Package 'escalation'

Description

escalation provides methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, ncluding the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.

Cast dose_paths object to tibble.

Description

Cast dose_paths object to tibble.

Usage

## S3 method for class 'dose_paths'
as_tibble(x, ...)

Arguments

Value

Object of class tibble

Cast dose_selector object to tibble.

Description

Cast dose_selector object to tibble.

Usage

## S3 method for class 'selector'
as_tibble(x, ...)

Arguments

Value

Object of class tibble

Description

Cumulative statistics are shown to gauge how the simulations converge.

Usage

## S3 method for class 'simulations_collection'
as_tibble(x, target_dose = NULL, alpha = 0.05, ...)

Arguments

Value

a tibble with cols:

• dose, the dose-level

• n, cumulative inference using the first n simulated iterations

• design.x, The first design in the comparison, aka design X

• hit.x, logical showing if design X recommended dose in iterate n

• design.y, The second design in the comparison, aka design Y

• hit.x, logical showing if design Y recommended dose in iterate n

• X, cumulative sum of hit.x within dose, i.e. count of recommendations

• X2, cumulative sum of hit.x^2 within dose

• Y, cumulative sum of hit.y within dose, i.e. count of recommendations

• Y2, cumulative sum of hit.y^2 within dose

• XY, cumulative sum of hit.x * hit.y within dose

• psi1, X / n

• psi2, Y / n

• v_psi1, variance of psi1

• v_psi2, variance of psi2

• cov_psi12, covariance of psi1 and psi2

• delta, psi1 - psi2

• v_delta, variance of delta

• se_delta, standard error of delta

• delta_l, delta - q * se_delta, where q is alpha / 2 normal quantile

• delta_u, delta + q * se_delta, where q is alpha / 2 normal quantile

• comparison, Label of design.x vs design.y, using design names

Description

Crystallise a set of dose_paths with probabilities to calculate how likely each path is. Once probabilised in this way, the probabilities of the terminal nodes in this set of paths will sum to 1. This allows users to calculate operating characteristics.

Usage

calculate_probabilities(dose_paths, true_prob_tox, true_prob_eff = NULL, ...)

Arguments

See Also

dose_paths

Examples

# Phase 1 example.
# Calculate dose paths for the first three cohorts in a 3+3 trial of 5 doses:
paths <- get_three_plus_three(num_doses = 5) %>%
  get_dose_paths(cohort_sizes = c(3, 3, 3))

# Set the true probabilities of toxicity
true_prob_tox <- c(0.12, 0.27, 0.44, 0.53, 0.57)
# And calculate exact operating performance
x <- paths %>% calculate_probabilities(true_prob_tox)
prob_recommend(x)

# Phase 1/2 example.
prob_select = c(0.1, 0.3, 0.5, 0.07, 0.03)
selector_factory <- get_random_selector(prob_select = prob_select,
                                        supports_efficacy = TRUE)
paths <- selector_factory %>% get_dose_paths(cohort_sizes = c(2, 2))
true_prob_eff <- c(0.27, 0.35, 0.41, 0.44, 0.45)
x <- paths %>% calculate_probabilities(true_prob_tox = true_prob_tox,
                                       true_prob_eff = true_prob_eff)
prob_recommend(x)

Description

Check the consistency of a dose_selector instance

Usage

check_dose_selector_consistency(x)

Arguments

Examples

boin_fitter <- get_boin(num_doses = 5, target = 0.3)
x <- fit(boin_fitter, "1NNN")
check_dose_selector_consistency(x)

Description

Get a vector of integers that reflect the cohorts to which the evaluated patients belong.

Usage

cohort(x, ...)

Arguments

Value

an integer vector

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model <- get_dfcrm(skeleton = skeleton, target = target)
fit <- model %>% fit('1NNN 2NTN')
fit %>% cohort()

Description

Sample times between patient arrivals using the exponential distribution.

Usage

cohorts_of_n(n = 3, mean_time_delta = 1)

Arguments

Value

data.frame with column time_delta containing durations of time between patient arrivals.

Examples

cohorts_of_n()
cohorts_of_n(n = 10, mean_time_delta = 5)

Description

Should this dose-finding experiment continue? Or have circumstances prevailed that dictate this trial should stop? This method is critical to the automatic calculation of statistical operating characteristics and dose-pathways. You add stopping behaviours to designs using calls like stop_at_n and stop_when_too_toxic.

Usage

continue(x, ...)

Arguments

Value

logical

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model1 <- get_dfcrm(skeleton = skeleton, target = target)
fit1 <- model1 %>% fit('1NNN 2NTN')
fit1 %>% continue()

model2 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  stop_at_n(n = 6)
fit2 <- model2 %>% fit('1NNN 2NTN')
fit2 %>% continue()

Description

Plot the convergence processes from a collection of simulations.

Usage

convergence_plot(x, ...)

Arguments

Value

a ggplot2 plot

Examples

## Not run: 
# See ? simulate_compare

## End(Not run)

Description

Class to house the latent random variables that govern toxicity and efficacy events in patients. Instances of this class can be used in simulation-like tasks to effectively use the same simulated individuals in different designs, thus supporting reduced Monte Carlo error and more efficient comparison. This class differs from PatientSample in that the latent variables that underlie efficacy and toxicity events, and therefore those events themselves, are correlated, e.g. for positive association, a patient that experiences toxicity has increased probability of experiencing efficacy too. Correlated uniformly-distributed variables are obtained by inverting bivariate normal variables. The extent to which the events are correlated is controlled by rho, the correlation of the two normal variables.

Super class

escalation::PatientSample -> CorrelatedPatientSample

Public fields

Methods

Public methods

• CorrelatedPatientSample$new()

• CorrelatedPatientSample$expand_to()

• CorrelatedPatientSample$clone()

Method new()

Creator.

Usage

CorrelatedPatientSample$new(
  num_patients = 0,
  time_to_tox_func = function() runif(n = 1),
  time_to_eff_func = function() runif(n = 1),
  rho = 0
)

Arguments

Returns

[CorrelatedPatientSample].

Method expand_to()

Expand sample to size at least num_patients

Usage

CorrelatedPatientSample$expand_to(num_patients)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

CorrelatedPatientSample$clone(deep = FALSE)

Arguments

References

Sweeting, M., Slade, D., Jackson, D., & Brock, K. (2024). Potential outcome simulation for efficient head-to-head comparison of adaptive dose-finding designs. arXiv preprint arXiv:2402.15460

Description

dose_paths reflect all possible paths a dose-finding trial may take. When the probability of those paths is calculated using an assumed set of true dose-event probabilities, in this package those paths are said to be crysallised. Once crystallised, operating charactersitics can be calculated.

Usage

crystallised_dose_paths(
  dose_paths,
  true_prob_tox,
  true_prob_eff = NULL,
  terminal_nodes
)

Arguments

Value

An object of type crystallised_dose_paths

Examples

# Calculate dose paths for the first three cohorts in a 3+3 trial of 5 doses:
paths <- get_three_plus_three(num_doses = 5) %>%
  get_dose_paths(cohort_sizes = c(3, 3, 3))

# Set the true probabilities of toxicity
true_prob_tox <- c(0.12, 0.27, 0.44, 0.53, 0.57)
# Crytallise the paths with the probabilities of toxicity
x <- paths %>% calculate_probabilities(true_prob_tox)
# And then examine, for example, the probabilities of recommending each dose
# at the terminal nodes of these paths:
prob_recommend(x)

Description

This method continues a dose-finding trial until there are n patients at a dose. Once that condition is met, it delegates stopping responsibility to its parent dose selector, whatever that might be. This class is greedy in that it meets its own needs before asking any other selectors in a chain what they want. Thus, different behaviours may be achieved by nesting dose selectors in different orders. See examples.

Usage

demand_n_at_dose(parent_selector_factory, n, dose)

Arguments

Value

an object of type selector_factory that can fit a dose-finding model to outcomes.

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25

# This model will demand 9 at any dose before it countenances stopping.
model1 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  demand_n_at_dose(n = 9, dose = 'any')

# This model will recommend continuing:
model1 %>% fit('1NNT 1NNN 2TNN 2NNN') %>% continue()
# It tells you to continue because there is no selector considering when
# you should stop - dfcrm implements no stopping rule by default.

# In contrast, we can add a stopping selector to discern the behaviour of
# demand_n_at_dose. We will demand 9 are seen at the recommended dose before
# stopping is permitted in model3:
model2 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  stop_at_n(n = 12)
model3 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  stop_at_n(n = 12) %>%
  demand_n_at_dose(n = 9, dose = 'recommended')

# This model advocates stopping because 12 patients are seen in total:
model2 %>% fit('1NNN 1NNN 2TNN 2NNN') %>% continue()
# But this model advocates continuing because 9 patients have not been seen
# at any dose yet:
model3 %>% fit('1NNN 1NNN 2TNN 2NNN') %>% continue()
# This shows how demand_n_at_dose overrides stopping behaviours that come
# before it in the daisychain.

