Trial Analysis

#> Warning: package 'stringr' was built under R version 4.5.2

Introduction

This vignette picks up where the previous one (Trial Definition), ends. To recap, our trial defines the six fundamental elements of a CRM trial as

The dose grid

The trial will use a dose grid consisting of the following doses: 1, 3, 9, 20, 30, 45, 60, 80 and 100. The units in which doses are defined is irrelevant to the operation of the CRM.

The dose-toxicity model

The trial uses a logistic log Normal dose toxicity model

\[ log(\frac{p_i}{1 - p_i}) = \alpha + \beta log(d_i / d^*) \]

where the prior joint distribution of \(\alpha\) and \(\beta\) is

\[ \begin{bmatrix} \alpha \\ log(\beta) \end{bmatrix} \sim N\begin{pmatrix} \begin{bmatrix} -0.85\\0 \end{bmatrix} , \begin{bmatrix} 1 & -0.5 \\ -0.5 & 1 \end{bmatrix} \end{pmatrix}. \]

The increment rule

The maximum increment for doses greater than 0 and less than 20 is 100 x (1 + 1)%, or 200% of the highest dose used so far, whereas for 20 or more, the maximum increment is 100 x (1 + 0.5)%, or 150% of the highest dose used so far.

Note that a 2-fold increment corresponds to a 3-fold escalation.

The dose selection rule

Here, we choose to use Neuenschwander’s rule (Neuenschwander, Branson, and Gsponer 2008), in which the dose for the next cohort to be the dose (amongst those doses that are eligible for selection according to the escalation rule) that has the highest posterior chance of having a probability of toxicity in the target range - here [0.2, 0.35) - provided that the dose’s chance of having a probability in the overdose range - here [0.35, 1.0] - is less than 0.25.

The cohort size

Whilst the dose for the next cohort is 20 or less and no DLTs have been observed, the minimum cohort size is 1. Otherwise, it is 3.

The stopping rule

The trial will stop when either

Trial definition

The code to define these elements of the trial design is given in the Trial Definition vignette.

Analysing a trial

Given the trial design constructed above, the process of analysing a real life instance of the trial is simply a matter of providing the model with the actual toxicity status of the participants treated so far. The escalation rules we defined earlier allow the use of a single patient run-in until either the first DLT is observed or until dose 20 has been administered.

The single patient run-in

Assume that the first three patients - dosed at 1, 3 and 5 - completed the trial without incident, but that the fourth patient - treated at 10 - experienced a DLT.

We provide this information to crmPack via a Data object:

firstFour <- Data(
  x = c(1, 3, 9, 20),
  y = c(0, 0, 0, 1),
  ID = 1:4,
  cohort = 1:4,
  doseGrid = doseGrid
)

Within a Data object, the doses at which each patient is treated are given by the x slot and their toxicity status (a Boolean where a toxicity is represented by a truthy value) by the y slot.

The observed data is easily visualised

plot(firstFour)

A visual representation of the data from the first four participants. The first three, treated at doses 1, 3 and 9, do not report any toxicities. The fourth, treated at 20, does.

and, since the plot method returns a ggplot object, it is easily customised.

plot(firstFour) + theme_light()

The same graph as above, but with a white background to the plot area rather than a grey one.

Now, update the model to obtain the posterior estimate of the dose-toxicity curve:

vignetteMcmcOptions <- McmcOptions(burnin = 100, step = 2, samples = 1000)
postSamples <- mcmc(
  data = firstFour,
  model = model,
  options = vignetteMcmcOptions
)

The posterior estimate of the dose toxicity curve is easily visualised:

plot(postSamples, model, firstFour)

A plot of the posterior after the first four participants. The mean probability of toxicity increases smoothly, with a slight convex curve, from about zero percent at a dose of zero to about 65% at a dose of 100. The confidence interval extends from 0% to about 25% at a dose of zero and from about 30% to about 90% at a dose of 100.

A visual representation of the model’s state is obtained with:

nextBest(
  my_next_best,
  doselimit = 100,
  samples = postSamples,
  model = model,
  data = empty_data
)$plot

Two graphs arranged in a single column. The upper graph shoes green lines of various heights that show the probability each dose is in the target toxicity range. There is a big arrow pointing to the bar at a dose of 20, indicating tat this dose has the highest probability of being in the target toxicity range. The lower graph as a similar series of red lines, indicating the probability that each dose is in the overdose range. There is a horizontal black dashed line at 25%, indicating that this is the highest acceptable probability of being in the overdose range. The red bars for doses of 30 and above all extend above 25%, indicating that their toxicity is unacceptable. The toxicity for doses of 20 and below lie below 25%.

