Package 'tipmap' reference manual

Title:	Tipping Point Analysis for Bayesian Dynamic Borrowing
Description:	Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.
Authors:	Christian Stock [aut, cre] , Morten Dreher [aut], Emma Torrini [ctb], Boehringer Ingelheim Pharma GmbH & Co. KG [cph, fnd]
Maintainer:	Christian Stock <[email protected]>
License:	Apache License 2.0
Version:	0.5.3
Built:	2024-11-06 06:14:27 UTC
Source:	https://github.com/boehringer-ingelheim/tipmap

Data on new trial in target population

Description

Creates a vector containing data on the new trial in the target population. This may be hypothetical data in the planning stage.

Usage

create_new_trial_data(n_total, est, se)
create_new_trial_data(n_total, est, se)

Arguments

`n_total`	The total sample size.
`est`	Treatment effect estimate.
`se`	Standard error of the treatment effect estimate.

Value

A numeric vector with data on the new trial, incl. quantiles of an assumed normal data likelihood.

Examples

new_trial_data <- create_new_trial_data(
  n_total = 30, est = 1.27, se = 0.95
)

new_trial_data <- create_new_trial_data(
  n_total = 30, est = 1.27, se = 0.95
)

Quantiles of posterior distributions for a range of weights on the informative component of the robust MAP prior

Description

Returns a data frame containing the default quantiles of posterior mixture distributions or bounds of highest posterior density intervals, generated with varying weights on the informative component of the MAP prior.

Usage

create_posterior_data(
  map_prior,
  new_trial_data,
  sigma,
  null_effect = 0,
  interval_type = "equal-tailed",
  n_samples = 10000
)
create_posterior_data(
  map_prior,
  new_trial_data,
  sigma,
  null_effect = 0,
  interval_type = "equal-tailed",
  n_samples = 10000
)

Arguments

`map_prior`	A MAP prior containing information about the trial(s) in the source population, created using `RBesT`.
`new_trial_data`	A vector containing information about the new trial. See `create_new_trial_data()`.
`sigma`	Standard deviation to be used for the weakly informative component of the MAP prior, recommended to be the unit-information standard deviation.
`null_effect`	The mean of the robust component of the MAP prior. Defaults to 0.
`interval_type`	The type of credible interval (character of length 1), either 'equal-tailed' (default) or 'hpdi', the highest posterior density interval.
`n_samples`	Number of samples to compute highest posterior density intervals (hence, only applicable if the 'interval_type' is 'hpdi').

Details

Highest posterior density intervals are based on 'coda::HPDinterval()' and are an experimental feature.

Value

A data frame containing the default quantiles of posterior mixture distributions or bounds of highest posterior density intervals.

References

Best, N., Price, R. G., Pouliquen, I. J., & Keene, O. N. (2021). Assessing efficacy in important subgroups in confirmatory trials: An example using Bayesian dynamic borrowing. Pharm Stat, 20(3), 551–562. https://doi.org/10.1002/pst.2093

Examples


# create vector containing data on new trial
new_trial_data <- create_new_trial_data(
  n_total = 30,
  est = 1.27,
  se = 0.95
)

# read MAP prior created by RBesT
map_prior <- load_tipmap_data("tipmapPrior.rds")

# create posterior data - with interval_type = equal_tailed
# (the default for tipping point plots)
posterior_data1 <- create_posterior_data(
  map_prior = map_prior,
  new_trial_data = new_trial_data,
  sigma = 12,
  interval_type = "equal-tailed"   
)


# create posterior data - with interval_type = hpdi
posterior_data2 <- create_posterior_data(
  map_prior = map_prior,
  new_trial_data = new_trial_data,
  sigma = 12,
  interval_type = "hpdi",
  n_samples = 1e4
)


# create vector containing data on new trial
new_trial_data <- create_new_trial_data(
  n_total = 30,
  est = 1.27,
  se = 0.95
)

# read MAP prior created by RBesT
map_prior <- load_tipmap_data("tipmapPrior.rds")

# create posterior data - with interval_type = equal_tailed
# (the default for tipping point plots)
posterior_data1 <- create_posterior_data(
  map_prior = map_prior,
  new_trial_data = new_trial_data,
  sigma = 12,
  interval_type = "equal-tailed"   
)


# create posterior data - with interval_type = hpdi
posterior_data2 <- create_posterior_data(
  map_prior = map_prior,
  new_trial_data = new_trial_data,
  sigma = 12,
  interval_type = "hpdi",
  n_samples = 1e4
)

Creates input data frame for construction of MAP prior

Description

Assembling information from trials in the source population in a structured way (required as a pre-processing step for MAP prior creation).

