Package 'beastt' reference manual

Title:	Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials
Description:	Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data.
Authors:	Christina Fillmore [aut, cre] , Ben Arancibia [aut], Nate Bean [aut] , GlaxoSmithKline Research & Development Limited [cph, fnd]
Maintainer:	Christina Fillmore <[email protected]>
License:	Apache License (>= 2)
Version:	0.0.2
Built:	2024-11-20 10:33:36 UTC
Source:	https://github.com/gsk-biostatistics/beastt

Analysis Server

Description

Analysis Server

Usage

analysisServer(id, reactiveEndpoint)
analysisServer(id, reactiveEndpoint)

Arguments

`id`	mod ID
`reactiveEndpoint`	reactive element with the type of endpoint

BDB Code Template Maker

Description

RStudio add-in to create template BDB code

Usage

bdb_code_template_maker()
bdb_code_template_maker()

Calculate a posterior distribution that is beta (or a mixture of beta components). Only the relevant treatment arms from the internal dataset should be read in (e.g., only the control arm if constructing a posterior distribution for the control response rate).

Usage

calc_post_beta(internal_data, response, prior)
calc_post_beta(internal_data, response, prior)

Arguments

`internal_data`	This can either be a propensity score object or a tibble of the internal data.
`response`	Name of response variable
`prior`	A distributional object corresponding to a beta distribution or a mixture distribution of beta components

Details

For a given arm of an internal trial (e.g., the control arm or an active treatment arm) of size $N_I$ , suppose the response data are binary such that $y_i \sim \mbox{Bernoulli}(\theta)$ , $i=1,\ldots,N_I$ . The posterior distribution for $\theta$ is written as

$\pi( \theta \mid \boldsymbol{y} ) \propto \mathcal{L}(\theta \mid \boldsymbol{y}) \; \pi(\theta),$

where $\mathcal{L}(\theta \mid \boldsymbol{y})$ is the likelihood of the response data from the internal arm and $\pi(\theta)$ is a prior distribution on $\theta$ (either a beta distribution or a mixture distribution with an arbitrary number of beta components). The posterior distribution for $\theta$ is either a beta distribution or a mixture of beta components depending on whether the prior is a single beta distribution or a mixture distribution.

Value

distributional object

Examples

library(dplyr)
library(distributional)
calc_post_beta(internal_data = filter(int_binary_df, trt == 1),
                              response = y,
                              prior = dist_beta(0.5, 0.5))
library(dplyr)
library(distributional)
calc_post_beta(internal_data = filter(int_binary_df, trt == 1),
                              response = y,
                              prior = dist_beta(0.5, 0.5))

Calculate Posterior Normal

Description

Calculate a posterior distribution that is normal (or a mixture of normal components). Only the relevant treatment arms from the internal dataset should be read in (e.g., only the control arm if constructing a posterior distribution for the control mean).

Usage

calc_post_norm(internal_data, response, prior, internal_sd = NULL)
calc_post_norm(internal_data, response, prior, internal_sd = NULL)

Arguments

`internal_data`	This can either be a propensity score object or a tibble of the internal data.
`response`	Name of response variable
`prior`	A distributional object corresponding to a normal distribution, a t distribution, or a mixture distribution of normal and/or t components
`internal_sd`	Standard deviation of internal response data if assumed known. It can be left as `NULL` if assumed unknown

Details

For a given arm of an internal trial (e.g., the control arm or an active treatment arm) of size $N_I$ , suppose the response data are normally distributed such that $y_i \sim N(\theta, \sigma_I^2)$ , $i=1,\ldots,N_I$ . If $\sigma_I^2$ is assumed known, the posterior distribution for $\theta$ is written as

$\pi( \theta \mid \boldsymbol{y}, \sigma_{I}^2 ) \propto \mathcal{L}(\theta \mid \boldsymbol{y}, \sigma_{I}^2) \; \pi(\theta),$

where $\mathcal{L}(\theta \mid \boldsymbol{y}, \sigma_{I}^2)$ is the likelihood of the response data from the internal arm and $\pi(\theta)$ is a prior distribution on $\theta$ (either a normal distribution, a $t$ distribution, or a mixture distribution with an arbitrary number of normal and/or $t$ components). Any $t$ components of the prior for $\theta$ are approximated with a mixture of two normal distributions.

