Defaults

Parameter	Argument	Purpose	Default
\(\small{\alpha}\)	`alpha`	Nominal level of the test	`0.05`
CV	`CV`	CV	none
doses	`doses`	Vector of doses	see examples
\(\small{\pi}\)	`targetpower`	Minimum desired power	`0.80`
\(\small{\beta_0}\)	`beta0`	‘True’ or assumed slope of the power model	see below
\(\small{\theta_1}\)	`theta1`	Lower limit for the ratio of dose normalized means Rdmn	see below
\(\small{\theta_2}\)	`theta2`	Upper limit for the ratio of dose normalized means Rdmn	see below
design	`design`	Planned design	`"crossover"`
dm	`dm`	Design matrix	`NULL`
CV_b	`CVb`	Coefficient of variation of the between-subject variability	–
print	`print`	Show information in the console?	`TRUE`
details	`details`	Show details of the sample size search?	`FALSE`
imax	`imax`	Maximum number of iterations	`100`

Arguments targetpower, theta1, theta2, and CV have to be given as fractions, not in percent.
The CV is generally the within- (intra-) subject coefficient of variation. Only for design = "parallel" it is the total (a.k.a. pooled) CV. The between- (intra-) subject coefficient of variation CV_b is only necessary if design = "IBD" (if missing, it will be set to 2*CV).

The ‘true’ or assumed slope of the power model \(\small{\beta_0}\) defaults to 1+log(0.95)/log(rd), where rd is the ratio of the highest/lowest dose.

Supported designs are "crossover" (default; Latin Squares), "parallel", and "IBD" (incomplete block design). Note that when "crossover" is chosen, instead of Latin Squares any Williams’ design could be used as well (identical degrees of freedom result in the same sample size).

With sampleN.dp(..., details = FALSE, print = FALSE) results are provided as a data frame ¹ with ten (design = "crossover") or eleven (design = "parallel" or design = "IBD") columns: Design, alpha, CV, (CVb,) doses, beta0, theta1, theta2, Sample size, Achieved power, and Target power. To access e.g., the sample size use sampleN.dp[["Sample size"]].

The estimated sample size gives always the total number of subjects (not subject/sequence in crossovers or subjects/group in a parallel design – like in some other software packages).

Examples

Estimate the sample size for a modified Fibonacci dose-escalation study, lowest dose 10, three levels. Assumed CV 0.20 and \(\small{\beta_0}\) slightly higher than 1. Defaults employed.

mod.fibo <- function(lowest, levels) {
  # modified Fibonacci dose-escalation
  fib      <- c(2, 1 + 2/3, 1.5, 1 + 1/3)
  doses    <- numeric(levels)
  doses[1] <- lowest
  level    <- 2
  repeat {
    if (level <= 4) {
      doses[level] <- doses[level-1] * fib[level-1]
    } else {  # ratio 1.33 for all higher doses
      doses[level] <- doses[level-1] * fib[4]
    }
    level <- level + 1
    if (level > levels) {
      break
    }
  }
  return(signif(doses, 3))
}
lowest <- 10
levels <- 3
doses  <- mod.fibo(lowest, levels)
sampleN.dp(CV = 0.20, doses = doses, beta0 = 1.02)
# 
# ++++ Dose proportionality study, power model ++++
#             Sample size estimation
# -------------------------------------------------
# Study design: crossover (3x3 Latin square) 
# alpha = 0.05, target power = 0.8
# Equivalence margins of R(dnm) = 0.8 ... 1.25 
# Doses = 10 20 33.3 
# True slope = 1.02, CV = 0.2
# Slope acceptance range = 0.81451 ... 1.1855 
# 
# Sample size (total)
#  n     power
# 15   0.808127

As above but with an additional level.

levels <- 4
doses  <- mod.fibo(lowest, levels)
x <- sampleN.dp(CV = 0.20, doses = doses, beta0 = 1.02) # we need the data.frame later
# 
# ++++ Dose proportionality study, power model ++++
#             Sample size estimation
# -------------------------------------------------
# Study design: crossover (4x4 Latin square) 
# alpha = 0.05, target power = 0.8
# Equivalence margins of R(dnm) = 0.8 ... 1.25 
# Doses = 10 20 33.3 50 
# True slope = 1.02, CV = 0.2
# Slope acceptance range = 0.86135 ... 1.1386 
# 
# Sample size (total)
#  n     power
# 16   0.867441

Note that with the wider dose range the acceptance range narrows.