Usage

create_prior_data(study_label = NULL, n_total, est, se)
create_prior_data(study_label = NULL, n_total, est, se)

Arguments

`study_label`	An optional vector containing trial labels.
`n_total`	A vector containing total sample sizes.
`est`	A vector containing treatment effect estimates.
`se`	A vector containing standard errors of the effect estimates.

Value

A data frame containing data on the trials in the source population.

Examples

prior_data <- create_prior_data(
  n_total = c(160, 240, 320),
  est = c(1.23, 1.40, 1.51),
  se = c(0.4, 0.36, 0.31)
)
prior_data <- create_prior_data(
  n_total = c(160, 240, 320),
  est = c(1.23, 1.40, 1.51),
  se = c(0.4, 0.36, 0.31)
)

Create data frame ready to use for tipping point analysis

Description

Combines new trial data created by createTargetData(), a posterior distribution created by create_posterior_data() and a robust MAP prior using RBesT::automixfit() and an optional meta-analysis, e.g. created using the meta package, into a data frame needed for the functions tipmap_plot() and get_tipping_point().

Usage

create_tipmap_data(new_trial_data, posterior, map_prior, meta_analysis = NULL)
create_tipmap_data(new_trial_data, posterior, map_prior, meta_analysis = NULL)

Arguments

`new_trial_data`	A data frame containing data on the new trial in the target population. See `create_new_trial_data()`.
`posterior`	A mixture combining MAP prior and target population. See `create_posterior_data()`.
`map_prior`	A robust MAP prior created by `RBesT::automixfit()`.
`meta_analysis`	A data frame containing a meta-analysis of trial(s) to be borrowed from. See `createPriorData()`.

Value

A data frame ready to be used for tipmap_plot() and get_tipping_point()

Examples


# specify new trial data
new_trial_data <- create_new_trial_data(n_total = 30, est = 1.5, se = 2.1)

# read MAP prior data
map_prior <- load_tipmap_data("tipmapPrior.rds")

# read posterior data
posterior <- load_tipmap_data("tipPost.rds")

tip_dat <- create_tipmap_data(
  new_trial_data = new_trial_data,
  posterior = posterior,
  map_prior = map_prior
)

# specify new trial data
new_trial_data <- create_new_trial_data(n_total = 30, est = 1.5, se = 2.1)

# read MAP prior data
map_prior <- load_tipmap_data("tipmapPrior.rds")

# read posterior data
posterior <- load_tipmap_data("tipPost.rds")

tip_dat <- create_tipmap_data(
  new_trial_data = new_trial_data,
  posterior = posterior,
  map_prior = map_prior
)

Default quantiles

Description

Default quantiles

Usage

default_quantiles
default_quantiles

Format

An object of class numeric of length 13.

Default weights

Description

Default weights

Usage

default_weights
default_weights

Format

An object of class numeric of length 201.

Draw samples from a mixture of beta distributions

Description

Draws samples from a mixture of beta distributions, representing pooled weights on the informative component of a robust MAP prior, as elicited from experts via the roulette method.

Usage

draw_beta_mixture_nsamples(n, chips_mult, expert_weight = NULL)
draw_beta_mixture_nsamples(n, chips_mult, expert_weight = NULL)

Arguments

`n`	Numeric value, the number of samples to be drawn.
`chips_mult`	Numeric matrix, containing expert weighting (distributions of chips). Rows should represent experts, columns should represent bins / weight intervals.
`expert_weight`	An optional numeric vector, containing the weight assigned to each expert (defaults to equal weights).