If $\sigma_I^2$ is unknown, the marginal posterior distribution for $\theta$ is instead written as

$\pi( \theta \mid \boldsymbol{y} ) \propto \left\{ \int_0^\infty \mathcal{L}(\theta, \sigma_{I}^2 \mid \boldsymbol{y}) \; \pi(\sigma_{I}^2) \; d\sigma_{I}^2 \right\} \times \pi(\theta).$

In this case, the prior for $\sigma_I^2$ is chosen to be $\pi(\sigma_{I}^2) = (\sigma_I^2)^{-1}$ such that $\left\{ \int_0^\infty \mathcal{L}(\theta, \sigma_{I}^2 \mid \boldsymbol{y}) \; \pi(\sigma_{I}^2) \; d\sigma_{I}^2 \right\}$ becomes a non-standardized $t$ distribution. This integrated likelihood is then approximated with a mixture of two normal distributions.

If internal_sd is supplied a positive value and prior corresponds to a single normal distribution, then the posterior distribution for $\theta$ is a normal distribution. If internal_sd = NULL or if other types of prior distributions are specified (e.g., mixture or t distribution), then the posterior distribution is a mixture of normal distributions.

Value

distributional object

Examples

library(distributional)
library(dplyr)
post_treated <- calc_post_norm(internal_data = filter(int_norm_df, trt == 1),
                               response = y,
                               prior = dist_normal(50, 10),
                               internal_sd = 0.15)

library(distributional)
library(dplyr)
post_treated <- calc_post_norm(internal_data = filter(int_norm_df, trt == 1),
                               response = y,
                               prior = dist_normal(50, 10),
                               internal_sd = 0.15)

Calculate Power Prior Beta

Description

Calculate a (potentially inverse probability weighted) beta power prior for the control response rate using external control data.

Usage

calc_power_prior_beta(external_data, response, prior)
calc_power_prior_beta(external_data, response, prior)

Arguments

`external_data`	This can either be a `prop_scr_obj` created by calling `create_prop_scr()` or a tibble of the external data. If it is just a tibble the weights will be assumed to be 1.
`response`	Name of response variable
`prior`	A beta distributional object that is the initial prior for the control response rate before the external control data are observed

Details

Weighted participant-level response data from an external study are incorporated into an inverse probability weighted (IPW) power prior for the control response rate $\theta_C$ . When borrowing information from an external control arm of size $N_{EC}$ , the components of the IPW power prior for $\theta_C$ are defined as follows:

Initial prior:: $\theta_C \sim \mbox{Beta}(\nu_0, \phi_0)$
IPW likelihood of the external response data $\boldsymbol{y}_E$ with weights $\hat{\boldsymbol{a}}_0$ :: $\mathcal{L}_E(\theta_C \mid \boldsymbol{y}_E, \hat{\boldsymbol{a}}_0) \propto \exp \left( \sum_{i=1}^{N_{EC}} \hat{a}_{0i} \left[ y_i \log(\theta_C) + (1 - y_i) \log(1 - \theta_C) \right] \right)$
IPW power prior:: $\theta_C \mid \boldsymbol{y}_E, \hat{\boldsymbol{a}}_0 \sim \mbox{Beta} \left( \sum_{i=1}^{N_{EC}} \hat{a}_{0i} y_i + \nu_0, \sum_{i=1}^{N_{EC}} \hat{a}_{0i} (1 - y_i) + \phi_0 \right)$

Defining the weights $\hat{\boldsymbol{a}}_0$ to equal 1 results in a conventional beta power prior.

Value

Beta power prior object

Examples

library(distributional)
library(dplyr)
# This function can be used directly on the data
calc_power_prior_beta(external_data = ex_binary_df,
  response = y,
  prior = dist_beta(0.5, 0.5))

# Or this function can be used with a propensity score object
ps_obj <- calc_prop_scr(internal_df = filter(int_binary_df, trt == 0),
  external_df = ex_binary_df,
  id_col = subjid,
  model = ~ cov1 + cov2 + cov3 + cov4)

calc_power_prior_beta(ps_obj,
  response = y,
  prior = dist_beta(0.5, 0.5))
library(distributional)
library(dplyr)
# This function can be used directly on the data
calc_power_prior_beta(external_data = ex_binary_df,
  response = y,
  prior = dist_beta(0.5, 0.5))

# Or this function can be used with a propensity score object
ps_obj <- calc_prop_scr(internal_df = filter(int_binary_df, trt == 0),
  external_df = ex_binary_df,
  id_col = subjid,
  model = ~ cov1 + cov2 + cov3 + cov4)

calc_power_prior_beta(ps_obj,
  response = y,
  prior = dist_beta(0.5, 0.5))

Calculate Power Prior Normal

Description

Calculate a (potentially inverse probability weighted) normal power prior using external data.

