R/dfcrm_selector.R

In escalation: Modular Approach to Dose Finding Clinical Trials

#' Get an object to fit the CRM model using the dfcrm package.
#'
#' @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 \code{\link{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.
#'
#' @param parent_selector_factory optional object of type
#' \code{\link{selector_factory}} that is in charge of dose selection before
#' this class gets involved. Leave as NULL to just use CRM from the start.
#' @param skeleton Dose-toxicity skeleton, a non-decreasing vector of
#' probabilities.
#' @param target We seek a dose with this probability of toxicity.
#' @param ... Extra args are passed to \code{\link[dfcrm]{crm}}.
#'
#' @return an object of type \code{\link{selector_factory}} that can fit the
#' CRM model to outcomes.
#'
#' @export
#'
#' @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.
#'
#' @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
get_dfcrm <- function(parent_selector_factory = NULL, skeleton, target, ...) {

  x <- list(
    parent_selector_factory = parent_selector_factory,
    skeleton = skeleton,
    target = target,
    extra_args = list(...)
  )

  class(x) <- c('dfcrm_selector_factory',
                'tox_selector_factory',
                'selector_factory')
  return(x)
}

#' @importFrom dfcrm crm
dfcrm_selector <- function(parent_selector = NULL, outcomes, skeleton, target,
                           ...) {

  if(is.character(outcomes)) {
    df <- parse_phase1_outcomes(outcomes, as_list = FALSE)
  } else if(is.data.frame(outcomes)) {
    df <- spruce_outcomes_df(outcomes)
  } else {
    stop('outcomes should be a character string or a data-frame.')
  }

  if(nrow(df) > 0) {
    # Checks
    if(max(df$dose) > length(skeleton)) {
      stop('dfcrm_selector - maximum dose given exceeds number of doses.')
    }
    x <- crm(prior = skeleton,
             target = target,
             tox = df$tox %>% as.integer(),
             level = df$dose,
             var.est = TRUE,
             ...)
  } else {
    x <- list(
      level = integer(length = 0),
      tox = integer(length = 0),
      mtd = 1,
      ptox = skeleton)
  }

  l <- list(
    parent = parent_selector,
    cohort = df$cohort,
    outcomes = outcomes,
    skeleton = skeleton,
    target = target,
    dfcrm_fit = x
  )

  class(l) = c('dfcrm_selector', 'tox_selector', 'selector')
  l
}

# Factory interface

#' @importFrom magrittr %>%
#' @export
fit.dfcrm_selector_factory <- function(selector_factory, outcomes, ...) {

  if(is.null(selector_factory$parent)) {
    parent <- NULL
  } else {
    parent <- selector_factory$parent %>% fit(outcomes, ...)
  }

  args <- list(
    parent = parent,
    outcomes = outcomes,
    skeleton = selector_factory$skeleton,
    target = selector_factory$target
  )
  args <- append(args, selector_factory$extra_args)
  do.call(dfcrm_selector, args = args)
}

# Selector interface

#' @export
tox_target.dfcrm_selector <- function(x, ...) {
  return(x$target)
}

#' @export
num_patients.dfcrm_selector <- function(x, ...) {
  return(length(x$dfcrm_fit$level))
}

#' @export
cohort.dfcrm_selector <- function(x, ...) {
  return(x$cohort)
}

#' @export
doses_given.dfcrm_selector <- function(x, ...) {
  return(x$dfcrm_fit$level)
}

#' @export
tox.dfcrm_selector <- function(x, ...) {
  return(x$dfcrm_fit$tox)
}

#' @export
num_doses.dfcrm_selector <- function(x, ...) {
  return(length(x$skeleton))
}

#' @export
recommended_dose.dfcrm_selector <- function(x, ...) {
  if(!is.null(x$parent)) {
    parent_dose <- recommended_dose(x$parent)
    parent_cont <- continue(x$parent)
    if(parent_cont & !is.na(parent_dose)) {
      return(parent_dose)
    }
  }