The lower panel of the plot shows the posterior probability that each dose is in the overdose range. The dashed horizontal black line shows the acceptable risk of overdose: Doses with red lines which go above this line are considered toxic. The upper panel shows the probability that each dose is in the target toxicity range. Clearly, doses of 30 and 45 have the highest probability of being in the target toxicity range. However, the risk that both are in the overdose range is unacceptable. Therefore, 20 is the dose recommended for the next cohort.

We can produce a tabulation of the model state with

tabulatePosterior <- function(mcmcSamples, observedData) {
  as_tibble(
    nextBest(
      my_next_best,
      doselimit = 100,
      samples = mcmcSamples,
      model = model,
      data = observedData
    )$probs
  ) %>%
    left_join(
      tibble(
        dose = observedData@x,
        WithDLT = observedData@y
      ) %>%
        group_by(dose) %>%
        summarise(
          Treated = n(),
          WithDLT = sum(WithDLT),
          .groups = "drop"
        ),
      by = "dose"
    ) %>%
    replace_na(list(Treated = 0, WithDLT = 0)) %>%
    select(dose, Treated, WithDLT, target, overdose) %>%
    kableExtra::kable(
      col.names = c("Dose", "Treated", "With DLT", "Target range", "Overdose range"),
      digits = c(0, 0, 0, 3, 3)
    ) %>%
    kableExtra::add_header_above(c(" " = 1, "Participants" = 2, "Probability that dose is in " = 2))
}

tabulatePosterior(postSamples, firstFour)
Participants
Probability that dose is in
Dose Treated With DLT Target range Overdose range
1 1 0 0.062 0.007
3 1 0 0.097 0.031
9 1 0 0.202 0.112
20 1 1 0.305 0.273
30 0 0 0.335 0.430
45 0 0 0.244 0.676
60 0 0 0.138 0.829
80 0 0 0.076 0.912
100 0 0 0.051 0.945

From these presentations, we can see that:

  1. The highest dose so far administered is 20, so the escalation rule permits doses up to and including 40 to be considered as the dose for the next cohort. However…
  2. Doses of 30 and above are considered unsafe
  3. Of the remaining doses, 20 has the highest posterior probability of being in the target toxicity range
  4. A DLT has been reported

Items 1 and 4 in the list tell us both that the size of the next cohort should be three. Items 2 and 3 together imply that the highest dose that can be used in the next cohort is 20.

Thus, the model’s recommendation is that the next cohort should consist of three patients, each treated at 20. This can be confirmed programmatically:

nextMaxDose <- maxDose(my_increments, firstFour)
nextMaxDose
#> [1] 40

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples,
  model = model,
  data = firstFour
)
doseRecommendation$value
#> [1] 9

However, given that the probability that 20 is in the overdose range is only just less than the threshold of 0.25 (and because the only participant so far treated at 20 experienced a DLT) it would be a perfectly reasonable clinical decision to treat the next cohort at 10 - or, indeed, at any other dose below 20. There is absolutely no obligation to follow the CRM dose recommendation without consideration of other factors that might affect the choice of the most appropriate dose for the next cohort. However, for the purpose of exposition, we will treat the next cohort at 20, as recommended by the model.

We can confirm that the trial’s stopping rules have not been satisfied:

stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples,
  model,
  firstFour
)
#> [1] FALSE
#> attr(,"message")
#> attr(,"message")[[1]]
#> attr(,"message")[[1]][[1]]
#> [1] "Number of cohorts is 4 and thus reached the prespecified minimum number 3"
#> 
#> attr(,"message")[[1]][[2]]
#> [1] "Probability for target toxicity is 20 % for dose 9 and thus below the required 50 %"
#> 
#> 
#> attr(,"message")[[2]]
#> [1] "Number of patients is 4 and thus below the prespecified minimum number 20"
#> 
#> attr(,"individual")
#> attr(,"individual")[[1]]
#> [1] FALSE
#> attr(,"message")
#> attr(,"message")[[1]]
#> [1] "Number of cohorts is 4 and thus reached the prespecified minimum number 3"
#> 
#> attr(,"message")[[2]]
#> [1] "Probability for target toxicity is 20 % for dose 9 and thus below the required 50 %"
#> 
#> attr(,"individual")
#> attr(,"individual")[[1]]
#> [1] TRUE
#> attr(,"message")
#> [1] "Number of cohorts is 4 and thus reached the prespecified minimum number 3"
#> attr(,"report_label")
#> [1] "≥ 3 cohorts dosed"
#> 
#> attr(,"individual")[[2]]
#> [1] FALSE
#> attr(,"message")
#> [1] "Probability for target toxicity is 20 % for dose 9 and thus below the required 50 %"
#> attr(,"report_label")
#> [1] "P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5"
#> 
#> attr(,"report_label")
#> [1] NA
#> 
#> attr(,"individual")[[2]]
#> [1] FALSE
#> attr(,"message")
#> [1] "Number of patients is 4 and thus below the prespecified minimum number 20"
#> attr(,"report_label")
#> [1] "≥ 20 patients dosed"
#> 
#> attr(,"report_label")
#> [1] NA

The first full cohort

Assume that none of the three patients in the first full cohort report a DLT:

firstFullCohort <- Data(
  x = c(1, 3, 9, 20, 20, 20, 20),
  y = c(0, 0, 0, 1, 0, 0, 0),
  ID = 1:7,
  cohort = c(1:4, rep(5, 3)),
  doseGrid = doseGrid
)

Update the model:

postSamples1 <- mcmc(
  data = firstFullCohort,
  model = model,
  options = vignetteMcmcOptions
)

Tabulate the posterior:

tabulatePosterior(postSamples1, firstFullCohort)
Participants
Probability that dose is in
Dose Treated With DLT Target range Overdose range
1 1 0 0.015 0.005
3 1 0 0.012 0.012
9 1 0 0.079 0.018
20 4 1 0.243 0.084
30 0 0 0.322 0.226
45 0 0 0.355 0.497
60 0 0 0.244 0.719
80 0 0 0.100 0.884
100 0 0 0.047 0.943

Should the trial stop? If not, what dose should be used for the next cohort?

nextMaxDose <- maxDose(my_increments, firstFullCohort)
nextMaxDose
#> [1] 40

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples1,
  model = model,
  data = firstFullCohort
)
doseRecommendation$value
#> [1] 30

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples1,
  model,
  firstFullCohort
)
attributes(x) <- NULL
x
#> [1] FALSE

So the trial should continue, treating three patients in the next cohort at 30.

The second full cohort

Assume that none of the three patients in the next cohort report a DLT:

secondFullCohort <- Data(
  x = c(1, 3, 9, 20, 20, 20, 20, 30, 30, 30),
  y = c(0, 0, 0, 1, 0, 0, 0, 0, 0, 0),
  ID = 1:10,
  cohort = c(1:4, rep(5, 3), rep(6, 3)),
  doseGrid = doseGrid
)

Update the model:

postSamples2 <- mcmc(
  data = secondFullCohort,
  model = model,
  options = vignetteMcmcOptions
)

Tabulate the posterior:

tabulatePosterior(postSamples2, secondFullCohort)
Participants
Probability that dose is in
Dose Treated With DLT Target range Overdose range
1 1 0 0.001 0.000
3 1 0 0.012 0.000
9 1 0 0.029 0.004
20 4 1 0.141 0.026
30 3 0 0.360 0.077
45 0 0 0.406 0.330
60 0 0 0.301 0.598
80 0 0 0.143 0.823
100 0 0 0.091 0.890

The dose with the highest posterior probability of being in the target toxicity range is now 45, but this dose also has an unacceptably high probability of being in the overdose range. Therefore, the trial should continue and the next cohort should be treated at 30:

nextMaxDose <- maxDose(my_increments, secondFullCohort)
nextMaxDose
#> [1] 45

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples2,
  model = model,
  data = secondFullCohort
)
doseRecommendation$value
#> [1] 30

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples2,
  model,
  secondFullCohort
)
attributes(x) <- NULL
x
#> [1] FALSE