# Once 9 are seen at the recommended dose, the decision to stop is made:
fit <- model3 %>% fit('1NNN 1NNN 2TNN 2NNN 2TTN')
fit %>% continue()
fit %>% recommended_dose()

Description

This method optionally prevents dose selectors from skipping doses when escalating and / or deescalating. The default is that skipping when escalating is prevented but skipping when deescalating is permitted, but both of these behaviours can be altered.

Usage

dont_skip_doses(
  parent_selector_factory,
  when_escalating = TRUE,
  when_deescalating = FALSE
)

Arguments

Value

an object of type selector_factory that can fit a dose-finding model to outcomes.

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model1 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  dont_skip_doses()
fit1 <- model1 %>% fit('1NNN')

model2 <- get_dfcrm(skeleton = skeleton, target = target)
fit2 <- model2 %>% fit('1NNN')

# fit1 will not skip doses
fit1 %>% recommended_dose()
# But fit2 will:
fit2 %>% recommended_dose()

# Similar demonstration for de-escalation
model1 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  dont_skip_doses(when_deescalating = TRUE)
fit1 <- model1 %>% fit('1NNN 2N 3TTT')

model2 <- get_dfcrm(skeleton = skeleton, target = target)
fit2 <- model2 %>% fit('1NNN 2N 3TTT')

# fit1 will not skip doses
fit1 %>% recommended_dose()
# But fit2 will:
fit2 %>% recommended_dose()

Description

Get a vector of logical values reflecting whether each dose is admissible. Admissibility is defined in different ways for different models, and may not be defined at all in some models. For instance, in the TPI method, doses are inadmissible when the posterior probability is high that the toxicity rate exceeds the target value. In contrast, admissibility is not defined in the general CRM model (but it can be added with auxiliary classes). In this latter case, doses are implicitly considered to be admissible, by default.

Usage

dose_admissible(x, ...)

Arguments

Value

a logical vector

Examples

outcomes <- '1NNN 2TTT'

# TPI example. This method defines admissibility.
fit1 <- get_tpi(num_doses = 5, target = 0.3, k1 = 1, k2 = 1.5,
                exclusion_certainty = 0.95) %>%
  fit(outcomes)
fit1 %>% dose_admissible()

# Ordinary CRM example with no admissibility function.
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
fit2 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  fit(outcomes)
fit2 %>% dose_admissible()

# Same CRM example with added admissibility function
fit3 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  stop_when_too_toxic(dose = 1, tox_threshold = target, confidence = 0.8) %>%
  fit(outcomes)
fit3 %>% dose_admissible()

Description

Get the integers from 1 to the number of doses under investigation.

Usage

dose_indices(x, ...)

Arguments

Value

an integer vector

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model <- get_dfcrm(skeleton = skeleton, target = target)
fit <- model %>% fit('1NNN 2NTN')
fit %>% dose_indices()

Description

A dose-escalation design exists to select doses in response to observed outcomes. The entire space of possible responses can be calculated to show the behaviour of a design in response to all feasible outcomes. The get_dose_paths function performs that task and returns an instance of this object.

Usage

dose_paths()

See Also

selector

Examples

# Calculate dose-paths for the 3+3 design:
paths <- get_three_plus_three(num_doses = 5) %>%
  get_dose_paths(cohort_sizes = c(3, 3))

Description

This function does not need to be called by users. It is used internally.

Usage

dose_paths_function(selector_factory)

Arguments

Value

A function.

Description

Get a vector of the dose-levels that have been administered to patients.

Usage

doses_given(x, ...)

Arguments

Value

an integer vector

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model <- get_dfcrm(skeleton = skeleton, target = target)
fit <- model %>% fit('1NNN 2NTN')
fit %>% doses_given()

Description

Get a vector of the binary efficacy outcomes for evaluated patients.

Usage

eff(x, ...)

Arguments

Value

an integer vector

Examples

prob_select = c(0.1, 0.3, 0.5, 0.07, 0.03)
model <- get_random_selector(prob_select = prob_select,
                             supports_efficacy = TRUE)
x <- model %>% fit('1NTN 2EN 5BB')
eff(x)

Description

Get the number of toxicities seen at each dose under investigation.

Usage

eff_at_dose(x, ...)

Arguments

Value

an integer vector

Examples

prob_select = c(0.1, 0.3, 0.5, 0.07, 0.03)
model <- get_random_selector(prob_select = prob_select,
                             supports_efficacy = TRUE)
x <- model %>% fit('1NTN 2EN 5BB')
eff_at_dose(x)

Description

Get the minimum permissible efficacy rate, if supported. NULL if not.

Usage

eff_limit(x, ...)

Arguments

Value

numeric

Examples

efftox_priors <- trialr::efftox_priors
p <- efftox_priors(alpha_mean = -7.9593, alpha_sd = 3.5487,
                   beta_mean = 1.5482, beta_sd = 3.5018,
                   gamma_mean = 0.7367, gamma_sd = 2.5423,
                   zeta_mean = 3.4181, zeta_sd = 2.4406,
                   eta_mean = 0, eta_sd = 0.2,
                   psi_mean = 0, psi_sd = 1)
real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0)
model <- get_trialr_efftox(real_doses = real_doses,
                           efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                           p_e = 0.1, p_t = 0.1,
                           eff0 = 0.5, tox1 = 0.65,
                           eff_star = 0.7, tox_star = 0.25,
                           priors = p, iter = 1000, chains = 1, seed = 2020)
x <- model %>% fit('1N 2E 3B')
eff_limit(x)

Description

Get the empirical or observed efficacy rate seen at each dose under investigation. This is simply the number of efficacies divded by the number of patients evaluated.

Usage

empiric_eff_rate(x, ...)

Arguments

Value

a numerical vector

Examples

prob_select = c(0.1, 0.3, 0.5, 0.07, 0.03)
model <- get_random_selector(prob_select = prob_select,
                             supports_efficacy = TRUE)
x <- model %>% fit('1NTN 2EN 5BB')
empiric_tox_rate(x)

Description

Get the empirical or observed toxicity rate seen at each dose under investigation. This is simply the number of toxicities divded by the number of patients evaluated.

Usage

empiric_tox_rate(x, ...)

Arguments

Value

a numerical vector

Examples

# CRM example
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
outcomes <- '1NNN 2NTN'
fit <- get_dfcrm(skeleton = skeleton, target = target) %>% fit(outcomes)
fit %>% empiric_tox_rate()

Description

This function stops with en error if it detects that outcomes describing a trial path have diverged from that advocated by the 3+3 method.

Usage

enforce_three_plus_three(outcomes, allow_deescalate = FALSE)

Arguments

Value

Nothing. Function stops if problem detected.

Examples

## Not run: 
enforce_three_plus_three('1NNN 2NTN 2NNN')  # OK
enforce_three_plus_three('1NNN 2NTN 2N')  # OK too, albeit in-progress cohort
enforce_three_plus_three('1NNN 1N')  # Not OK because should have escalated

## End(Not run)

Description

Fit a dose-finding model to some outcomes.

Usage

fit(selector_factory, outcomes, ...)

Arguments

Value

Object of generic type selector.

See Also

selector, selector_factory

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model <- get_dfcrm(skeleton = skeleton, target = target)
fit <- model %>% fit('1NNN 2NTN')
fit %>% recommended_dose()  # Etc

Description

This method creates a dose selector that will follow a pre-specified trial path. Whilst the trial path is matched by realised outcomes, the selector will recommend the next dose in the desired sequence. As soon as the observed outcomes diverge from the desired path, the selector stops giving dose recommendations. This makes it possible, for instance, to specify a fixed escalation plan that should be followed until the first toxicity is seen. This tactic is used by some model-based designs to get rapidly to the doses where the action is. See, for example, the dfcrm package and Cheung (2011).

Usage

follow_path(path)

Arguments

Value

an object of type selector_factory that can fit a dose-finding model to outcomes.

References

Cheung. Dose Finding by the Continual Reassessment Method. 2011. Chapman and Hall/CRC. ISBN 9781420091519

Examples

model1 <- follow_path(path = '1NNN 2NNN 3NNN 4NNN')

fit1 <- model1 %>% fit('1NNN 2N')
fit1 %>% recommended_dose()
fit1 %>% continue()
# The model recommends continuing at dose 2 because the observed outcomes
# perfectly match the desired escalation path.

fit2 <- model1 %>% fit('1NNN 2NT')
fit2 %>% recommended_dose()
fit2 %>% continue()
# Uh oh. Toxicity has now been seen, the outcomes diverge from the sought
# path, hence this class recommends no dose now.
# At this point, we can hand over dose selection decisions to another class
# by chaining them together, like:
model2 <- follow_path(path = '1NNN 2NNN 3NNN 4NNN') %>%
  get_dfcrm(skeleton = c(0.05, 0.1, 0.25, 0.4, 0.6), target = 0.25)
fit3 <- model2 %>% fit('1NNN 2NT')
# Now the CRM model is using all of the outcomes to calculate the next dose:
fit3 %>% recommended_dose()
fit3 %>% continue()

Description

Get an object to fit the BOIN model using the BOIN package.