Value

A numeric vector containing samples from a pooled distribution of expert opinions.

Examples

rweights <- draw_beta_mixture_nsamples(
  n = 50,
  chips_mult = rbind(
    c(0, 0, 0, 0, 2, 3, 3, 2, 0, 0),
    c(0, 0, 0, 1, 2, 4, 2, 1, 0, 0),
    c(0, 0, 0, 2, 2, 2, 2, 2, 0, 0)
  ),
  expert_weight = rep(1/3, 3)
)
print(rweights)
## Not run: 
hist(rweights)

## End(Not run)
rweights <- draw_beta_mixture_nsamples(
  n = 50,
  chips_mult = rbind(
    c(0, 0, 0, 0, 2, 3, 3, 2, 0, 0),
    c(0, 0, 0, 1, 2, 4, 2, 1, 0, 0),
    c(0, 0, 0, 2, 2, 2, 2, 2, 0, 0)
  ),
  expert_weight = rep(1/3, 3)
)
print(rweights)
## Not run: 
hist(rweights)

## End(Not run)

Fit beta distribution for one expert

Description

Fit beta distribution to data elicited from one expert via the roulette method.

Usage

fit_beta_1exp(df)
fit_beta_1exp(df)

Arguments

`df`	A dataframe generated by `get_model_input_1exp`.

Details

This function is based on SHELF::fitdist and yields identical results.

Value

Parameters (alpha and beta) of a beta fit.

Examples

chips <- c(0, 2, 3, 2, 1, 1, 1, 0, 0, 0)
x <- get_cum_probs_1exp(chips)
y <- get_model_input_1exp(x)
fit_beta_1exp(df = y)["par"] 

chips <- c(0, 2, 3, 2, 1, 1, 1, 0, 0, 0)
x <- get_cum_probs_1exp(chips)
y <- get_model_input_1exp(x)
fit_beta_1exp(df = y)["par"]

Fit beta distributions for multiple experts

Description

Fit beta distributions to data elicited from multiple experts via the roulette method.

Usage

fit_beta_mult_exp(chips_mult)
fit_beta_mult_exp(chips_mult)

Arguments

chips_mult

A dataframe or matrix containing weights. It should contain one row per expert and 10 columns, one for each bin, representing weights from 0 to 1.

Value

A dataframe containing the parameters of the individual beta distributions.

Examples

beta_fits <- fit_beta_mult_exp(
  chips_mult = rbind(
    c(0, 0, 0, 0, 2, 3, 3, 2, 0, 0),
    c(0, 0, 0, 1, 2, 4, 2, 1, 0, 0),
    c(0, 0, 0, 2, 2, 2, 2, 2, 0, 0)
  )
)
print(beta_fits)

beta_fits <- fit_beta_mult_exp(
  chips_mult = rbind(
    c(0, 0, 0, 0, 2, 3, 3, 2, 0, 0),
    c(0, 0, 0, 1, 2, 4, 2, 1, 0, 0),
    c(0, 0, 0, 2, 2, 2, 2, 2, 0, 0)
  )
)
print(beta_fits)

Get cumulative probabilities from distribution of chips of one expert

Description

Get cumulative probabilities from distribution of chips of one expert

Usage

get_cum_probs_1exp(chips)
get_cum_probs_1exp(chips)

Arguments

chips

Vector of integers, representing the distribution of chips assigned by one expert, as elicited through the roulette method. Each element of the vector represents one bin in the grid.

Value

A numeric vector with the cumulative distribution of chips.

Examples

chips <- c(0, 2, 3, 2, 1, 1, 1, 0, 0, 0)
x <- get_cum_probs_1exp(chips)
print(x)

chips <- c(0, 2, 3, 2, 1, 1, 1, 0, 0, 0)
x <- get_cum_probs_1exp(chips)
print(x)

Transform cumulative probabilities to fit beta distributions

Description

Transform cumulative probabilities to fit beta distributions

Usage

get_model_input_1exp(cum_probs, w = NULL)
get_model_input_1exp(cum_probs, w = NULL)

Arguments

`cum_probs`	Numeric vector, containing cumulative probabilities of weights for one expert, as elicited through the roulette method. Each element of the vector represents one bin in the grid.
`w`	Numeric vector, upper interval limit of bin (defaults to `1:length(cum_probs) / length(cum_probs)`).