The third full cohort

Assume that none of the three patients in the third cohort report a DLT:

thirdFullCohort <- Data(
  x = c(1, 3, 9, rep(20, 4), rep(30, 6)),
  y = c(0, 0, 0, 1, rep(0, 9)),
  ID = 1:13,
  cohort = c(1:4, rep(5, 3), rep(6, 3), rep(7, 3)),
  doseGrid = doseGrid
)

Update the model:

postSamples3 <- mcmc(
  data = thirdFullCohort,
  model = model,
  options = vignetteMcmcOptions
)

Tabulate the posterior:

tabulatePosterior(postSamples3, thirdFullCohort)
Participants
Probability that dose is in
Dose Treated With DLT Target range Overdose range
1 1 0 0.000 0.000
3 1 0 0.003 0.000
9 1 0 0.008 0.002
20 4 1 0.067 0.005
30 6 0 0.193 0.032
45 0 0 0.430 0.163
60 0 0 0.337 0.493
80 0 0 0.200 0.743
100 0 0 0.124 0.847

45 is still the dose with the highest posterior probability of being in the target toxicity range, and its probability of being in the overdose range is now acceptable. Therefore, the trial should continue and the next cohort should be treated at 45:

nextMaxDose <- maxDose(my_increments, thirdFullCohort)
nextMaxDose
#> [1] 45

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples3,
  model = model,
  data = thirdFullCohort
)
doseRecommendation$value
#> [1] 45

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples3,
  model,
  thirdFullCohort
)
attributes(x) <- NULL
x
#> [1] FALSE

The fourth full cohort

Assume that none of the three patients in the fourth cohort report a DLT:

fourthFullCohort <- Data(
  x = c(1, 3, 9, rep(20, 4), rep(30, 6), rep(45, 3)),
  y = c(0, 0, 0, 1, rep(0, 12)),
  ID = 1:16,
  cohort = c(1:4, rep(5:8, each = 3)),
  doseGrid = doseGrid
)

Update the model:

postSamples4 <- mcmc(
  data = fourthFullCohort,
  model = model,
  options = vignetteMcmcOptions
)

Tabulate the posterior:

tabulatePosterior(postSamples4, fourthFullCohort)
Participants
Probability that dose is in
Dose Treated With DLT Target range Overdose range
1 1 0 0.000 0.000
3 1 0 0.002 0.000
9 1 0 0.004 0.000
20 4 1 0.024 0.001
30 6 0 0.112 0.006
45 3 0 0.343 0.076
60 0 0 0.425 0.322
80 0 0 0.306 0.583
100 0 0 0.179 0.757

60 is now the dose with the highest posterior probability of being in the target toxicity range, but its probability of being in the overdose range is unacceptable. Therefore, the trial should continue and the next cohort should be treated at 45:

nextMaxDose <- maxDose(my_increments, fourthFullCohort)
nextMaxDose
#> [1] 67.5

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples4,
  model = model,
  data = fourthFullCohort
)
doseRecommendation$value
#> [1] 45

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples4,
  model,
  fourthFullCohort
)
attributes(x) <- NULL
x
#> [1] FALSE

The fifth full cohort

Assume that two of the three patients in the fourth cohort report a DLT:

fifthFullCohort <- Data(
  x = c(1, 3, 9, rep(20, 4), rep(30, 6), rep(45, 6)),
  y = c(0, 0, 0, 1, rep(0, 13), 1, 1),
  ID = 1:19,
  cohort = c(1:4, rep(5:9, each = 3)),
  doseGrid = doseGrid
)

Update the model:

postSamples5 <- mcmc(
  data = fifthFullCohort,
  model = model,
  options = vignetteMcmcOptions
)

Tabulate the posterior:

tabulatePosterior(postSamples5, fifthFullCohort)
Participants
Probability that dose is in
Dose Treated With DLT Target range Overdose range
1 1 0 0.000 0.000
3 1 0 0.006 0.000
9 1 0 0.010 0.002
20 4 1 0.068 0.005
30 6 0 0.236 0.024
45 6 2 0.539 0.187
60 0 0 0.355 0.572
80 0 0 0.146 0.834
100 0 0 0.081 0.914