Usage

get_boin(num_doses, target, use_stopping_rule = TRUE, ...)

Arguments

Value

an object of type selector_factory that can fit the BOIN model to outcomes.

References

Yan, F., Pan, H., Zhang, L., Liu, S., & Yuan, Y. (2019). BOIN: An R Package for Designing Single-Agent and Drug-Combination Dose-Finding Trials Using Bayesian Optimal Interval Designs. Journal of Statistical Software, 27(November 2017), 0–35. https://doi.org/10.18637/jss.v000.i00

Liu, S., & Yuan, Y. (2015). Bayesian optimal designs for Phase I clinical trials. J. R. Stat. Soc. C, 64, 507–523. https://doi.org/10.1111/rssc.12089

Examples

target <- 0.25
model1 <- get_boin(num_doses = 5, target = target)

outcomes <- '1NNN 2NTN'
model1 %>% fit(outcomes) %>% recommended_dose()

Description

This function returns an object that can be used to fit the BOIN12 model for phase I/II dose-finding, i.e. it selects doses according to efficacy and toxicity outcomes.

Usage

get_boin12(
  num_doses,
  phi_t,
  phi_e,
  u1 = 100,
  u2,
  u3,
  u4 = 0,
  n_star = 6,
  c_t = 0.95,
  c_e = 0.9,
  start_dose = 1,
  prior_alpha = 1,
  prior_beta = 1,
  ...
)

Arguments

Value

an object of type selector_factory that can fit the BOIN12 model to outcomes.

References

Lin, R., Zhou, Y., Yan, F., Li, D., & Yuan, Y. (2020). BOIN12: Bayesian optimal interval phase I/II trial design for utility-based dose finding in immunotherapy and targeted therapies. JCO precision oncology, 4, 1393-1402.

Examples

# Examples in Lin et al.
model <- get_boin12(num_doses = 5, phi_t = 0.35, phi_e = 0.25,
                    u2 = 40, u3 = 60, n_star = 6)
fit <- model %>% fit('1NNN 2ENT 3ETT 2EEN')
fit %>% recommended_dose()
fit %>% continue()
fit %>% is_randomising()
fit %>% dose_admissible()
fit %>% prob_administer()

Description

This function returns an object that can be used to fit a CRM model using methods provided by the dfcrm package.

Dose selectors are designed to be daisy-chained together to achieve different behaviours. This class is a **resumptive** selector, meaning it carries on when the previous dose selector, where present, has elected not to continue. For example, this allows instances of this class to be preceded by a selector that follows a fixed path in an initial escalation plan, such as that provided by follow_path. In this example, when the observed trial outcomes deviate from that initial plan, the selector following the fixed path elects not to continue and responsibility passes to this class. See Examples.

The time-to-event variant, TITE-CRM, is used via the dfcrm::titecrm function when you specify tite = TRUE. This weights the observations to allow dose-selections based on partially observed outcomes.

Usage

get_dfcrm(parent_selector_factory = NULL, skeleton, target, tite = FALSE, ...)

Arguments

Value

an object of type selector_factory that can fit the CRM model to outcomes.

References

Cheung, K. 2019. dfcrm: Dose-Finding by the Continual Reassessment Method. R package version 0.2-2.1. https://CRAN.R-project.org/package=dfcrm

Cheung, K. 2011. Dose Finding by the Continual Reassessment Method. Chapman and Hall/CRC. ISBN 9781420091519

O’Quigley J, Pepe M, Fisher L. Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics. 1990;46(1):33-48. doi:10.2307/2531628

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model1 <- get_dfcrm(skeleton = skeleton, target = target)

# By default, dfcrm fits the empiric model:
outcomes <- '1NNN 2NTN'
model1 %>% fit(outcomes) %>% recommended_dose()

# But we can provide extra args to get_dfcrm that are than passed onwards to
# the call to dfcrm::crm to override the defaults. For example, if we want
# the one-parameter logistic model:
model2 <- get_dfcrm(skeleton = skeleton, target = target, model = 'logistic')
model2 %>% fit(outcomes) %>% recommended_dose()
# dfcrm does not offer a two-parameter logistic model but other classes do.

# We can use an initial dose-escalation plan, a pre-specified path that
# should be followed until trial outcomes deviate, at which point the CRM
# model takes over. For instance, if we want to use two patients at each of
# the first three doses in the absence of toxicity, irrespective the model's
# advice, we would run:
model1 <- follow_path('1NN 2NN 3NN') %>%
  get_dfcrm(skeleton = skeleton, target = target)

# If outcomes match the desired path, the path is followed further:
model1 %>% fit('1NN 2N') %>% recommended_dose()

# But when the outcomes diverge:
model1 %>% fit('1NN 2T') %>% recommended_dose()

# Or the pre-specified path comes to an end:
model1 %>% fit('1NN 2NN 3NN') %>% recommended_dose()
# The CRM model takes over.

Description

Get an object to fit the TITE-CRM model using the dfcrm package.

Usage

get_dfcrm_tite(parent_selector_factory = NULL, skeleton, target, ...)

Arguments

Details

This function is a short-cut to get_dfcrm(tite = TRUE). See get_dfcrm for full details.

Value

an object of type selector_factory that can fit the CRM model to outcomes.

References

Cheung, K. 2019. dfcrm: Dose-Finding by the Continual Reassessment Method. R package version 0.2-2.1. https://CRAN.R-project.org/package=dfcrm

Cheung, K. 2011. Dose Finding by the Continual Reassessment Method. Chapman and Hall/CRC. ISBN 9781420091519

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model1 <- get_dfcrm_tite(skeleton = skeleton, target = target)
outcomes <- data.frame(
  dose = c(1, 1, 2, 2, 3, 3),
  tox = c(0, 0, 0, 0, 1, 0),
  weight = c(1, 1, 1, 0.9, 1, 0.5),
  cohort = c(1, 2, 3, 4, 5, 6)
)
fit <- model1 %>% fit(outcomes)

Description

A dose-escalation design exists to select doses in response to observed outcomes. The entire space of possible responses can be calculated to show the behaviour of a design in response to all feasible outcomes. This function performs that task.

Usage

get_dose_paths(selector_factory, cohort_sizes, ...)

Arguments

Value

Object of type dose_paths.

Examples

# Calculate paths for a 3+3 design for the next two cohorts of three patients
paths <- get_three_plus_three(num_doses = 5) %>%
  get_dose_paths(cohort_sizes = c(3, 3))

Description

Get posterior model weights for several empiric CRM skeletons, assuming a normal prior on the beta model parameter

Usage

get_empiric_crm_skeleton_weights(
  skeletons,
  events_at_dose,
  n_at_dose,
  prior = rep(1, nrow(skeletons))
)

Arguments

Value

numerical vector, posterior weights of the skeletons.

Description

The modified toxicity probability interval (mTPI)is a dose-escalation design by Ji et al. As the name suggests, it is an adaptation of the TPI design.

Usage

get_mtpi(
  parent_selector_factory = NULL,
  num_doses,
  target,
  epsilon1,
  epsilon2,
  exclusion_certainty,
  alpha = 1,
  beta = 1,
  ...
)

Arguments

Value

an object of type selector_factory that can fit the TPI model to outcomes.

Details

The design seeks a dose with probability of toxicity $p_{i}$ close to a target probability $p_{T}$ by iteratively calculating the interval

$p_{T} - \epsilon_{1} < p_{i} < p_{T} + \epsilon_{2}$

In this model, $\epsilon_{1}$ and $\epsilon_{2}$ are specified constants. $p_{i}$ is estimated by a Bayesian beta-binomial conjugate model

$p_{i} | data \sim Beta(\alpha + x_{1}, \beta + n_{i} - x_{i}),$

where $x_{i}$ is the number of toxicities observed and $n_{i}$ is the number of patients treated at dose $i$, and $\alpha$ and $\beta$ are hyperparameters for the beta prior on $p_{i}$. A dose is excluded as inadmissible if

$P(p_{i} > p_{T} | data) > \xi$

The trial commences at a starting dose, possibly dose 1. If dose $i$ has just been evaluated in patient(s), dose selection decisions proceed by calculating the unit probability mass of the true toxicity rate at dose $i$ using the partition of the probability space $p_{i} < p_{T} - \epsilon_{1}$, $p_{T} - \epsilon_{1} < p_{i} < p_{T} + \epsilon_{2}$, and $p_{i} > p_{T} + \epsilon_{2}$. The unit probability mass (UPM) of an interval is the posterior probability that the true toxicity rate belongs to the interval divided by the width of the interval. The interval with maximal UPM determines the recommendation for the next patient(s), with the intervals corresponding to decisions tp escalate, stay, and de-escalate dose, respectively. Further to this are rules that prevent escalation to an inadmissible dose. In their paper, the authors demonstrate acceptable operating performance using $\alpha = \beta = 1$, $K_{1} = 1$, $K_{2} = 1.5$ and $\xi = 0.95$. See the publications for full details.