Usage

calc_power_prior_norm(
  external_data,
  response,
  prior = NULL,
  external_sd = NULL
)
calc_power_prior_norm(
  external_data,
  response,
  prior = NULL,
  external_sd = NULL
)

Arguments

`external_data`	This can either be a `prop_scr_obj` created by calling `create_prop_scr()` or a tibble of the external data. If it is just a tibble the weights will be assumed to be 1. Only the external data for the arm(s) of interest should be included in this object (e.g., external control data if creating a power prior for the control mean)
`response`	Name of response variable
`prior`	Either `NULL` or a normal distributional object that is the initial prior for the parameter of interest (e.g., control mean) before the external data are observed
`external_sd`	Standard deviation of external response data if assumed known. It can be left as `NULL` if assumed unknown

Details

Weighted participant-level response data from an external study are incorporated into an inverse probability weighted (IPW) power prior for the parameter of interest $\theta$ (e.g., the control mean if borrowing from an external control arm). When borrowing information from an external dataset of size $N_{E}$ , the IPW likelihood of the external response data $y_E$ with weights $\hat{\boldsymbol{a}}_0$ is defined as

$\mathcal{L}_E(\theta \mid \boldsymbol{y}_E, \hat{\boldsymbol{a}}_0, \sigma_{E}^2) \propto \exp \left( -\frac{1}{2 \sigma_{E}^2} \sum_{i=1}^{N_{E}} \hat{a}_{0i} (y_i - \theta)^2 \right).$

The prior argument should be either a distributional object with a family type of normal or NULL, corresponding to the use of a normal initial prior or an improper uniform initial prior (i.e., $\pi(\theta) \propto 1$ ), respectively.

The external_sd argument can be a positive value if the external standard deviation is assumed known or left as NULL otherwise. If external_sd = NULL, then prior must be NULL to indicate the use of an improper uniform initial prior for $\theta$ , and an improper prior is defined for the unknown external standard deviation such that $\pi(\sigma_E^2) \propto (\sigma_E^2)^{-1}$ . The details of the IPW power prior for each case are as follows:

⁠external_sd = positive value⁠ ( $\sigma_E^2$ known):: With either a proper normal or an improper uniform initial prior, the IPW weighted power prior for $\theta$ is a normal distribution.
external_sd = NULL ( $\sigma_E^2$ unknown):: With improper priors for both $\theta$ and $\sigma_E^2$ , the marginal IPW weighted power prior for $\theta$ after integrating over $\sigma_E^2$ is a non-standardized $t$ distribution.

Defining the weights $\hat{\boldsymbol{a}}_0$ to equal 1 results in a conventional normal (or $t$ ) power prior if the external standard deviation is known (unknown).

Value

Normal power prior object

Examples

library(distributional)
library(dplyr)
# This function can be used directly on the data
calc_power_prior_norm(ex_norm_df,
  response = y,
  prior = dist_normal(50, 10),
  external_sd = 0.15)

# Or this function can be used with a propensity score object
ps_obj <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
calc_power_prior_norm(ps_obj,
                     response = y,
                     prior = dist_normal(50, 10),
                     external_sd = 0.15)

library(distributional)
library(dplyr)
# This function can be used directly on the data
calc_power_prior_norm(ex_norm_df,
  response = y,
  prior = dist_normal(50, 10),
  external_sd = 0.15)

# Or this function can be used with a propensity score object
ps_obj <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
calc_power_prior_norm(ps_obj,
                     response = y,
                     prior = dist_normal(50, 10),
                     external_sd = 0.15)

Create a Propensity Score Object

Description

Calculate the propensity scores and ATT inverse probability weights for participants from internal and external datasets. Only the relevant treatment arms from each dataset should be read in (e.g., only the control arm from each dataset if creating a hybrid control arm).