  # By default:
  return(as.integer(x$dfcrm_fit$mtd))
}

#' @export
continue.dfcrm_selector <- function(x, ...) {
  # dfcrm offers no methods for stopping but those are provided by other
  # classes in this package.
  # In the daisychain of selectors, this class is resumptive, meaning it will
  # continue with dose-selection after its optional parent, where present, has
  # opted to not continue.
  # Thus, this class always opts to continue:
  return(TRUE)
}

#' @export
mean_prob_tox.dfcrm_selector <- function(x, ...) {
  return(x$dfcrm_fit$ptox)
}

#' @export
median_prob_tox.dfcrm_selector <- function(x, ...) {
  return(prob_tox_quantile(x, p = 0.5))
}

#' @export
#' @importFrom gtools inv.logit
#' @importFrom stats qnorm
prob_tox_quantile.dfcrm_selector <- function(x, p, ...) {
  if(num_patients(x) <= 0) {
    return(as.numeric(rep(NA, num_doses(x))))
  } else {
    beta_hat <- x$dfcrm_fit$estimate
    beta_var <- x$dfcrm_fit$post.var
    # High values for beta lead to low values of prob_tox, so flip the tails:
    beta_q <- qnorm(p = 1 - p, mean = beta_hat, sd = sqrt(beta_var))
    if(x$dfcrm_fit$model == 'empiric') {
      return(x$skeleton ^ exp(beta_q))
    } else if(x$dfcrm_fit$model == 'logistic') {
      alpha <- x$dfcrm_fit$intcpt
      dose_scaled <- x$dfcrm_fit$dosescaled
      inv.logit(alpha + exp(beta_hat) * dose_scaled)
    } else {
      stop(paste0("Don't know what to do with dfcrm model '",
                  x$dfcrm_fit$model, "'"))
    }
  }
}

#' @export
#' @importFrom gtools logit
#' @importFrom stats pnorm
prob_tox_exceeds.dfcrm_selector <- function(x, threshold, ...) {

  if(num_patients(x) <= 0) {
    return(as.numeric(rep(NA, num_doses(x))))
  } else {
    beta_hat <- x$dfcrm_fit$estimate
    beta_var <- x$dfcrm_fit$post.var
    if(x$dfcrm_fit$model == 'empiric') {
      return(pnorm(q = log(log(threshold) / log(x$skeleton)),
              mean = beta_hat, sd = sqrt(beta_var)))
    } else if(x$dfcrm_fit$model == 'logistic') {
      alpha <- x$dfcrm_fit$intcpt
      dose_scaled <- x$dfcrm_fit$dosescaled
      pnorm(log((logit(threshold) - alpha) / dose_scaled), mean = beta_hat,
            sd = sqrt(beta_var))

    } else {
      stop(paste0("Don't know what to do with dfcrm model '",
                  x$dfcrm_fit$model, "'"))
    }
  }
}

#' @export
supports_sampling.dfcrm_selector <- function(x, ...) {
  return(TRUE)
}

#' @export
#' @importFrom tidyr gather
prob_tox_samples.dfcrm_selector <- function(x, tall = FALSE,
                                            num_samples = 4000,...) {
  df <- get_posterior_prob_tox_sample(x, iter = num_samples)
  if(tall) {
    dose <- prob_tox <- .draw <- NULL
    df %>%
      gather(dose, prob_tox, -.draw)
  } else {
    return(df)
  }
}

#' @export
#' @importFrom magrittr %>%
#' @importFrom dplyr mutate select everything
summary.dfcrm_selector <- function(object, ...) {
  Dose <- N <- Tox <- EmpiricToxRate <- Skeleton <- NULL
  summary.selector(object) %>%
    mutate(Skeleton = c(NA, object$skeleton)) %>%
    select(Dose, N, Tox, EmpiricToxRate, Skeleton, everything())
}