References

Ji, Y., Liu, P., Li, Y., & Bekele, B. N. (2010). A modified toxicity probability interval method for dose-finding trials. Clinical Trials, 7(6), 653-663. https://doi.org/10.1177/1740774510382799

Ji, Y., & Yang, S. (2017). On the Interval-Based Dose-Finding Designs, 1-26. Retrieved from https://arxiv.org/abs/1706.03277

Examples

target <- 0.25
model1 <- get_mtpi(num_doses = 5, target = target, epsilon1 = 0.05,
  epsilon2 = 0.05, exclusion_certainty = 0.95)

outcomes <- '1NNN 2NTN'
model1 %>% fit(outcomes) %>% recommended_dose()

Description

The modified toxicity probability interval 2 (mTPI-2) is a dose-escalation design by Guo et al. As the name suggests, it is an adaptation of the mTPI design.

Usage

get_mtpi2(
  parent_selector_factory = NULL,
  num_doses,
  target,
  epsilon1,
  epsilon2,
  exclusion_certainty,
  alpha = 1,
  beta = 1,
  ...
)

Arguments

Value

an object of type selector_factory that can fit the mTPI-2 model to outcomes.

Details

The design seeks a dose with probability of toxicity $p_{i}$ close to a target probability $p_{T}$ by iteratively calculating the interval

$p_{T} - \epsilon_{1} < p_{i} < p_{T} + \epsilon_{2}$

In this model, $\epsilon_{1}$ and $\epsilon_{2}$ are specified constants. $p_{i}$ is estimated by a Bayesian beta-binomial conjugate model

$p_{i} | data \sim Beta(\alpha + x_{1}, \beta + n_{i} - x_{i}),$

where $x_{i}$ is the number of toxicities observed and $n_{i}$ is the number of patients treated at dose $i$, and $\alpha$ and $\beta$ are hyperparameters for the beta prior on $p_{i}$. A dose is excluded as inadmissible if

$P(p_{i} > p_{T} | data) > \xi$

The trial commences at a starting dose, possibly dose 1. If dose $i$ has just been evaluated in patient(s), dose selection decisions proceed by calculating the unit probability mass of the true toxicity rate at dose $i$ using the partition of the probability space into subintervals with equal length given by$(\epsilon_{1} + \epsilon_{2})$. $EI$ is the equivalence interval $p_{T} - epsilon_{1}, p_{T} - epsilon_{2}$, with $LI$ the set of all intervals below, and $HI$ the set of all intervals above. The unit probability mass (UPM) of an interval is the posterior probability that the true toxicity rate belongs to the interval divided by the width of the interval. The interval with maximal UPM determines the recommendation for the next patient(s), with the intervals corresponding to decisions to escalate, stay, and de-escalate dose, respectively. Further to this are rules that prevent escalation to an inadmissible dose. In the original mTPI paper, the authors demonstrate acceptable operating performance using $\alpha = \beta = 1$, $K_{1} = 1$, $K_{2} = 1.5$ and $\xi = 0.95$. The authors of the mTPI-2 approach show desirable performance as compared to the original mTPI method, under particular parameter choices. See the publications for full details.

References

Ji, Y., Liu, P., Li, Y., & Bekele, B. N. (2010). A modified toxicity probability interval method for dose-finding trials. Clinical Trials, 7(6), 653–663. https://doi.org/10.1177/1740774510382799

Ji, Y., & Yang, S. (2017). On the Interval-Based Dose-Finding Designs, 1–26. Retrieved from https://arxiv.org/abs/1706.03277

Guo, W., Wang, SJ., Yang, S., Lynn, H., Ji, Y. (2017). A Bayesian Interval Dose-Finding Design Addressing Ockham's Razor: mTPI-2. https://doi.org/10.1016/j.cct.2017.04.006

Examples

target <- 0.25
model1 <- get_mtpi2(num_doses = 5, target = target, epsilon1 = 0.05,
  epsilon2 = 0.05, exclusion_certainty = 0.95)

outcomes <- '1NNN 2NTN'
model1 %>% fit(outcomes) %>% recommended_dose()

Description

An instance of PatientSample, or one of its subclasses like CorrelatedPatientSample, reflects one particular state of the world, where patient i would reliably experience a toxicity or efficacy event if treated at a particular dose. This function, given true toxicity and efficacy probabilities at doses 1, ..., num_doses, calculates 0/1 matrices to reflect whether the patients in those samples would have experienced toxicity and efficacy at the doses, had they been dosed as such. Using the vernacular of causal inference, these are _potential outcomes_. At any single instant, a patient can only be dosed at one dose, so only one of the outcomes for a patient would in reality have been observed; the rest are counterfactual.

Usage

get_potential_outcomes(patient_samples, true_prob_tox, true_prob_eff)

Arguments

Value

a list of lists, with names tox and eff, each mapping to a matrix of the potential outcomes.

Examples

num_sims <- 10
ps <- lapply(1:num_sims, function(x) PatientSample$new())
# Set tox_u and eff_u for each simulation
set.seed(2024)
lapply(1:num_sims, function(x) {
  tox_u_new <- runif(n = 20)
  eff_u_new <- runif(n = 20)
  ps[[x]]$set_eff_and_tox(tox_u = tox_u_new, eff_u = eff_u_new)
})
true_prob_tox <- c(0.05, 0.10, 0.15, 0.18, 0.45)
true_prob_eff <- c(0.40, 0.50, 0.52, 0.53, 0.53)
get_potential_outcomes(
  patient_samples = ps,
  true_prob_tox = true_prob_tox,
  true_prob_eff = true_prob_eff
)

Description

Get an object to fit a dose-selector that randomly selects doses. Whilst this design is unlikely to pass the ethical hurdles when investigating truly experimental treatments, this class is useful for illustrating methods and can be useful for benchmarking.

Usage

get_random_selector(
  parent_selector_factory = NULL,
  prob_select,
  supports_efficacy = FALSE,
  ...
)

Arguments

Value

an object of type selector_factory.

Examples

prob_select = c(0.1, 0.3, 0.5, 0.07, 0.03)
model <- get_random_selector(prob_select = prob_select)
fit <- model %>% fit('1NTN')
fit %>% recommended_dose() # This is random
# We could also precede this selector with a set path:
model <- follow_path('1NN 2NN 3NN') %>%
  get_random_selector(prob_select = prob_select)
fit <- model %>% fit('1NN')
fit %>% recommended_dose() # This is not-random; it comes from the path.
fit <- model %>% fit('1NN 2NT')
fit %>% recommended_dose() # This is random; the path is discarded.

Description

Get an object to fit the 3+3 model.

Usage

get_three_plus_three(num_doses, allow_deescalate = FALSE, ...)

Arguments

Value

an object of type selector_factory that can fit the 3+3 model to outcomes.

References

Storer BE. Design and Analysis of Phase I Clinical Trials. Biometrics. 1989;45(3):925-937. doi:10.2307/2531693

Korn EL, Midthune D, Chen TT, Rubinstein LV, Christian MC, Simon RM. A comparison of two phase I trial designs. Statistics in Medicine. 1994;13(18):1799-1806. doi:10.1002/sim.4780131802

Examples

model <- get_three_plus_three(num_doses = 5)

fit1 <- model %>% fit('1NNN 2NTN')
fit1 %>% recommended_dose()
fit1 %>% continue()

fit2 <- model %>% fit('1NNN 2NTN 2NNT')
fit2 %>% recommended_dose()
fit2 %>% continue()

Description

The toxicity probability interval (TPI)is a dose-escalation design by Ji et al.

Usage

get_tpi(
  num_doses,
  target,
  k1,
  k2,
  exclusion_certainty,
  alpha = 0.005,
  beta = 0.005,
  ...
)

Arguments

Value

an object of type selector_factory that can fit the TPI model to outcomes.

Details

The design seeks a dose with probability of toxicity $p_{i}$ close to a target probability $p_{T}$ by iteratively calculating the interval

$p_{T} - K_{2} \sigma_{i} < p_{i} < p_{T} + K_{1} \sigma_{i}$

In this model, $K_{1}$ and $K_{2}$ are specified constants and $\sigma_{i}$ is the standard deviation of $p_{i}$ arising from a Bayesian beta-binomial conjugate model

$p_{i} | data \sim Beta(\alpha + x_{i}, \beta + n_{i} - x_{i}),$

where $x_{i}$ is the number of toxicities observed and $n_{i}$ is the number of patients treated at dose $i$, and $\alpha$ and $\beta$ are hyperparameters for the beta prior on $p_{i}$. A dose is excluded as inadmissible if

$P(p_{i} > p_{T} | data) > \xi$

The trial commences at a starting dose, possibly dose 1. If dose $i$ has just been evaluated in patient(s), dose selection decisions proceed by calculating the posterior probability that the true toxicity rate at dose $i$ belongs to the three partition regions $p_{i} < p_{T} - K_{2} \sigma_{i}$, $p_{T} - K_{2} \sigma_{i} < p_{i} < p_{T} + K_{1} \sigma_{i}$, and $p_{i} > p_{T} + K_{2} \sigma_{i}$, corresponding to decisions escalate, stay, and de-escalate dose, respectively. Further to this are rules that prevent escalation to an inadmissible dose. In their paper, the authors demonstrate acceptable operating performance using $\alpha = \beta = 0.005$, $K_{1} = 1$, $K_{2} = 1.5$ and $\xi = 0.95$. See the publications for full details.

