| optimal_binary {drugdevelopR} | R Documentation |
Optimal phase II/III drug development planning with binary endpoint
Description
The optimal_binary function of the drugdevelopR package enables
planning of phase II/III drug development programs with optimal sample size
allocation and go/no-go decision rules for binary endpoints. In this case,
the treatment effect is measured by the risk ratio (RR). The assumed true
treatment effects can be assumed to be fixed or modelled by a prior
distribution. The R Shiny application
prior visualizes the prior
distributions used in this package. Fast computing is enabled by parallel
programming.
Usage
optimal_binary(
w,
p0,
p11,
p12,
in1,
in2,
n2min,
n2max,
stepn2,
rrgomin,
rrgomax,
steprrgo,
alpha,
beta,
c2,
c3,
c02,
c03,
K = Inf,
N = Inf,
S = -Inf,
steps1 = 1,
stepm1 = 0.95,
stepl1 = 0.85,
b1,
b2,
b3,
gamma = 0,
fixed = FALSE,
skipII = FALSE,
num_cl = 1
)
Arguments
w |
weight for mixture prior distribution |
p0 |
assumed true rate of control group, see here for details |
p11 |
assumed true rate of treatment group, see here for details |
p12 |
assumed true rate of treatment group, see here for details |
in1 |
amount of information for |
in2 |
amount of information for |
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 |
rrgomin |
minimal threshold value for the go/no-go decision rule |
rrgomax |
maximal threshold value for the go/no-go decision rule |
steprrgo |
step size for the optimization over RRgo |
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 |
steps1 |
lower boundary for effect size category "small" in RR scale, default: 1 |
stepm1 |
lower boundary for effect size category "medium" in RR scale = upper boundary for effect size category "small" in RR scale, default: 0.95 |
stepl1 |
lower boundary for effect size category "large" in RR scale = upper boundary for effect size category "medium" in RR scale, default: 0.85 |
b1 |
expected gain for effect size category "small" |
b2 |
expected gain for effect size category "medium" |
b3 |
expected gain for effect size category "large" |
gamma |
to model different populations in phase II and III choose |
fixed |
choose if true treatment effects are fixed or random, if TRUE p11 is used as fixed effect for p1 |
skipII |
skipII choose if skipping phase II is an option, default: FALSE;
if TRUE, the program calculates the expected utility for the case when phase
II is skipped and compares it to the situation when phase II is not skipped.
The results are then returned as a two-row data frame, |
num_cl |
number of clusters used for parallel computing, default: 1 |
Value
The output of the function is a data.frame object containing the optimization results:
- u
maximal expected utility under the optimization constraints, i.e. the expected utility of the optimal sample size and threshold value
- RRgo
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
- sProg2
probability of a successful program with "medium" treatment effect in phase III
- sProg3
probability of a successful program with "large" treatment effect in phase III
- 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.
References
IQWiG (2016). Allgemeine Methoden. Version 5.0, 10.07.2016, Technical Report. Available at https://www.iqwig.de/ueber-uns/methoden/methodenpapier/, assessed last 15.05.19.
Examples
# Activate progress bar (optional)
## Not run:
progressr::handlers(global = TRUE)
## End(Not run)
# Optimize
optimal_binary(w = 0.3, # define parameters for prior
p0 = 0.6, p11 = 0.3, p12 = 0.5,
in1 = 30, in2 = 60, # (https://web.imbi.uni-heidelberg.de/prior/)
n2min = 20, n2max = 100, stepn2 = 4, # define optimization set for n2
rrgomin = 0.7, rrgomax = 0.9, steprrgo = 0.05, # define optimization set for RRgo
alpha = 0.025, beta = 0.1, # drug development planning parameters
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150, # fixed and variable costs for phase II/III,
K = Inf, N = Inf, S = -Inf, # set constraints
steps1 = 1, # define lower boundary for "small"
stepm1 = 0.95, # "medium"
stepl1 = 0.85, # and "large" treatment effect size categories
b1 = 1000, b2 = 2000, b3 = 3000, # define expected benefits
gamma = 0, # population structures in phase II/III
fixed = FALSE, # true treatment effects are fixed/random
skipII = FALSE, # choose if skipping phase II is an option
num_cl = 2) # number of cores for parallelized computing