Explore the impact of dropouts.

res <- data.frame(n = seq(x[["Sample size"]], 12, -1), power = NA)
for (i in 1:nrow(res)) {
  res$power[i] <- signif(suppressMessages(
                           power.dp(CV = 0.20, doses = doses,
                                    beta0 = 1.02, n = res$n[i])), 5)
}
res <- res[res$power >= 0.80, ]
print(res, row.names = FALSE)
#   n   power
#  16 0.86744
#  15 0.82914
#  14 0.81935
#  13 0.81516

As usual nothing to worry about.

Rather extreme: Five levels and we desire only three periods. Hence, we opt for an incomplete block design. The design matrix of a balanced minimal repeated measurements design is obtained by the function balmin.RMD() of package crossdes.

levels <- 5
doses  <- mod.fibo(lowest, levels)
per    <- 3
block  <- levels*(levels-1)/(per-1)
dm     <- crossdes::balmin.RMD(levels, block, per)
x      <- sampleN.dp(CV = 0.20, doses = doses, beta0 = 1.02,
                     design = "IBD", dm = dm)
# 
# ++++ Dose proportionality study, power model ++++
#             Sample size estimation
# -------------------------------------------------
# Study design: IBD (5x10x3) 
# alpha = 0.05, target power = 0.8
# Equivalence margins of R(dnm) = 0.8 ... 1.25 
# Doses = 10 20 33.3 50 66.7 
# True slope = 1.02, CV = 0.2, CVb = 0.4
# Slope acceptance range = 0.88241 ... 1.1176 
# 
# Sample size (total)
#  n     power
# 30   0.898758

The IBD comes with a price since we need at least two blocks.

res <- data.frame(n = seq(x[["Sample size"]], nrow(dm), -1),
                  power = NA)
for (i in 1:nrow(res)) {
  res$power[i] <- signif(suppressMessages(
                           power.dp(CV = 0.20, doses = doses,
                                    beta0 = 1.02, design = "IBD",
                                    dm = dm, n = res$n[i])), 5)
}
res <- res[res$power >= 0.80, ]
print(res, row.names = FALSE)
#   n   power
#  30 0.89876
#  29 0.89196
#  28 0.87939
#  27 0.87201
#  26 0.85793
#  25 0.83587
#  24 0.82470
#  23 0.80405

Again, we don’t have to worry about dropouts.

For a wide dose range the acceptance range narrows and becomes increasingly difficult to meet.

doses  <- 2^(seq(0, 8, 2))
levels <- length(doses)
sampleN.dp(CV = 0.30, doses = doses, beta0 = 1.02,
           design = "crossover")
# 
# ++++ Dose proportionality study, power model ++++
#             Sample size estimation
# -------------------------------------------------
# Study design: crossover (5x5 Latin square) 
# alpha = 0.05, target power = 0.8
# Equivalence margins of R(dnm) = 0.8 ... 1.25 
# Doses = 1 4 16 64 256 
# True slope = 1.02, CV = 0.3
# Slope acceptance range = 0.95976 ... 1.0402 
# 
# Sample size (total)
#  n     power
# 70   0.809991

In an exploratory setting more liberal limits could be specified (only one has to be specified; the other is calculated as the reciprocal of it).

sampleN.dp(CV = 0.30, doses = doses, beta0 = 1.02,
           design = "crossover", theta1 = 0.75)
# 
# ++++ Dose proportionality study, power model ++++
#             Sample size estimation
# -------------------------------------------------
# Study design: crossover (5x5 Latin square) 
# alpha = 0.05, target power = 0.8
# Equivalence margins of R(dnm) = 0.75 ... 1.333333 
# Doses = 1 4 16 64 256 
# True slope = 1.02, CV = 0.3
# Slope acceptance range = 0.94812 ... 1.0519 
# 
# Sample size (total)
#  n     power
# 30   0.828246

Hummel et al. ² proposed even more liberal \(\small{\theta_{1},\theta_{2}}\) of {0.50, 2.0}.

R Documentation. Data Frames. 2022-02-08. R-manual.↩︎
Hummel J, McKendrick S, Brindley C, French R. Exploratory assessment of dose proportionality: review of current approaches and proposal for a practical criterion. Pharm. Stat. 2009; 8(1): 38–49. doi:10.1002/pst.326.↩︎

Dose-Proportionality

Details and examples of other methods are accessible via the menu bar on top of the page and the online manual of all functions.

Defaults

Examples

Cave!

Author

License

GPL-3 2022-02-21 Helmut Schütz