References

Ji, Y., Li, Y., & Bekele, B. N. (2007). Dose-finding in phase I clinical trials based on toxicity probability intervals. Clinical Trials, 4(3), 235–244. https://doi.org/10.1177/1740774507079442

Ji, Y., & Yang, S. (2017). On the Interval-Based Dose-Finding Designs, 1–26. Retrieved from https://arxiv.org/abs/1706.03277

Examples

target <- 0.25
model1 <- get_tpi(num_doses = 5, target = target, k1 = 1, k2 = 1.5,
  exclusion_certainty = 0.95)

outcomes <- '1NNN 2NTN'
model1 %>% fit(outcomes) %>% recommended_dose()

Description

This function returns an object that can be used to fit a CRM model using methods provided by the trialr package.

Dose selectors are designed to be daisy-chained together to achieve different behaviours. This class is a **resumptive** selector, meaning it carries on when the previous dose selector, where present, has elected not to continue. For example, this allows instances of this class to be preceded by a selector that follows a fixed path in an initial escalation plan, such as that provided by follow_path. In this example, when the observed trial outcomes deviate from that initial plan, the selector following the fixed path elects not to continue and responsibility passes to this class. See Examples.

The time-to-event variant, TITE-CRM, is used when you specify tite = TRUE. This weights the observations to allow dose-selections based on partially observed outcomes.

Usage

get_trialr_crm(
  parent_selector_factory = NULL,
  skeleton,
  target,
  model,
  tite = FALSE,
  ...
)

Arguments

Value

an object of type selector_factory that can fit the CRM model to outcomes.

References

Kristian Brock (2020). trialr: Clinical Trial Designs in 'rstan'. R package version 0.1.5. https://github.com/brockk/trialr

Kristian Brock (2019). trialr: Bayesian Clinical Trial Designs in R and Stan. arXiv preprint arXiv:1907.00161.

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
# The model to use must be specified in trialr:
model1 <- get_trialr_crm(skeleton = skeleton, target = target,
                         model = 'empiric', beta_sd = 1.34)
# Refer to the trialr documentation for more details on model forms.
outcomes <- '1NNN 2NTN'
model1 %>% fit(outcomes) %>% recommended_dose()

# But we can provide extra args to trialr that are than passed onwards to
# the call to trialr::stan_crm to override the defaults.
# For example, if we want the one-parameter logistic model, we run:
model2 <- get_trialr_crm(skeleton = skeleton, target = target,
                         model = 'logistic', a0 = 3,
                         beta_mean = 0, beta_sd = 1)
model2 %>% fit(outcomes) %>% recommended_dose()
# And, if we want the two-parameter logistic model, we run:
model3 <- get_trialr_crm(skeleton = skeleton, target = target,
                         model = 'logistic2',
                         alpha_mean = 0, alpha_sd = 2,
                         beta_mean = 0, beta_sd = 1)
model3 %>% fit(outcomes) %>% recommended_dose()

# We can use an initial dose-escalation plan, a pre-specified path that
# should be followed until trial outcomes deviate, at which point the CRM
# model takes over. For instance, if we want to use two patients at each of
# the first three doses in the absence of toxicity, irrespective the model's
# advice, we would run:
model1 <- follow_path('1NN 2NN 3NN') %>%
  get_trialr_crm(skeleton = skeleton, target = target, model = 'empiric',
                 beta_sd = 1.34)

# If outcomes match the desired path, the path is followed further:
model1 %>% fit('1NN 2N') %>% recommended_dose()

# But when the outcomes diverge:
model1 %>% fit('1NN 2T') %>% recommended_dose()

# Or the pre-specified path comes to an end:
model1 %>% fit('1NN 2NN 3NN') %>% recommended_dose()
# ...the CRM model takes over.

Description

Get an object to fit the TITE-CRM model using the trialr package.

Usage

get_trialr_crm_tite(
  parent_selector_factory = NULL,
  skeleton,
  target,
  model,
  ...
)

Arguments

Details

This function is a short-cut to get_trialr_crm(tite = TRUE). See get_trialr_crm for full details.

Value

an object of type selector_factory that can fit the CRM model to outcomes.

Examples

# TODO

Description

This function returns an object that can be used to fit the EffTox model for phase I/II dose-finding using methods provided by the trialr package.

Usage

get_trialr_efftox(
  parent_selector_factory = NULL,
  real_doses,
  efficacy_hurdle,
  toxicity_hurdle,
  p_e,
  p_t,
  eff0,
  tox1,
  eff_star,
  tox_star,
  priors,
  ...
)

Arguments

Value

an object of type selector_factory that can fit the EffTox model to outcomes.

References

Thall, P., & Cook, J. (2004). Dose-Finding Based on Efficacy-Toxicity Trade-Offs. Biometrics, 60(3), 684-693. https://doi.org/10.1111/j.0006-341X.2004.00218.x

Thall, P., Herrick, R., Nguyen, H., Venier, J., & Norris, J. (2014). Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding. Clinical Trials, 11(6), 657-666. https://doi.org/10.1177/1740774514547397

Brock, K. (2020). trialr: Clinical Trial Designs in 'rstan'. R package version 0.1.5. https://github.com/brockk/trialr

Brock, K. (2019). trialr: Bayesian Clinical Trial Designs in R and Stan. arXiv preprint arXiv:1907.00161.

Examples

efftox_priors <- trialr::efftox_priors
p <- efftox_priors(alpha_mean = -7.9593, alpha_sd = 3.5487,
                   beta_mean = 1.5482, beta_sd = 3.5018,
                   gamma_mean = 0.7367, gamma_sd = 2.5423,
                   zeta_mean = 3.4181, zeta_sd = 2.4406,
                   eta_mean = 0, eta_sd = 0.2,
                   psi_mean = 0, psi_sd = 1)
real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0)
model <- get_trialr_efftox(real_doses = real_doses,
                           efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                           p_e = 0.1, p_t = 0.1,
                           eff0 = 0.5, tox1 = 0.65,
                           eff_star = 0.7, tox_star = 0.25,
                           priors = p, iter = 1000, chains = 1, seed = 2020)

Description

This function returns an object that can be used to fit a Neuenschwander, Branson and Gsponer (NBG) model for dose-finding using methods provided by the trialr package.

Usage

get_trialr_nbg(
  parent_selector_factory = NULL,
  real_doses,
  d_star,
  target,
  alpha_mean,
  alpha_sd,
  beta_mean,
  beta_sd,
  tite = FALSE,
  ...
)

Arguments

Details

The model form implemented in trialr is:

$F(x_{i}, \alpha, \beta) = 1 / (1 + \exp{-(\alpha + \exp{(\beta)} log(x_i / d_*))})$

with normal priors on alpha and beta.

Dose selectors are designed to be daisy-chained together to achieve different behaviours. This class is a **resumptive** selector, meaning it carries on when the previous dose selector, where present, has elected not to continue. For example, this allows instances of this class to be preceded by a selector that follows a fixed path in an initial escalation plan, such as that provided by follow_path. In this example, when the observed trial outcomes deviate from that initial plan, the selector following the fixed path elects not to continue and responsibility passes to this class. See examples under get_dfcrm.

A time-to-event variant, like TITE-CRM, is used when you specify tite = TRUE. This weights the observations to allow dose-selections based on partially observed outcomes.

Value

an object of type selector_factory that can fit the NBG model to outcomes.

References

Neuenschwander, B., Branson, M., & Gsponer, T. (2008). Critical aspects of the Bayesian approach to phase I cancer trials. Statistics in Medicine, 27, 2420–2439. https://doi.org/10.1002/sim.3230

Brock, K. (2020). trialr: Clinical Trial Designs in 'rstan'. R package version 0.1.5. https://github.com/brockk/trialr

Brock, K. (2019). trialr: Bayesian Clinical Trial Designs in R and Stan. arXiv preprint arXiv:1907.00161.

Examples

real_doses <- c(5, 10, 25, 40, 60)
d_star <- 60
target <- 0.25

model <- get_trialr_nbg(real_doses = real_doses, d_star = d_star,
                        target = target,
                        alpha_mean = 2, alpha_sd = 1,
                        beta_mean = 0.5, beta_sd = 1)
# Refer to the trialr documentation for more details on model & priors.
outcomes <- '1NNN 2NTN'
fit <- model %>% fit(outcomes)
fit %>% recommended_dose()
fit %>% mean_prob_tox()

Description

Get an object to fit a TITE version of the NBG dose-finding model using trialr

Usage

get_trialr_nbg_tite(
  parent_selector_factory = NULL,
  real_doses,
  d_star,
  target,
  alpha_mean,
  alpha_sd,
  beta_mean,
  beta_sd,
  ...
)

Arguments

Value

an object of type selector_factory that can fit the NBG model to outcomes.