Value

Dataframe to be used as input to fit beta distributions by [fit_beta_1exp()].

Examples

chips <- c(0, 2, 3, 2, 1, 1, 1, 0, 0, 0)
x <- get_cum_probs_1exp(chips)
print(x)
y <- get_model_input_1exp(x)
print(y)

chips <- c(0, 2, 3, 2, 1, 1, 1, 0, 0, 0)
x <- get_cum_probs_1exp(chips)
print(x)
y <- get_model_input_1exp(x)
print(y)

Filter posterior by given weights

Description

Returns quantiles of the posterior distribution of the treatment effect for one or more specified weights.

Usage

get_posterior_by_weight(posterior, weight)
get_posterior_by_weight(posterior, weight)

Arguments

`posterior`	The posterior data to be filtered (see [create_posterior_data()]).
`weight`	The weight(s) to be filtered by.

Value

The filtered posterior values

Examples

get_posterior_by_weight(
  posterior = load_tipmap_data("tipPost.rds"),
  weight = c(0.05, 0.1)
)

get_posterior_by_weight(
  posterior = load_tipmap_data("tipPost.rds"),
  weight = c(0.05, 0.1)
)

Summarize expert weights

Description

Computes minimum, maximum, mean and quartiles for expert weights.

Usage

get_summary_mult_exp(chips_mult, n = 500, expert_weight = NULL)
get_summary_mult_exp(chips_mult, n = 500, expert_weight = NULL)

Arguments

`chips_mult`	Numeric matrix, containing expert weights.
`n`	Number of samples to be drawn to obtain summary statistics (defaults to 500).
`expert_weight`	Weights assigned to each expert (defaults to equal weights).

Value

A vector containing summary statistics.

Examples

get_summary_mult_exp(
  chips_mult = rbind(
    c(0, 0, 0, 0, 2, 3, 3, 2, 0, 0),
    c(0, 0, 0, 1, 2, 4, 2, 1, 0, 0),
    c(0, 0, 0, 2, 2, 2, 2, 2, 0, 0)
  ), 
  n = 50
)

get_summary_mult_exp(
  chips_mult = rbind(
    c(0, 0, 0, 0, 2, 3, 3, 2, 0, 0),
    c(0, 0, 0, 1, 2, 4, 2, 1, 0, 0),
    c(0, 0, 0, 2, 2, 2, 2, 2, 0, 0)
  ), 
  n = 50
)

Identify tipping point for a specific quantile.

Description

Identifies the weights closest to tipping points for specified quantiles.

Usage

get_tipping_points(tipmap_data, quantile, null_effect = 0)
get_tipping_points(tipmap_data, quantile, null_effect = 0)

Arguments

`tipmap_data`	A data frame created by `create_tipmap_data()`.
`quantile`	The quantile(s) of the tipping point. Possible values are 0.025, 0.05, 0.1, 0.2, 0.8, 0.9, 0.95 and 0.975.
`null_effect`	The null treatment effect. Defaults to 0.

Value

The weight closest to the tipping point for the specified quantile

Examples

tip_dat <- load_tipmap_data("tipdat.rds")#'
get_tipping_points(tip_dat, quantile = 0.025)
get_tipping_points(tip_dat, quantile = c(0.025, 0.05, 0.1, 0.2), null_effect = 0.1)

tip_dat <- load_tipmap_data("tipdat.rds")#'
get_tipping_points(tip_dat, quantile = 0.025)
get_tipping_points(tip_dat, quantile = c(0.025, 0.05, 0.1, 0.2), null_effect = 0.1)

Load exemplary datasets

Description

Loads one of three exemplary datasets in the package.

Usage

load_tipmap_data(file)
load_tipmap_data(file)

Arguments

file

The dataset to be loaded.

Value

A pre-saved dataset.