Usage

calc_prop_scr(internal_df, external_df, id_col, model, ...)
calc_prop_scr(internal_df, external_df, id_col, model, ...)

Arguments

`internal_df`	Internal dataset with one row per subject and all the variables needed to run the model
`external_df`	External dataset with one row per subject and all the variables needed to run the model
`id_col`	Name of the column in both datasets used to identify each subject. It must be the same across datasets
`model`	Model used to calculate propensity scores
`...`	Optional arguments

Details

For the subset of participants in both the external and internal studies for which we want to balance the covariate distributions (e.g., external control and internal control participants if constructing a hybrid control arm), we define a study-inclusion propensity score for each participant as

$e(x_i) = P(S_i = 1 \mid x_i),$

where $x_i$ denotes a vector of baseline covariates for the $i$ th participant and $S_i$ denotes the indicator that the participant is enrolled in the internal trial ( $S_i = 1$ if internal, $S_i = 0$ if external). The estimated propensity score $\hat{e}(x_i)$ is obtained using logistic regression.

An ATT inverse probability weight is calculated for each individual as

$\hat{a}_{0i} = \frac{\hat{e}(x_i)}{\hat{P}(S_i = s_i | x_i)} = s_i + (1 - s_i ) \frac{\hat{e}(x_i)}{1 - \hat{e}(x_i)}.$

In a weighted estimator, data from participants in the external study are given a weight of $\hat{e}(x_i)⁄(1 - \hat{e}(x_i))$ whereas data from participants in the internal trial are given a weight of 1.

Value

prop_scr_obj object, with the internal and the external data and the propensity score and inverse probability weight calculated for each subject.

Examples

# This can be used for both continuous and binary data
library(dplyr)
# Continuous
calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
# Binary
calc_prop_scr(internal_df = filter(int_binary_df, trt == 0),
                       external_df = ex_binary_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)

# This can be used for both continuous and binary data
library(dplyr)
# Continuous
calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
# Binary
calc_prop_scr(internal_df = filter(int_binary_df, trt == 0),
                       external_df = ex_binary_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)

External Binary Control Data for Propensity Score Balancing

Description

This is a simulated dataset used to illustrate Bayesian dynamic borrowing in the case when borrowing from an external control arm with a binary endpoint, where the baseline covariate distributions of the internal and external data are balanced via inverse probability weighting.

Usage

ex_binary_df
ex_binary_df

Format

`ex_binary_df`

A data frame with 150 rows and 6 columns:

subjid: Unique subject ID
cov1: Covariate 1, which is normally distributed around 65 with a SD of 10
cov2: Covariate 2, which is binary (0 vs. 1) with about 30% of participants having level 1
cov3: Covariate 3, which is binary (0 vs. 1) with about 40% of participants having level 1
cov4: Covariate 4, which is binary (0 vs. 1) with about 50% of participants having level 1
y: Response, which is binary (0 vs. 1)

External Normal Control Data for Propensity Score Balancing

Description

This is a simulated dataset used to illustrate Bayesian dynamic borrowing in the case when borrowing from an external control arm with a normal endpoint, where the baseline covariate distributions of the internal and external data are balanced via inverse probability weighting.

Usage

ex_norm_df
ex_norm_df

Format

`ex_norm_df`

A data frame with 150 rows and 6 columns:

subjid: Unique subject ID
cov1: Covariate 1, which is normally distributed around 50 with a SD of 10
cov2: Covariate 2, which is binary (0 vs. 1) with about 20% of participants having level 1
cov3: Covariate 3, which is binary (0 vs. 1) with about 60% of participants having level 1
cov4: Covariate 4, which is binary (0 vs. 1) with about 30% of participants having level 1
y: Response, which is normally distributed with a SD of 0.15

Internal Binary Data for Propensity Score Balancing

Description

Usage

int_binary_df
int_binary_df

Format

`int_binary_df`

A data frame with 160 rows and 7 columns:

subjid: Unique subject ID
cov1: Covariate 1, which is normally distributed around 62 with an sd of 8
cov2: Covariate 2, which is binary (0 vs. 1) with about 40% of participants having level 1
cov3: Covariate 3, which is binary (0 vs. 1) with about 40% of participants having level 1
cov4: Covariate 4, which is binary (0 vs. 1) with about 60% of participants having level 1
trt: Treatment indicator, where 0 = control and 1 = active treatment
y: Response, which is binary (0 vs. 1)