Examples

# TODO

Description

This function returns an object that can be used to fit Wages & Taits model for phase I/II dose-finding, i.e. it selects doses according to efficacy and toxicity outcomes. This function delegates prior-to-posterior calculations to the dfcrm package.

Usage

get_wages_and_tait(
  parent_selector_factory = NULL,
  tox_skeleton,
  eff_skeletons,
  eff_skeleton_weights = rep(1, nrow(eff_skeletons)),
  tox_limit,
  eff_limit,
  num_randomise,
  ...
)

Arguments

Value

an object of type selector_factory.

References

Wages, N. A., & Tait, C. (2015). Seamless Phase I/II Adaptive Design for Oncology Trials of Molecularly Targeted Agents. Journal of Biopharmaceutical Statistics, 25(5), 903–920. https://doi.org/10.1080/10543406.2014.920873

Examples

# Example in Wages & Tait (2015)
tox_skeleton = c(0.01, 0.08, 0.15, 0.22, 0.29, 0.36)
eff_skeletons = matrix(nrow=11, ncol=6)
eff_skeletons[1,] <- c(0.60, 0.50, 0.40, 0.30, 0.20, 0.10)
eff_skeletons[2,] <- c(0.50, 0.60, 0.50, 0.40, 0.30, 0.20)
eff_skeletons[3,] <- c(0.40, 0.50, 0.60, 0.50, 0.40, 0.30)
eff_skeletons[4,] <- c(0.30, 0.40, 0.50, 0.60, 0.50, 0.40)
eff_skeletons[5,] <- c(0.20, 0.30, 0.40, 0.50, 0.60, 0.50)
eff_skeletons[6,] <- c(0.10, 0.20, 0.30, 0.40, 0.50, 0.60)
eff_skeletons[7,] <- c(0.20, 0.30, 0.40, 0.50, 0.60, 0.60)
eff_skeletons[8,] <- c(0.30, 0.40, 0.50, 0.60, 0.60, 0.60)
eff_skeletons[9,] <- c(0.40, 0.50, 0.60, 0.60, 0.60, 0.60)
eff_skeletons[10,] <- c(0.50, 0.60, 0.60, 0.60, 0.60, 0.60)
eff_skeletons[11,] <- c(rep(0.60, 6))
eff_skeleton_weights = rep(1, nrow(eff_skeletons))
tox_limit = 0.33
eff_limit = 0.05
model <- get_wages_and_tait(tox_skeleton = tox_skeleton,
                            eff_skeletons = eff_skeletons,
                            tox_limit = tox_limit, eff_limit = eff_limit,
                            num_randomise = 20)
fit <- model %>% fit('1NN 2EN 3BE')
fit %>% recommended_dose()
fit %>% is_randomising()
fit %>% dose_admissible()
fit %>% prob_administer()

Description

Visualise dose-paths as a graph

Usage

graph_paths(paths, viridis_palette = "viridis", RColorBrewer_palette = NULL)

Arguments

Details

The viridis package supports palettes: viridis, magma, plasma, inferno, and cividis. The RColorBrewer package supports many palettes. Refer to those packages on CRAN for more details.

Examples

paths <- get_three_plus_three(num_doses = 5) %>%
  get_dose_paths(cohort_sizes = c(3, 3, 3))
## Not run: 
graph_paths(paths)
graph_paths(paths, viridis_palette = 'plasma')
graph_paths(paths, RColorBrewer_palette = 'YlOrRd')

## End(Not run)

Description

Get the percentage of patients evaluated at each dose under investigation.

Usage

is_randomising(x, ...)

Arguments

Value

a logical value

Examples

outcomes <- '1NNN 2NTN'
fit <- get_random_selector(prob_select = c(0.1, 0.6, 0.3)) %>%
  fit(outcomes)
fit %>% is_randomising()

Description

Weights for tolerance and toxicity events using linear function of time

Usage

linear_follow_up_weight(
  now_time,
  recruited_time,
  tox,
  max_time,
  tox_has_weight_1 = TRUE
)

Arguments

Value

numerical vector of weights

Examples

linear_follow_up_weight(
  now_time = 10,
  recruited_time = 4:7,
  tox = c(0, 0, 0, 1),
  max_time = 6,
  tox_has_weight_1 = TRUE
)

linear_follow_up_weight(
  now_time = 10,
  recruited_time = 4:7,
  tox = c(0, 0, 0, 1),
  max_time = 6,
  tox_has_weight_1 = FALSE
)

Description

Get the estimated mean efficacy rate at each dose under investigation. This is a set of modelled statistics. The underlying models estimate efficacy probabilities in different ways. If no model-based estimate of the mean is available, this function will return a vector of NAs.

Usage

mean_prob_eff(x, ...)

Arguments

Value

a numerical vector

Examples

efftox_priors <- trialr::efftox_priors
p <- efftox_priors(alpha_mean = -7.9593, alpha_sd = 3.5487,
                   beta_mean = 1.5482, beta_sd = 3.5018,
                   gamma_mean = 0.7367, gamma_sd = 2.5423,
                   zeta_mean = 3.4181, zeta_sd = 2.4406,
                   eta_mean = 0, eta_sd = 0.2,
                   psi_mean = 0, psi_sd = 1)
real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0)
model <- get_trialr_efftox(real_doses = real_doses,
                           efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                           p_e = 0.1, p_t = 0.1,
                           eff0 = 0.5, tox1 = 0.65,
                           eff_star = 0.7, tox_star = 0.25,
                           priors = p, iter = 1000, chains = 1, seed = 2020)
x <- model %>% fit('1N 2E 3B')
mean_prob_eff(x)

Description

Get the estimated mean toxicity rate at each dose under investigation. This is a set of modelled statistics. The underlying models estimate toxicity probabilities in different ways. If no model-based estimate of the mean is available, this function will return a vector of NAs.

Usage

mean_prob_tox(x, ...)

Arguments

Value

a numerical vector

Examples

# CRM example
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
outcomes <- '1NNN 2NTN'
fit <- get_dfcrm(skeleton = skeleton, target = target) %>% fit(outcomes)
fit %>% mean_prob_tox()

Description

Get the estimated median efficacy rate at each dose under investigation. This is a set of modelled statistics. The underlying models estimate efficacy probabilities in different ways. If no model-based estimate of the median is available, this function will return a vector of NAs.

Usage

median_prob_eff(x, ...)

Arguments

Value

a numerical vector

Examples

efftox_priors <- trialr::efftox_priors
p <- efftox_priors(alpha_mean = -7.9593, alpha_sd = 3.5487,
                   beta_mean = 1.5482, beta_sd = 3.5018,
                   gamma_mean = 0.7367, gamma_sd = 2.5423,
                   zeta_mean = 3.4181, zeta_sd = 2.4406,
                   eta_mean = 0, eta_sd = 0.2,
                   psi_mean = 0, psi_sd = 1)
real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0)
model <- get_trialr_efftox(real_doses = real_doses,
                           efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                           p_e = 0.1, p_t = 0.1,
                           eff0 = 0.5, tox1 = 0.65,
                           eff_star = 0.7, tox_star = 0.25,
                           priors = p, iter = 1000, chains = 1, seed = 2020)
x <- model %>% fit('1N 2E 3B')
median_prob_eff(x)

Description

Get the estimated median toxicity rate at each dose under investigation. This is a set of modelled statistics. The underlying models estimate toxicity probabilities in different ways. If no model-based estimate of the median is available, this function will return a vector of NAs.

Usage

median_prob_tox(x, ...)

Arguments

Value

a numerical vector

Examples

# CRM example
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
outcomes <- '1NNN 2NTN'
fit <- get_dfcrm(skeleton = skeleton, target = target) %>% fit(outcomes)
fit %>% median_prob_tox()

Description

Get the model data-frame for a dose-finding analysis, inlcuding columns for patient id, cohort id, dose administered, and toxicity outcome. In some scenarios, further columns are provided.

Usage

model_frame(x, ...)

Arguments

Value

tibble, which acts like a data.frame.

Examples

# In a toxicity-only setting:
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model <- get_dfcrm(skeleton = skeleton, target = target)
fit <- model %>% fit('1NNN 2NTN')
fit %>% model_frame()

# In an efficacy-toxicity setting
prob_select = c(0.1, 0.3, 0.5, 0.07, 0.03)
model <- get_random_selector(prob_select = prob_select,
                             supports_efficacy = TRUE)
x <- model %>% fit('1NTN 2EN 5BB', supports_efficacy = TRUE)
fit %>% model_frame()

Description

Get the number of patients evaluated at each dose under investigation.

Usage

n_at_dose(x, ...)