Examples


load_tipmap_data(file = "tipdat.rds")
load_tipmap_data(file = "tipmapPrior.rds")
load_tipmap_data(file = "tipPost.rds")

load_tipmap_data(file = "tipdat.rds")
load_tipmap_data(file = "tipmapPrior.rds")
load_tipmap_data(file = "tipPost.rds")

Assessing bias

Description

Assessment of absolute bias in posterior means and medians for a given weight and evidence level, using simulated data as input.

Usage

oc_bias(
  m,
  se,
  true_effect,
  weights = seq(0, 1, by = 0.01),
  map_prior,
  sigma,
  n_cores = 1,
  eval_strategy = "sequential"
)
oc_bias(
  m,
  se,
  true_effect,
  weights = seq(0, 1, by = 0.01),
  map_prior,
  sigma,
  n_cores = 1,
  eval_strategy = "sequential"
)

Arguments

`m`	Numerical vector of simulated effect estimates.
`se`	Numerical vector of simulated standard errors (`m` and `se` need to have the same length).
`true_effect`	Numerical value, representing the true treatment effect (usually the mean of the simulated `m`).
`weights`	Vector of weights of the informative component of the MAP prior (defaults to `seq(0, 1, by = 0.01)`).
`map_prior`	A MAP prior containing information about the trials in the source population, created using `RBesT`; a mixture of normal distributions is required.
`sigma`	Standard deviation of the weakly informative component of the MAP prior, recommended to be the unit-information standard deviation.
`n_cores`	Integer value, representing the number of cores to be used (defaults to 1); only applies if `eval_strategy` is not "sequential".
`eval_strategy`	Character variable, representing the evaluation strategy, either "sequential", "multisession", or "multicore" (see documentation of `future::plan`, defaults to "sequential").

Value

A 2-dimensional array containing results on bias.

Examples

set.seed(123)
n_sims <- 5 # small number for exemplary application 
sim_dat <- list(
  "m" = rnorm(n = n_sims, mean = 1.15, sd = 0.1),
  "se" = rnorm(n = n_sims, mean = 1.8, sd = 0.3)
)
results <- oc_bias(
  m = sim_dat[["m"]],
  se = sim_dat[["se"]],
  true_effect = 1.15,
  weights = seq(0, 1, by = 0.01), 
  map_prior = load_tipmap_data("tipmapPrior.rds"), 
  sigma = 16.23,
  eval_strategy = "sequential",
  n_cores = 1
) 
print(results)
set.seed(123)
n_sims <- 5 # small number for exemplary application 
sim_dat <- list(
  "m" = rnorm(n = n_sims, mean = 1.15, sd = 0.1),
  "se" = rnorm(n = n_sims, mean = 1.8, sd = 0.3)
)
results <- oc_bias(
  m = sim_dat[["m"]],
  se = sim_dat[["se"]],
  true_effect = 1.15,
  weights = seq(0, 1, by = 0.01), 
  map_prior = load_tipmap_data("tipmapPrior.rds"), 
  sigma = 16.23,
  eval_strategy = "sequential",
  n_cores = 1
) 
print(results)

Assessing coverage

Description

Assessment of coverage of posterior intervals for a given weight and evidence level, using simulated data as input.

Usage

oc_coverage(
  m,
  se,
  true_effect,
  weights = seq(0, 1, by = 0.01),
  map_prior,
  sigma,
  n_cores = 1,
  eval_strategy = "sequential"
)
oc_coverage(
  m,
  se,
  true_effect,
  weights = seq(0, 1, by = 0.01),
  map_prior,
  sigma,
  n_cores = 1,
  eval_strategy = "sequential"
)

Arguments

`m`	Numerical vector of simulated effect estimates.
`se`	Numerical vector of simulated standard errors (`m` and `se` need to have the same length).
`true_effect`	Numerical value, representing the true treatment effect (usually the mean of the simulated `m`).
`weights`	Vector of weights of the informative component of the MAP prior (defaults to `seq(0, 1, by = 0.01)`).
`map_prior`	A MAP prior containing information about the trials in the source population, created using `RBesT`; a mixture of normal distributions is required.
`sigma`	Standard deviation of the weakly informative component of the MAP prior, recommended to be the unit-information standard deviation.
`n_cores`	Integer value, representing the number of cores to be used (defaults to 1); only applies if `eval_strategy` is not "sequential".
`eval_strategy`	Character variable, representing the evaluation strategy, either "sequential", "multisession", or "multicore" (see documentation of `future::plan`, defaults to "sequential").