45 remains the dose with the highest posterior probability of being in the target toxicity range, and its probability of being in the overdose range is acceptable. Moreover, the probability that 45 is in the target toxicity range is above 0.5 and more than three cohorts have been treated in total. Therefore, the trial should stop and conclude that 45 is the MTD:

nextMaxDose <- maxDose(my_increments, fifthFullCohort)
nextMaxDose
#> [1] 67.5

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples5,
  model = model,
  data = fifthFullCohort
)
doseRecommendation$value
#> [1] 45

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples5,
  model,
  fifthFullCohort
)
x
#> [1] TRUE
#> attr(,"message")
#> attr(,"message")[[1]]
#> attr(,"message")[[1]][[1]]
#> [1] "Number of cohorts is 9 and thus reached the prespecified minimum number 3"
#> 
#> attr(,"message")[[1]][[2]]
#> [1] "Probability for target toxicity is 54 % for dose 45 and thus above the required 50 %"
#> 
#> 
#> attr(,"message")[[2]]
#> [1] "Number of patients is 19 and thus below the prespecified minimum number 20"
#> 
#> attr(,"individual")
#> attr(,"individual")[[1]]
#> [1] TRUE
#> attr(,"message")
#> attr(,"message")[[1]]
#> [1] "Number of cohorts is 9 and thus reached the prespecified minimum number 3"
#> 
#> attr(,"message")[[2]]
#> [1] "Probability for target toxicity is 54 % for dose 45 and thus above the required 50 %"
#> 
#> attr(,"individual")
#> attr(,"individual")[[1]]
#> [1] TRUE
#> attr(,"message")
#> [1] "Number of cohorts is 9 and thus reached the prespecified minimum number 3"
#> attr(,"report_label")
#> [1] "≥ 3 cohorts dosed"
#> 
#> attr(,"individual")[[2]]
#> [1] TRUE
#> attr(,"message")
#> [1] "Probability for target toxicity is 54 % for dose 45 and thus above the required 50 %"
#> attr(,"report_label")
#> [1] "P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5"
#> 
#> attr(,"report_label")
#> [1] NA
#> 
#> attr(,"individual")[[2]]
#> [1] FALSE
#> attr(,"message")
#> [1] "Number of patients is 19 and thus below the prespecified minimum number 20"
#> attr(,"report_label")
#> [1] "≥ 20 patients dosed"
#> 
#> attr(,"report_label")
#> [1] NA

Summarising the trial results

crmPack provides a wealth of information about the trial’s results. The following code snippets illustrate some of the many possibilities for how the trial might be summarised.

plot(fifthFullCohort)

A visual representation of the data after nineteen participants have been treated. One each at doses 1, 3 and 9; four at a dose of 20; 6 at a dose of 30 and 6 at a dose of 45. Toxicitiues were reported by participants 4 (at a dose of 20) and 18 and 19 (both at a dose of 45).

{rfig.alt = "A plot of the posterior after nineteen participants have been treated. The mean probability of toxicity increases smoothly from about zero percent at a dose of zero to about 55% at a dose of 100. The confidence interval extends from 0% to about 6% at a dose of zero and from about 22% to about 90% at a dose of 100."} plot(postSamples5, model, fifthFullCohort)

{rfig.alt = "Two graphs arranged in a single column. The upper graph shoes green lines of various heights that show the probability each dose is in the target toxicity range. There is a big arrow pointing to the bar at a dose of 45, indicating that this dose has the highest probability of being in the target toxicity range. The lower graph as a similar series of red lines, indicating the probability that each dose is in the overdose range. There is a horizontal black dashed line at 25%, indicating that this is the highest acceptable probability of being in the overdose range. The red bars for doses of 60 and above all extend above 25%, indicating that their toxicity is unacceptable. The toxicity for doses of 45 and below lie below 25%."} doseRecommendation$plot