Internal Normal Data for Propensity Score Balancing

Description

Usage

int_norm_df
int_norm_df

Format

`int_norm_df`

A data frame with 120 rows and 7 columns:

subjid: Unique subject ID
cov1: Covariate 1, which is normally distributed around 55 with a SD of 8
cov2: Covariate 2, which is binary (0 vs. 1) with about 30% of participants having level 1
cov3: Covariate 3, which is binary (0 vs. 1) with about 50% of participants having level 1
cov4: Covariate 4, which is binary (0 vs. 1) with about 30% of participants having level 1
trt: Treatment indicator, where 0 = control and 1 = active treatment
y: Response, which is normally distributed with a SD of 0.15

Test If Propensity Score Object

Description

Test If Propensity Score Object

Usage

is_prop_scr(x)
is_prop_scr(x)

Arguments

`x`	Object to test

Value

Boolean

Examples

library(dplyr)
x <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
is_prop_scr(x)

library(dplyr)
x <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
is_prop_scr(x)

norm inputs UI

Description

norm inputs UI

Usage

normalanalysisUI(id)
normalanalysisUI(id)

Arguments

id

mod id

norm input server

Description

norm input server

Usage

normalServer(id)
normalServer(id)

Arguments

id

mod id

Plot Distribution

Description

Plot Distribution

Usage

plot_dist(...)
plot_dist(...)

Arguments

...

Distributional object(s) to plot. When passing multiple objects naming them will change the labels in the plot, else they will use the distributional format

Value

ggplot object that is the density of the provided distribution

Examples

library(distributional)
plot_dist(dist_normal(0, 1))
#Plotting Multiple
plot_dist(dist_normal(0, 1), dist_normal(10, 5))
plot_dist('Prior' = dist_normal(0, 1), 'Posterior' = dist_normal(10, 5))
library(distributional)
plot_dist(dist_normal(0, 1))
#Plotting Multiple
plot_dist(dist_normal(0, 1), dist_normal(10, 5))
plot_dist('Prior' = dist_normal(0, 1), 'Posterior' = dist_normal(10, 5))

Plot Inputs server

Description

Plot Inputs server

Usage

plotServer(id)
plotServer(id)

Arguments

id

mod id

Inputs for plots

Description

Inputs for plots

Usage

plotUI(id, robust)
plotUI(id, robust)

Arguments

`id`	mod id
`robust`	Boolean if the

Density of the Propensity Score Object

Description

Plot overlapping density curves of the propensity scores for both the internal and external participants, or plot external IPWs.

Usage

prop_scr_dens(
  x,
  variable = c("propensity score", "ps", "inverse probability weight", "ipw"),
  ...
)
prop_scr_dens(
  x,
  variable = c("propensity score", "ps", "inverse probability weight", "ipw"),
  ...
)

Arguments

`x`	Propensity score object
`variable`	Variable to plot. It must be either a propensity score ("ps" or "propensity score") or inverse probability weight ("ipw" or "inverse probability weight")
`...`	Optional arguments for `geom_density`

Value

ggplot object

Examples

library(dplyr)
ps_obj <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
# Plotting the Propensity Scores
prop_scr_dens(ps_obj)
# Or plotting the inverse probability weights
prop_scr_dens(ps_obj, variable = "ipw")

library(dplyr)
ps_obj <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
# Plotting the Propensity Scores
prop_scr_dens(ps_obj)
# Or plotting the inverse probability weights
prop_scr_dens(ps_obj, variable = "ipw")

Histogram of the Propensity Score Object

Description

Plot overlapping histograms of the propensity scores for both the internal and external participants, or plot external IPWs.