Value

A 2-dimensional array containing results on coverage.

Examples

set.seed(123)
n_sims <- 5 # small number for exemplary application 
sim_dat <- list(
  "m" = rnorm(n = n_sims, mean = 1.15, sd = 0.1),
  "se" = rnorm(n = n_sims, mean = 1.8, sd = 0.3)
)
results <- oc_coverage(
  m = sim_dat[["m"]],
  se = sim_dat[["se"]],
  true_effect = 1.15,
  weights = seq(0, 1, by = 0.01), 
  map_prior = load_tipmap_data("tipmapPrior.rds"), 
  sigma = 16.23
) 
print(results)
set.seed(123)
n_sims <- 5 # small number for exemplary application 
sim_dat <- list(
  "m" = rnorm(n = n_sims, mean = 1.15, sd = 0.1),
  "se" = rnorm(n = n_sims, mean = 1.8, sd = 0.3)
)
results <- oc_coverage(
  m = sim_dat[["m"]],
  se = sim_dat[["se"]],
  true_effect = 1.15,
  weights = seq(0, 1, by = 0.01), 
  map_prior = load_tipmap_data("tipmapPrior.rds"), 
  sigma = 16.23
) 
print(results)

Assessing probability of success

Description

Assessment of the probability of truly or falsely (depending on simulated scenario) rejecting the null hypothesis of interest for a given weight and evidence level, using simulated data as input.

Usage

oc_pos(
  m,
  se,
  probs,
  weights = seq(0, 1, by = 0.01),
  map_prior,
  sigma,
  null_effect = 0,
  direction_pos = T,
  n_cores = 1,
  eval_strategy = "sequential"
)
oc_pos(
  m,
  se,
  probs,
  weights = seq(0, 1, by = 0.01),
  map_prior,
  sigma,
  null_effect = 0,
  direction_pos = T,
  n_cores = 1,
  eval_strategy = "sequential"
)

Arguments

`m`	Numerical vector of simulated effect estimates.
`se`	Numerical vector of simulated standard errors (`m` and `se` need to have the same length).
`probs`	Vector of quantiles q, with 1 minus q representing an evidence level of interest (where positive effect estimate indicate a beneficial treatment).
`weights`	Vector of weights of the informative component of the MAP prior (defaults to `seq(0, 1, by = 0.01)`).
`map_prior`	A MAP prior containing information about the trials in the source population, created using `RBesT`; a mixture of normal distributions is required.
`sigma`	Standard deviation of the weakly informative component of the MAP prior, recommended to be the unit-information standard deviation.
`null_effect`	Numerical value, representing the null effect (defaults to 0).
`direction_pos`	Logical value, `TRUE` (default) if effects greater that the `null_effect` indicate a beneficial treatment and `FALSE` otherwise.
`n_cores`	Integer value, representing the number of cores to be used (defaults to 1); only applies if `eval_strategy` is not "sequential".
`eval_strategy`	Character variable, representing the evaluation strategy, either "sequential", "multisession", or "multicore" (see documentation of `future::plan`, defaults to "sequential").

Value

A 2-dimensional array containing probabilities, either of truly (probability of success) or falsely rejecting the null hypothesis of interest for a given weight and evidence level.

