame lists the used optimization sequences, start and
finish date of the optimization procedure.
Effect sizes
In other settings, the definition of "small", "medium" and "large" effect
sizes can be user-specified using the input parameters steps1
, stepm1
and
stepl1
. Due to the complexity of the multitrial setting, this feature is
not included for this setting. Instead, the effect sizes were set to
to predefined values as explained under sProg1, sProg2 and sProg3 in the
Value section.
References
Cohen, J. (1988). Statistical power analysis for the behavioral sciences.
Examples
# Activate progress bar (optional)
## Not run: progressr::handlers(global = TRUE)
# Optimize
optimal_multitrial_normal(w = 0.3, # define parameters for prior
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600, # (https://web.imbi.uni-heidelberg.de/prior/)
a = 0.25, b = 0.75,
n2min = 20, n2max = 100, stepn2 = 4, # define optimization set for n2
kappamin = 0.02, kappamax = 0.2, stepkappa = 0.02, # define optimization set for kappa
alpha = 0.025, beta = 0.1, # drug development planning parameters
c2 = 0.675, c3 = 0.72, c02 = 15, c03 = 20, # fixed and variable costs for phase II/III
K = Inf, N = Inf, S = -Inf, # set constraints
b1 = 3000, b2 = 8000, b3 = 10000, # expected benefit for each effect size
case = 1, strategy = TRUE, # chose Case and Strategy
fixed = TRUE, # true treatment effects are fixed/random
num_cl = 1) # number of cores for parallelized computing