With a little bit of work, we can obtain a more detailed summary and plot of the posterior probabilities of toxicity at each dose:

slotNames(model)
#> [1] "params"          "ref_dose"        "datamodel"       "priormodel"     
#> [5] "modelspecs"      "init"            "datanames"       "datanames_prior"
#> [9] "sample"

fullSamples <- tibble(
  Alpha = postSamples5@data$alpha0,
  Beta = postSamples5@data$alpha1
) %>%
  expand(nesting(Alpha, Beta), Dose = doseGrid) %>%
  rowwise() %>%
  mutate(P = probFunction(model, alpha0 = Alpha, alpha1 = Beta)(dose = Dose)) %>%
  ungroup()

fullSummary <- fullSamples %>%
  group_by(Dose) %>%
  summarise(
    Mean = mean(P),
    Median = median(P),
    Q = list(quantile(P, probs = c(0.05, 0.1, 0.25, 0.75, 0.9, 0.95), na.rm = TRUE))
  ) %>%
  unnest_wider(
    col = Q,
    names_repair = function(.x) {
      ifelse(
        str_detect(.x, "\\d+%"),
        sprintf("Q%02.0f", as.numeric(str_remove_all(.x, "%"))),
        .x
      )
    }
  )
#> Warning in sprintf("Q%02.0f", as.numeric(str_remove_all(.x, "%"))): NAs
#> introduced by coercion

fullSummary %>%
  kableExtra::kable(
    col.names = c("Dose", "Mean", "Median", "5th", "10th", "25th", "75th", "90th", "95th"),
    digits = c(0, rep(3, 8))
  ) %>%
  add_header_above(c(" " = 3, "Quantiles" = 6)) %>%
  add_header_above(c(" " = 1, "P(Toxicity)" = 8))
P(Toxicity)
Quantiles
Dose Mean Median 5th 10th 25th 75th 90th 95th
1 0.006 0.000 0.000 0.000 0.000 0.003 0.015 0.027
3 0.013 0.003 0.000 0.000 0.000 0.014 0.036 0.058
9 0.036 0.018 0.001 0.001 0.005 0.049 0.097 0.124
20 0.090 0.073 0.010 0.015 0.034 0.126 0.193 0.230
30 0.153 0.137 0.040 0.051 0.084 0.209 0.275 0.314
45 0.266 0.260 0.100 0.135 0.186 0.335 0.411 0.469
60 0.381 0.363 0.174 0.211 0.277 0.490 0.579 0.619
80 0.506 0.484 0.241 0.297 0.375 0.645 0.743 0.806
100 0.594 0.585 0.291 0.347 0.448 0.750 0.849 0.902 

fullSamples %>%
  filter(Dose > 9) %>%
  ggplot() +
  geom_density(aes(x = P, color = as.factor(Dose))) +
  theme_light() +
  theme(
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.ticks.y = element_blank()
  ) +
  labs(
    title = "Posterior PDFs for doses > 9",
    colour = "Dose"
  )

A graph showing the posterior density of of the probability of toxicity for all doses greater than nine. The mode of each density moves to the right as dose increases. The densities for low doses are heaviliy skewed to the left. Densities for higher doses are more symmetric and flatter.

fullSummary %>%
  ggplot(aes(x = Dose)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), fill = "steelblue", alpha = 0.25) +
  geom_ribbon(aes(ymin = Q10, ymax = Q90), fill = "steelblue", alpha = 0.25) +
  geom_ribbon(aes(ymin = Q25, ymax = Q75), fill = "steelblue", alpha = 0.25) +
  geom_line(aes(y = Mean), colour = "black") +
  geom_line(aes(y = Median), colour = "blue") +
  theme_light() +
  labs(
    title = "Posterior Dose toxicity curve",
    colour = "Dose",
    y = "P(Toxicity)"
  )
#> Ignoring unknown labels:
#> • colour : "Dose"

A visual representation of the posterior dose - toxicity curve. Very closely spaced solid lines in black and blue, representing the mean and median estimate of toxicity for each dose rise almost linearly from zero percent for a dose of zero to about 55% for a dose of 100. Shading extends to each side of the two solid lines. The transparency of the shading increases with distance from the solid lines. The shading is funnel shaped, with a narrow mneck at a dose of 100 and a wider mouth at a dose of 100. The shading represents the central 90%, 80% and 50% confidence intervals for the posterior mean estimate of toxicity at each dose.

Note

The analyses presented in this vignette have used chains of a very short length. This is purely for convenience. Analyses of real trials should use considerably longer chains. As an example, an effective sample size of approximately 40,000 is required to estimate a percentage to within ±1%.

References

Neuenschwander, Beat, Michael Branson, and Thomas Gsponer. 2008. “Critical aspects of the Bayesian approach to phase I cancer trials.” Statistics in Medicine 27 (13): 2420–39. https://onlinelibrary.wiley.com/doi/10.1002/sim.3230.