Arguments

Value

an integer vector

Examples

# CRM example
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
outcomes <- '1NNN 2NTN'
fit <- get_dfcrm(skeleton = skeleton, target = target) %>% fit(outcomes)
fit %>% n_at_dose()

Description

Get the number of patients evaluated at the recommended dose.

Usage

n_at_recommended_dose(x, ...)

Arguments

Value

an integer

Examples

# CRM example
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
outcomes <- '1NNN 2NTN'
fit <- get_dfcrm(skeleton = skeleton, target = target) %>% fit(outcomes)
fit %>% n_at_recommended_dose()

Description

Number of different possible outcomes for a cohort of patients, each of which will experience one of a number of discrete outcomes. For instance, in a typical phase I dose-finding trial, each patient will experience: no-toxicity (N); or toxicity (T). The number of possible outcomes per patient is two. For a cohort of three patients, the number of cohort outcomes is four: NNN, NNT, NTT, TTT. Consider a more complex example: in a seamless phase I/II trial with efficacy and toxicity outcomes, an individual patient will experience one of four distinct outcomes: efficacy only (E); toxicity only (T); both efficacy and toxicity (B) or neither. How many different outcomes are there for a cohort of three patients? The answer is 20 but it is non-trivial to see why. This convenience function calculates that number using the formula for the number of combinations with replacement,

Usage

num_cohort_outcomes(num_patient_outcomes, cohort_size)

Arguments

Value

integer, number of distinct possible cohort outcomes

Examples

# As described in example, N or T in a cohort of three:
num_cohort_outcomes(num_patient_outcomes = 2, cohort_size = 3)
# Also described in example, E, T, B or N in a cohort of three:
num_cohort_outcomes(num_patient_outcomes = 4, cohort_size = 3)

Description

Number of possible nodes in an exhaustive analysis of dose-paths in a dose-finding trial. The number of nodes at depth i is the the number of nodes at depth i-1 multiplied by the number of possible cohort outcomes at depth i. For instance, if there were 16 nodes at the previous depth and four possible cohort outcomes at the current depth, then there are 64 possible nodes at the current depth. Knowing the number of nodes in a dose-paths analysis helps the analyst decide whether simulation or dose-paths are a better tool for assessing operating characteristics of a dose-finding design.

Usage

num_dose_path_nodes(num_patient_outcomes, cohort_sizes)

Arguments

Value

integer vector, number of nodes at increasing depths. The total number of nodes is the sum of this vector.

Examples

# In a 3+3 design, there are two possible outcomes for each patient and
# patients are evaluated in cohorts of three. In an analysis of dose-paths in
# the first two cohorts of three, how many nodes are there?
num_dose_path_nodes(num_patient_outcomes = 2, cohort_sizes = rep(3, 2))
# In contrast, using an EffTox design there are four possible outcomes for
# each patient. In a similar analysis of dose-paths in the first two cohorts
# of three, how many nodes are there now?
num_dose_path_nodes(num_patient_outcomes = 4, cohort_sizes = rep(3, 2))

Description

Get the number of doses under investigation in a dose-finding trial.

Usage

num_doses(x, ...)

Arguments

Value

integer

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model <- get_dfcrm(skeleton = skeleton, target = target)
fit <- model %>% fit('1NNN 2NTN')
fit %>% num_doses()

Description

Get the number of efficacies seen in a dose-finding trial.

Usage

num_eff(x, ...)

Arguments

Value

integer

Examples

prob_select = c(0.1, 0.3, 0.5, 0.07, 0.03)
model <- get_random_selector(prob_select = prob_select,
                             supports_efficacy = TRUE)
x <- model %>% fit('1NTN 2EN 5BB')
num_eff(x)

Description

Get the number of patients evaluated in a dose-finding trial.

Usage

num_patients(x, ...)

Arguments

Value

integer

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model <- get_dfcrm(skeleton = skeleton, target = target)
fit <- model %>% fit('1NNN 2NTN')
fit %>% num_patients()

Description

Get the number of toxicities seen in a dose-finding trial.

Usage

num_tox(x, ...)

Arguments

Value

integer

Examples

skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model <- get_dfcrm(skeleton = skeleton, target = target)
fit <- model %>% fit('1NNN 2NTN')
fit %>% num_tox()

Description

Parse a string of phase I/II dose-finding outcomes to a binary vector notation necessary for model invocation.

The outcome string describes the doses given, outcomes observed and groups patients into cohorts. The format of the string is described in Brock et al. (2017). See Examples.

The letters E, T, N and B are used to represents patients that experienced (E)fficacy only, (T)oxicity only, (B)oth efficacy and toxicity, and (N)either. These letters are concatenated after numerical dose-levels to convey the outcomes of cohorts of patients. For instance, 2ETB represents a cohort of three patients that were treated at dose-level 2, and experienced efficacy, toxicity and both events, respectively. The results of cohorts are separated by spaces. Thus, 2ETB 1NN extends our previous example, where the next cohort of two were treated at dose-level 1 and both patients experienced neither efficacy nor toxicity. See Examples.

Usage

parse_phase1_2_outcomes(outcomes, as_list = TRUE)

Arguments

Value

If as_list == TRUE, a list with elements eff, tox, dose and num_patients. If as_list == FALSE, a data.frame with columns eff, tox and dose.

References

Brock, K., Billingham, L., Copland, M., Siddique, S., Sirovica, M., & Yap, C. (2017). Implementing the EffTox dose-finding design in the Matchpoint trial. BMC Medical Research Methodology, 17(1), 112. https://doi.org/10.1186/s12874-017-0381-x

Examples

x = parse_phase1_2_outcomes('1NNE 2EEN 3TBB')
# Three cohorts of three patients. The first cohort was treated at dose 1 and
# had no toxicity with one efficacy, etc.
x$num_patients  # 9
x$dose         # c(1, 1, 1, 2, 2, 2, 3, 3, 3)
x$eff           # c(0, 0, 1, 1, 1, 0, 0, 1, 1)
sum(x$eff)      # 5
x$tox           # c(0, 0, 0, 0, 0, 0, 1, 1, 1)
sum(x$tox)      # 3

# The same information can be parsed to a data-frame:
y = parse_phase1_2_outcomes('1NNE 2EEN 3TBB', as_list = FALSE)
y

Description

Parse a string of phase I dose-finding outcomes to a binary vector notation necessary for model invocation.

The outcome string describes the doses given, outcomes observed and groups patients into cohorts. The format of the string is described in Brock (2019), and that itself is the phase I analogue of the similar idea described in Brock et al. (2017). See Examples.

The letters T and N are used to represents patients that experienced (T)oxicity and (N)o toxicity. These letters are concatenated after numerical dose-levels to convey the outcomes of cohorts of patients. For instance, 2NNT represents a cohort of three patients that were treated at dose-level 2, one of whom experienced toxicity, and two that did not. The results of cohorts are separated by spaces. Thus, 2NNT 1NN extends our previous example, where the next cohort of two were treated at dose-level 1 and neither experienced toxicity. See examples.

Usage

parse_phase1_outcomes(outcomes, as_list = TRUE)

Arguments

Value

If as_list == TRUE, a list with elements tox, doses and num_patients. If as_list == FALSE, a data.frame with columns tox and doses.

References

Brock, K. (2019). trialr: Bayesian Clinical Trial Designs in R and Stan. arXiv:1907.00161 [stat.CO]

Brock, K., Billingham, L., Copland, M., Siddique, S., Sirovica, M., & Yap, C. (2017). Implementing the EffTox dose-finding design in the Matchpoint trial. BMC Medical Research Methodology, 17(1), 112. https://doi.org/10.1186/s12874-017-0381-x

Examples

x = parse_phase1_outcomes('1NNN 2NTN 3TTT')
# Three cohorts of three patients. The first cohort was treated at dose 1 and
# none had toxicity. The second cohort was treated at dose 2 and one of the
# three had toxicity. Finally, cohort three was treated at dose 3 and all
# patients had toxicity.
x$num_patients  # 9
x$doses         # c(1, 1, 1, 2, 2, 2, 3, 3, 3)
x$tox           # c(0, 0, 0, 0, 1, 0, 1, 1, 1)
sum(x$tox)      # 4

# The same information can be parsed to a data-frame:
y = parse_phase1_outcomes('1NNN 2NTN 3TTT', as_list = FALSE)
y

Description

Class to house the latent random variables that govern toxicity and efficacy events in patients. Instances of this class can be used in simulation-like tasks to effectively use the same simulated individuals in different designs, thus supporting reduced Monte Carlo error and more efficient comparison.

Public fields

Methods

Public methods

• PatientSample$new()

• PatientSample$set_eff_and_tox()

• PatientSample$expand_to()

• PatientSample$get_tox_u()

• PatientSample$get_patient_tox()

• PatientSample$get_eff_u()

• PatientSample$get_patient_eff()

• PatientSample$clone()

Method new()

Creator.