Usage

prop_scr_hist(
  x,
  variable = c("propensity score", "ps", "inverse probability weight", "ipw"),
  ...
)
prop_scr_hist(
  x,
  variable = c("propensity score", "ps", "inverse probability weight", "ipw"),
  ...
)

Arguments

`x`	Propensity score object
`variable`	Variable to plot. It must be either a propensity score ("ps" or "propensity score") or inverse probability weight ("ipw" or "inverse probability weight")
`...`	Optional arguments for `geom_histogram`

Value

ggplot object

Examples

library(dplyr)
ps_obj <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
# Plotting the Propensity Scores
prop_scr_hist(ps_obj)
# Or plotting the inverse probability weights
prop_scr_hist(ps_obj, variable = "ipw")
library(dplyr)
ps_obj <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
# Plotting the Propensity Scores
prop_scr_hist(ps_obj)
# Or plotting the inverse probability weights
prop_scr_hist(ps_obj, variable = "ipw")

Love Plot of the Absolute Standardized Mean Differences

Description

Plot the unadjusted and IPW-adjusted absolute standardized mean differences for each covariate.

Usage

prop_scr_love(x, reference_line = NULL, ...)
prop_scr_love(x, reference_line = NULL, ...)

Arguments

`x`	Propensity score object
`reference_line`	Numeric value of where along the x-axis the vertical reference line should be placed
`...`	Optional options for `geom_point`

Value

ggplot object

Examples

library(dplyr)
ps_obj <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
# Plotting the Propensity Scores
prop_scr_love(ps_obj, reference_line = 0.1)

library(dplyr)
ps_obj <- calc_prop_scr(internal_df = filter(int_norm_df, trt == 0),
                       external_df = ex_norm_df,
                       id_col = subjid,
                       model = ~ cov1 + cov2 + cov3 + cov4)
# Plotting the Propensity Scores
prop_scr_love(ps_obj, reference_line = 0.1)

Robustify Normal Distributions

Description

Adds vague normal component, where the level of vagueness is controlled by the n parameter

Usage

robustify_norm(prior, n, weights = c(0.5, 0.5))
robustify_norm(prior, n, weights = c(0.5, 0.5))

Arguments

`prior`	Normal distributional object
`n`	Number of theoretical participants
`weights`	Vector of weights, where the first number corresponds to the informative component and the second is the vague

Details

In cases with a normal endpoint, a robust mixture prior can be created by adding a vague normal component to any normal prior with mean $\theta$ and variance $\sigma^2$ .The vague component is calculated to have the same mean $\theta$ and variance equal to $\sigma^2 \times n$ , where n is the specified number of theoretical participants. If robustifying a normal power prior that was calculated from external control data and n is defined as the number of external control participants, and the vague component would then correspond to one external control participant's worth of data.

Value

mixture distribution

Examples

library(distributional)
robustify_norm(dist_normal(0,1), n = 15)
library(distributional)
robustify_norm(dist_normal(0,1), n = 15)

Simulation Server

Description

Simulation Server

Usage

simulationServer(id)
simulationServer(id)

Arguments

id

mod ID

Simulation UI

Description

Simulation UI

Usage

simulationUI(id)
simulationUI(id)

Arguments

id

mod ID

Write Code function

Description

Write Code function

Usage

write_code(simulation, endpoint, selections)
write_code(simulation, endpoint, selections)

Arguments

`simulation`	A purpose, either "Simulation" or "Analysis"
`endpoint`	An endpoint type, one of "Binary", "Normal" or "Survival"
`selections`	input from UI

Package 'beastt'

Help Index

Analysis Server

Description

Usage

Arguments

Analysis UI

Description

Usage

Arguments

BDB Code Template Maker

Description

Usage

Binary Input UI

Description

Usage

Arguments

Binary Server UI

Description

Usage

Arguments

Calculate Posterior Beta

Description

Usage

Arguments

Details

Value

Examples

Calculate Posterior Normal

Description

Usage

Arguments

Details

Value

Examples

Calculate Power Prior Beta

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculate Power Prior Normal

Description

Usage

Arguments

Details

Value

See Also

Examples

Create a Propensity Score Object

Description

Usage

Arguments

Details

Value

Examples

External Binary Control Data for Propensity Score Balancing

Description

Usage

Format

ex_binary_df

External Normal Control Data for Propensity Score Balancing

Description

Usage

Format

ex_norm_df

Internal Binary Data for Propensity Score Balancing

Description

Usage

Format

int_binary_df

Internal Normal Data for Propensity Score Balancing

Description

Usage

Format

int_norm_df

Test If Propensity Score Object

Description

`ex_binary_df`

`ex_norm_df`

`int_binary_df`

`int_norm_df`