Examples

set.seed(123)
n_sims <- 5 # small number for exemplary application
sim_dat <- list(
  "m" = rnorm(n = n_sims, mean = 1.15, sd = 0.1),
  "se" = rnorm(n = n_sims, mean = 1.8, sd = 0.3)
)
results <- oc_pos(
  m = sim_dat[["m"]],
  se = sim_dat[["se"]],
  probs = c(0.025, 0.05, 0.1, 0.2), 
  weights = seq(0, 1, by = 0.01), 
  map_prior = load_tipmap_data("tipmapPrior.rds"), 
  sigma = 16.23,
  null_effect = 0,
  direction_pos = TRUE, 
  eval_strategy = "sequential",
  n_cores = 1
) 
print(results)
set.seed(123)
n_sims <- 5 # small number for exemplary application
sim_dat <- list(
  "m" = rnorm(n = n_sims, mean = 1.15, sd = 0.1),
  "se" = rnorm(n = n_sims, mean = 1.8, sd = 0.3)
)
results <- oc_pos(
  m = sim_dat[["m"]],
  se = sim_dat[["se"]],
  probs = c(0.025, 0.05, 0.1, 0.2), 
  weights = seq(0, 1, by = 0.01), 
  map_prior = load_tipmap_data("tipmapPrior.rds"), 
  sigma = 16.23,
  null_effect = 0,
  direction_pos = TRUE, 
  eval_strategy = "sequential",
  n_cores = 1
) 
print(results)

Custom dark blue

Description

Custom dark blue

Usage

tipmap_darkblue
tipmap_darkblue

Format

An object of class character of length 1.

Custom light red

Description

Custom light red

Usage

tipmap_lightred
tipmap_lightred

Format

An object of class character of length 1.

Visualize tipping point analysis

Description

Uses a data frame created by create_tipmap_data() to visualize the tipping point analysis.

Usage

tipmap_plot(
  tipmap_data,
  target_pop_lab = "Trial in target\n population",
  y_range = NULL,
  y_breaks = NULL,
  title = NULL,
  y_lab = "Mean difference",
  x_lab = "Weight on informative component of robust MAP prior",
  map_prior_lab = "MAP\nprior",
  meta_analysis_lab = "MA",
  legend_title = "Posterior quantile",
  null_effect = 0
)
tipmap_plot(
  tipmap_data,
  target_pop_lab = "Trial in target\n population",
  y_range = NULL,
  y_breaks = NULL,
  title = NULL,
  y_lab = "Mean difference",
  x_lab = "Weight on informative component of robust MAP prior",
  map_prior_lab = "MAP\nprior",
  meta_analysis_lab = "MA",
  legend_title = "Posterior quantile",
  null_effect = 0
)

Arguments

`tipmap_data`	A data frame containing tipping point data, generated by [create_tipmap_data()].
`target_pop_lab`	A label for the trial in the target population.
`y_range`	An optional argument specifying range of the y-axis.
`y_breaks`	An optional vector specifying breaks on the y-axis.
`title`	The plot title.
`y_lab`	The label for the y axis. Defaults to "Mean difference".
`x_lab`	The label for the x axis. Defaults to "Weight on informative component of MAP prior".
`map_prior_lab`	The label for the MAP prior. Defaults to "MAP prior"
`meta_analysis_lab`	An optional label for a meta-analysis (if included).
`legend_title`	An optional title for the plot legend. Defaults to "Posterior quantiles".
`null_effect`	The null treatment effect, determining where tipping points are calculated. Defaults to 0.

Value

A ggplot object of the tipping point plot

Examples


tipmap_data <- load_tipmap_data("tipdat.rds")
tipmap_plot(tipmap_data)

tipmap_data <- load_tipmap_data("tipdat.rds")
tipmap_plot(tipmap_data)

Package 'tipmap'

Help Index

Data on new trial in target population

Description

Usage

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Value

See Also

Examples

Quantiles of posterior distributions for a range of weights on the informative component of the robust MAP prior

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References

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Examples

Creates input data frame for construction of MAP prior

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Examples

Create data frame ready to use for tipping point analysis

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Value

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Examples

Default quantiles

Description

Usage

Format

Default weights

Description

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Format

Draw samples from a mixture of beta distributions

Description

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Value

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Examples

Fit beta distribution for one expert

Description

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Value

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Examples

Fit beta distributions for multiple experts

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Value

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Examples

Get cumulative probabilities from distribution of chips of one expert

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Examples

Transform cumulative probabilities to fit beta distributions

Description

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Value

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Examples

Filter posterior by given weights

Description

Usage

Arguments