decision2S: Decision Function for 2 Sample Designs

In RBesT: R Bayesian Evidence Synthesis Tools

Decision Function for 2 Sample Designs

Description

The function sets up a 2 sample decision function with an arbitrary number of conditions on the difference distribution.

Usage

decision2S(
  pc = 0.975,
  qc = 0,
  lower.tail = TRUE,
  link = c("identity", "logit", "log")
)

oc2Sdecision(
  pc = 0.975,
  qc = 0,
  lower.tail = TRUE,
  link = c("identity", "logit", "log")
)

Arguments

pc	Vector of critical cumulative probabilities of the difference distribution.
qc	Vector of respective critical values of the difference distribution. Must match the length of pc.
lower.tail	Logical; if TRUE (default), probabilities are P(X \leq x), otherwise, P(X > x).
link	Enables application of a link function prior to evaluating the difference distribution. Can take one of the values identity (default), logit or log.

Details

This function creates a one- or two-sided decision function on the basis of the difference distribution in a 2 sample situation. To support double criterion designs, see Neuenschwander et al., 2010, an arbitrary number of criterions can be given. The decision function demands that the probability mass below the critical value qc of the difference \theta_1 - \theta_2 is at least pc. Hence, for lower.tail=TRUE condition i is equivalent to

P(\theta_1 - \theta_2 \leq q_{c,i}) > p_{c,i}

and the decision function is implemented as indicator function using the heavy-side step function H(x) which is 0 for x \leq 0 and 1 for x > 0. As all conditions must be met, the final indicator function returns

\Pi_i H_i(P(\theta_1 - \theta_2 \leq q_{c,i}) - p_{c,i} ),

which is 1 if all conditions are met and 0 otherwise. For lower.tail=FALSE differences must be greater than the given quantiles qc.

For the case of a boolean vector given to lower.tail the direction of each decision aligns respectively, and a two-sided decision function is created.

Note that whenever a link other than identity is requested, then the underlying densities are first transformed using the link function and then the probabilties for the differences are calculated in the transformed space. Hence, for a binary endpoint the default identity link will calculate risk differences, the logit link will lead to decisions based on the differences in logits corresponding to a criterion based on the log-odds. The log link will evaluate ratios instead of absolute differences which could be useful for a binary endpoint or counting rates. The respective critical quantiles qc must be given on the transformed scale.

Value

The function returns a decision function, of class decision2S_1sided for one-sided, and of class decision2S_2sided for two-sided decisions.

One-sided decision functions take three arguments. The first and second argument are expected to be mixture (posterior) distributions from which the difference distribution is formed and all conditions are tested. The third argument determines if the function acts as an indicator function or if the function returns the distance from the decision boundary for each condition in log-space. That is, the distance is 0 at the decision boundary, negative for a 0 decision and positive for a 1 decision.

For two-sided decision functions, the two components can be extracted with functions lower() and upper(). The distance as calculated by the decision function is returned as a list with components lower and upper.

Functions

• oc2Sdecision(): Deprecated old function name. Please use decision2S instead.

References

Gsponer T, Gerber F, Bornkamp B, Ohlssen D, Vandemeulebroecke M, Schmidli H.A practical guide to Bayesian group sequential designs. Pharm. Stat.. 2014; 13: 71-80

See Also

Other design2S: decision2S_boundary(), oc2S(), pos2S()

Examples

# see Gsponer et al., 2010
priorT <- mixnorm(c(1, 0, 0.001), sigma = 88, param = "mn")
priorP <- mixnorm(c(1, -49, 20), sigma = 88, param = "mn")
# the success criteria is for delta which are larger than some
# threshold value which is why we set lower.tail=FALSE
successCrit <- decision2S(c(0.95, 0.5), c(0, 50), FALSE)
# the futility criterion acts in the opposite direction
futilityCrit <- decision2S(c(0.90), c(40), TRUE)
# intermediate criteria can also be defined: no futility and no success.
# version 1: not significant, but good effect size.
intermediateCrit1 <- decision2S(
  c(1 - 0.95, 0.5, 1 - 0.90),
  c(0, 50, 40),
  c(TRUE, FALSE, TRUE)
)
# version 2: significant, but too small effect size.
intermediateCrit2 <- decision2S(
  c(0.95, 1 - 0.5, 1 - 0.90),
  c(0, 50, 40),
  c(FALSE, TRUE, TRUE)
)
# version 3: not significant and small effect size.
intermediateCrit3 <- decision2S(
  c(1 - 0.95, 1 - 0.5, 1 - 0.90),
  c(0, 50, 40),
  c(TRUE, TRUE, TRUE)
)

print(successCrit)
print(futilityCrit)
print(intermediateCrit1)
print(intermediateCrit2)
print(intermediateCrit3)

# consider decision for specific outcomes
postP_interim <- postmix(priorP, n = 10, m = -50)
postT_interim <- postmix(priorT, n = 20, m = -80)
futilityCrit(postP_interim, postT_interim)
successCrit(postP_interim, postT_interim)
intermediateCrit1(postP_interim, postT_interim)
intermediateCrit2(postP_interim, postT_interim)
intermediateCrit3(postP_interim, postT_interim)

# Binary endpoint with double criterion decision on log-odds scale
# 95% certain positive difference and an odds ratio of 2 at least
decL2 <- decision2S(c(0.95, 0.5), c(0, log(2)), lower.tail = FALSE, link = "logit")
# 95% certain positive difference and an odds ratio of 3 at least
decL3 <- decision2S(c(0.95, 0.5), c(0, log(3)), lower.tail = FALSE, link = "logit")

# data scenario
post1 <- postmix(mixbeta(c(1, 1, 1)), n = 40, r = 10)
post2 <- postmix(mixbeta(c(1, 1, 1)), n = 40, r = 18)

# positive outcome and a median odds ratio of at least 2 ...
decL2(post2, post1)
# ... but not more than 3
decL3(post2, post1)

RBesT documentation built on March 13, 2026, 5:06 p.m.