optimal_bias {drugdevelopR}

Optimal phase II/III drug development planning for time-to-event endpoints when discounting phase II results

Description

The function optimal_bias of the drugdevelopR package enables planning of phase II/III drug development programs with optimal sample size allocation and go/no-go decision rules including methods for discounting of phase II results for time-to-event endpoints (Preussler et. al, 2020). The discounting may be necessary as programs that proceed to phase III can be overoptimistic about the treatment effect (i.e. they are biased). The assumed true treatment effects can be assumed fixed (planning is then also possible via user friendly R Shiny App: bias) 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_bias(
  w,
  hr1,
  hr2,
  id1,
  id2,
  d2min,
  d2max,
  stepd2,
  hrgomin,
  hrgomax,
  stephrgo,
  adj = "both",
  lambdamin = NULL,
  lambdamax = NULL,
  steplambda = NULL,
  alphaCImin = NULL,
  alphaCImax = NULL,
  stepalphaCI = NULL,
  alpha,
  beta,
  xi2,
  xi3,
  c2,
  c3,
  c02,
  c03,
  K = Inf,
  N = Inf,
  S = -Inf,
  steps1 = 1,
  stepm1 = 0.95,
  stepl1 = 0.85,
  b1,
  b2,
  b3,
  fixed = FALSE,
  num_cl = 1
)

Arguments

w

weight for mixture prior distribution

hr1

first assumed true treatment effect on HR scale for prior distribution, see the vignette on priors as well as the Shiny app for more details concerning the definition of a prior distribution.

hr2

second assumed true treatment effect on HR scale for prior distribution

id1

amount of information for hr1 in terms of number of events

id2

amount of information for hr2 in terms of number of events

d2min

minimal number of events for phase II

d2max

maximal number of events for phase II

stepd2

stepsize for the optimization over d2

hrgomin

minimal threshold value for the go/no-go decision rule

hrgomax

maximal threshold value for the go/no-go decision rule

stephrgo

stepsize for the optimization over HRgo

adj

choose type of adjustment: "multiplicative", "additive", "both" or "all". When using "both", res[1,] contains the results using the multiplicative method and res[2,] contains the results using the additive method. When using "all", there are also res[3,] and res[4,], containing the results of a multiplicative and an additive method which do not only adjust the treatment effect but also the threshold value for the decision rule.

lambdamin

minimal multiplicative adjustment parameter lambda (i.e. use estimate with a retention factor)

lambdamax

maximal multiplicative adjustment parameter lambda (i.e. use estimate with a retention factor)

steplambda

stepsize for the adjustment parameter lambda

alphaCImin

minimal additive adjustment parameter alphaCI (i.e. adjust the lower bound of the one-sided confidence interval)

alphaCImax

maximal additive adjustment parameter alphaCI (i.e. adjust the lower bound of the one-sided confidence interval)

stepalphaCI

stepsize for alphaCI

alpha

one-sided significance level

beta

1-beta power for calculation of the number of events for phase III by Schoenfeld (1981) formula

xi2

event rate for phase II

xi3

event rate for phase III

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 HR scale, default: 1

stepm1

lower boundary for effect size category "medium" in HR scale = upper boundary for effect size category "small" in HR scale, default: 0.95

stepl1

lower boundary for effect size category "large" in HR scale = upper boundary for effect size category "medium" in HR scale, default: 0.85

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 $

fixed

choose if true treatment effects are fixed or random, if TRUE hr1 is used as fixed effect

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:

Method

Type of adjustment: "multipl." (multiplicative adjustment of effect size), "add." (additive adjustment of effect size), "multipl2." (multiplicative adjustment of effect size and threshold), "add2." (additive adjustment of effect size and threshold)

Adj

optimal adjustment parameter (lambda or alphaCI according to Method)

u

maximal expected utility under the optimization constraints, i.e. the expected utility of the optimal sample size and threshold value

HRgo

optimal threshold value for the decision rule to go to phase III

d2

optimal total number of events for phase II

d3

total expected number of events for phase III; rounded to next natural number

d

total expected number of events in the program; d = d2 + d3

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.

Preussler, S., Kirchner, M., Goette, H., Kieser, M. (2020). Optimal designs for phase II/III drug development programs including methods for discounting of phase II results. Submitted to peer-review journal.

Schoenfeld, D. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316-319.

Examples

# Activate progress bar (optional)
## Not run: 
progressr::handlers(global = TRUE)

## End(Not run)
# Optimize

optimal_bias(w = 0.3,                       # define parameters for prior
  hr1 = 0.69, hr2 = 0.88, id1 = 210, id2 = 420,      # (https://web.imbi.uni-heidelberg.de/prior/)
  d2min = 20, d2max = 100, stepd2 = 5,               # define optimization set for d2
  hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.05,     # define optimization set for HRgo
  adj = "both",                                      # choose type of adjustment
  lambdamin = 0.2, lambdamax = 1, steplambda = 0.05, # define optimization set for lambda
  alphaCImin = 0.025, alphaCImax = 0.5,
  stepalphaCI = 0.025,                               # define optimization set for alphaCI
  alpha = 0.025, beta = 0.1, xi2 = 0.7, xi3 = 0.7,   # drug development planning parameters
  c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,           # fixed/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" effect size categories
  b1 = 1000, b2 = 2000, b3 = 3000,                   # define expected benefits 
  fixed = FALSE,                                     # true treatment effects are fixed/random
  num_cl = 1)                                        # number of coresfor parallelized computing