Usage

PatientSample$new(
  num_patients = 0,
  time_to_tox_func = function() runif(n = 1),
  time_to_eff_func = function() runif(n = 1)
)

Arguments

Returns

[PatientSample].

Method set_eff_and_tox()

Set the toxicity and efficacy latent variables that govern occurrence of toxicity and efficacy events. By default, instances of this class automatically grow these latent variables to accommodate arbitrarily high sample sizes. However, when you set these latent variables manually via this function, you override the ability of the class to self-manage, so its ability to grow is turned off by setting the internal variable self$can_grow <- FALSE.

Usage

PatientSample$set_eff_and_tox(
  tox_u,
  eff_u,
  tox_time = rep(0, length(tox_u)),
  eff_time = rep(0, length(eff_u))
)

Arguments

Method expand_to()

Expand sample to size at least num_patients

Usage

PatientSample$expand_to(num_patients)

Arguments

Method get_tox_u()

Get toxicity latent variable for patient i

Usage

PatientSample$get_tox_u(i)

Arguments

Method get_patient_tox()

Get 0 or 1 event marker for whether toxicity occurred in patient i

Usage

PatientSample$get_patient_tox(i, prob_tox, time = Inf)

Arguments

Method get_eff_u()

Get efficacy latent variable for patient i

Usage

PatientSample$get_eff_u(i)

Arguments

Method get_patient_eff()

Get 0 or 1 event marker for whether efficacy occurred in patient i

Usage

PatientSample$get_patient_eff(i, prob_eff, time = Inf)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

PatientSample$clone(deep = FALSE)

Arguments

References

Sweeting, M., Slade, D., Jackson, D., & Brock, K. (2024). Potential outcome simulation for efficient head-to-head comparison of adaptive dose-finding designs. arXiv preprint arXiv:2402.15460

Description

Break a phase I/II outcome string into a list of cohort parts.

Break a phase I/II outcome string into a list of cohort parts.

The outcome string describes the doses given, outcomes observed and the timing of analyses that recommend a dose. The format of the string is described in Brock _et al_. (2017).

The letters E, T, N & B are used to represents patients that experienced (E)fficacy, (T)oxicity, (N)either and (B)oth. These letters are concatenated after numerical dose-levels to convey the outcomes of cohorts of patients. For instance, 2NET represents a cohort of three patients that were treated at dose-level 2, one of whom experienced toxicity only, one that experienced efficacy only, and one that had neither. The results of cohorts are separated by spaces and it is assumed that a dose-finding decision takes place at the end of a cohort. Thus, 2NET 1NN builds on our previous example, where the next cohort of two were treated at dose-level 1 and neither of these patients experienced either event See examples.

Usage

phase1_2_outcomes_to_cohorts(outcomes)

Arguments

Value

a list with a slot for each cohort. Each cohort slot is itself a list, containing elements: * dose, the integer dose delivered to the cohort; * outcomes, a character string representing the E, T N or B outcomes for the patients in this cohort.

References

Brock, K., Billingham, L., Copland, M., Siddique, S., Sirovica, M., & Yap, C. (2017). Implementing the EffTox dose-finding design in the Matchpoint trial. BMC Medical Research Methodology, 17(1), 112. https://doi.org/10.1186/s12874-017-0381-x

Examples

x = phase1_2_outcomes_to_cohorts('1NEN 2ENT 3TB')
length(x)
x[[1]]$dose
x[[1]]$outcomes
x[[2]]$dose
x[[2]]$outcomes
x[[3]]$dose
x[[3]]$outcomes

Description

Break a phase I outcome string into a list of cohort parts.

Break a phase I outcome string into a list of cohort parts.

The outcome string describes the doses given, outcomes observed and the timing of analyses that recommend a dose. The format of the string is described in Brock (2019), and that itself is the phase I analogue of the similar idea described in Brock _et al_. (2017).

The letters T and N are used to represents patients that experienced (T)oxicity and (N)o toxicity. These letters are concatenated after numerical dose-levels to convey the outcomes of cohorts of patients. For instance, 2NNT represents a cohort of three patients that were treated at dose-level 2, one of whom experienced toxicity, and two that did not. The results of cohorts are separated by spaces and it is assumed that a dose-finding decision takes place at the end of a cohort. Thus, 2NNT 1NN builds on our previous example, where the next cohort of two were treated at dose-level 1 and neither of these patients experienced toxicity. See examples.

Usage

phase1_outcomes_to_cohorts(outcomes)

Arguments

Value

a list with a slot for each cohort. Each cohort slot is itself a list, containing elements: * dose, the integer dose delivered to the cohort; * outcomes, a character string representing the T or N outcomes for the patients in this cohort.

References

Brock, K. (2019). trialr: Bayesian Clinical Trial Designs in R and Stan. arXiv:1907.00161 [stat.CO]

Brock, K., Billingham, L., Copland, M., Siddique, S., Sirovica, M., & Yap, C. (2017). Implementing the EffTox dose-finding design in the Matchpoint trial. BMC Medical Research Methodology, 17(1), 112. https://doi.org/10.1186/s12874-017-0381-x

Examples

x = phase1_outcomes_to_cohorts('1NNN 2NNT 3TT')
length(x)
x[[1]]$dose
x[[1]]$outcomes
x[[2]]$dose
x[[2]]$outcomes
x[[3]]$dose
x[[3]]$outcomes

Description

Get the percentage of patients evaluated at each dose under investigation.

Usage

prob_administer(x, ...)

Arguments

Value

a numerical vector

Examples

# CRM example
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
outcomes <- '1NNN 2NTN'
fit <- get_dfcrm(skeleton = skeleton, target = target) %>% fit(outcomes)
fit %>% prob_administer()

Description

Get the estimated quantile of the efficacy rate at each dose under investigation. This is a set of modelled statistics. The underlying models estimate efficacy probabilities in different ways. If no model-based estimate of the median is available, this function will return a vector of NAs.

Usage

prob_eff_quantile(x, p, ...)

Arguments

Value

a numerical vector

Examples

efftox_priors <- trialr::efftox_priors
p <- efftox_priors(alpha_mean = -7.9593, alpha_sd = 3.5487,
                   beta_mean = 1.5482, beta_sd = 3.5018,
                   gamma_mean = 0.7367, gamma_sd = 2.5423,
                   zeta_mean = 3.4181, zeta_sd = 2.4406,
                   eta_mean = 0, eta_sd = 0.2,
                   psi_mean = 0, psi_sd = 1)
real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0)
model <- get_trialr_efftox(real_doses = real_doses,
                           efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                           p_e = 0.1, p_t = 0.1,
                           eff0 = 0.5, tox1 = 0.65,
                           eff_star = 0.7, tox_star = 0.25,
                           priors = p, iter = 1000, chains = 1, seed = 2020)
x <- model %>% fit('1N 2E 3B')
prob_tox_quantile(x, p = 0.9)

Description

Get the probabilities that each of the doses under investigation is recommended.

Usage

prob_recommend(x, ...)

Arguments

Value

vector of probabilities

Examples

true_prob_tox <- c(0.12, 0.27, 0.44, 0.53, 0.57)
sims <- get_three_plus_three(num_doses = 5) %>%
  simulate_trials(num_sims = 50, true_prob_tox = true_prob_tox)
sims %>% prob_recommend

Description

Get the probability that the toxicity rate at each dose exceeds some threshold.

Get the probability that the efficacy rate at each dose exceeds some threshold.

Usage

prob_tox_exceeds(x, threshold, ...)

prob_eff_exceeds(x, threshold, ...)

Arguments

Value

numerical vector of probabilities

numerical vector of probabilities

Examples

# CRM example
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
outcomes <- '1NNN 2NTN'
fit <- get_dfcrm(skeleton = skeleton, target = target) %>% fit(outcomes)
# What is probability that tox rate at each dose exceeds target by >= 10%?
fit %>% prob_tox_exceeds(threshold = target + 0.1)
efftox_priors <- trialr::efftox_priors
p <- efftox_priors(alpha_mean = -7.9593, alpha_sd = 3.5487,
                   beta_mean = 1.5482, beta_sd = 3.5018,
                   gamma_mean = 0.7367, gamma_sd = 2.5423,
                   zeta_mean = 3.4181, zeta_sd = 2.4406,
                   eta_mean = 0, eta_sd = 0.2,
                   psi_mean = 0, psi_sd = 1)
real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0)
model <- get_trialr_efftox(real_doses = real_doses,
                           efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                           p_e = 0.1, p_t = 0.1,
                           eff0 = 0.5, tox1 = 0.65,
                           eff_star = 0.7, tox_star = 0.25,
                           priors = p, iter = 1000, chains = 1, seed = 2020)
x <- model %>% fit('1N 2E 3B')
prob_tox_exceeds(x, threshold = 0.45)

Description

Get the estimated quantile of the toxicity rate at each dose under investigation. This is a set of modelled statistics. The underlying models estimate toxicity probabilities in different ways. If no model-based estimate of the median is available, this function will return a vector of NAs.

Usage

prob_tox_quantile(x, p, ...)

Arguments