Package {BayesPocket}

Type:	Package
Title:	Bayesian Causal Inference for Periodontal Diseases in Longitudinal Studies
Version:	0.1.0
Description:	Implements the Mixed Treatment-State Causal Model (MTSCM), a Bayesian framework for estimating causal effects of clinical interventions on bounded continuous outcomes in longitudinal observational studies with irregular visits. The methodology is specifically designed for periodontal disease research, where discrete treatments and continuous disease states (e.g., proportion of periodontal pockets exceeding 3 mm) reciprocally influence one another under dynamic feedback. The package integrates a double-censored Tobit likelihood to handle boundary mass at zero and one, subject-specific random effects to capture within-subject correlation, and flexible tree-based ensemble priors (standard BART and Soft BART) to model complex nonlinear interactions without parametric restrictions. Causal identification is established under the potential outcomes framework via the G-computation formula, with key estimands including the Mixed Average Potential Outcome (MAPO) and the Mixed Probability of Disease Resolution (MPDR). The package provides functions for model fitting, posterior inference, and causal estimand estimation.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	stats (≥ 4.4.2), GIGrvg(≥ 0.8), truncnorm(≥ 1.0-9), progress(≥ 1.2.3), stochtree(≥ 0.1.1), SoftBart(≥ 1.0.3), parallel(≥ 4.4.2), pbmcapply(≥ 1.5.1)
Depends:	R (≥ 3.5)
NeedsCompilation:	no
Packaged:	2026-05-08 20:31:34 UTC; kevin_liu
Author:	Qingyang Liu [aut, cre], Debdeep Pati [aut], Yang Ni [aut], Dipankar Bandyopadhyay [aut]
Maintainer:	Qingyang Liu <rh8liuqy@gmail.com>
Repository:	CRAN
Date/Publication:	2026-05-13 08:10:22 UTC

The 'BayesPocket' package.

Description

Implements a Bayesian double-censored model for causal inference in longitudinal studies of periodontal disease progression. The package provides tools for estimating causal effects of treatments on disease outcomes, accounting for time-varying confounders and left- and right-censored outcomes. It uses a Tobit regression model with extended Bayesian additive regression trees (XBART) for flexible modeling of complex relationships. The methodology is designed for observational dental data where treatments are assigned adaptively over time. Includes functions for model fitting and posterior inference of causal estimands.

Value

This is the summary page. No return value.

Author(s)

Maintainer: Qingyang Liu rh8liuqy@gmail.com (ORCID)

Authors:

• Debdeep Pati dpati2@wisc.edu

• Yang Ni yang.ni@austin.utexas.edu

• Dipankar Bandyopadhyay dbandyop@vcu.edu

Iterate Causal Estimand Calculations Over a Grid

Description

Wrapper to iterate causal estimand calculations over a grid of previous status values.

Usage

causal_estimand_inference(
  outcome_model_results,
  df,
  continuous_name,
  categorical_name,
  treatment_name,
  treatment_value,
  previous_status_name,
  credible_interval_level = 0.95,
  num_of_grids = 50
)

Arguments

Details

This function evaluates the causal estimands (such as MAPO and MPDR) across a specified grid of values for the previous disease state. For comprehensive details regarding the underlying framework, methodology, and the main model fitting procedure, please refer to causal_inference_model.

Value

a list of causal estimands calculated across the evaluation grid.

See Also

causal_inference_model

Examples

# data generation ---------------------------------------------------------

df1 <- data_generation(random_seed = 100,
                       N = 100,
                       sigma = 0.2,
                       sigma_u = 0.1)

# draw samples from the posterior distribution ----------------------------

inference_output <- causal_inference_model(df = df1,
                                           y_name = "current_value",
                                           continuous_name = c("previous_value",
                                                               "confounder"),
                                           categorical_name = c("treatment"),
                                           treatment_name = "treatment",
                                           previous_status_name = "previous_value",
                                           subjectID_name = "subjectID",
                                           num_warmup = 2,
                                           num_samples = 2,
                                           model_type = "Tobit-XBART",
                                           thin = 1,
                                           L = 5,
                                           alpha = 0.95,
                                           beta = 1.25,
                                           leaf_model_scale = 0.3/5,
                                           cutpoint_grid_size = 100,
                                           max_depth = 10,
                                           credible_interval_level = 0.95,
                                           random_seed = 100,
                                           calculate_causal_estimand = FALSE,
                                           previous_status_grid_size = 2)

# calculate the causal estimand over a grid of previous status values -------

outcome_model_results <- inference_output$outcome_model_results

inference_results <- causal_estimand_inference(outcome_model_results = outcome_model_results,
                 df = inference_output$df,
                 continuous_name = inference_output$continuous_name,
                 categorical_name = inference_output$categorical_name,
                 treatment_name = inference_output$treatment_name,
                 treatment_value = factor("TREATMENT A",
                                          levels = levels(df1$treatment)),
                 previous_status_name = inference_output$previous_status_name,
                 credible_interval_level = 0.95,
                 num_of_grids = 2) # Example uses a small 2-point grid

# View the newly calculated closed-form causal estimands ------------------

# 1. Print results for the first grid point
cat("--- Results for Grid Point 1 ---\n")
print(inference_results[[1]]$mapo_summary)
print(inference_results[[1]]$mpdr_summary)

# 2. Print results for the second grid point
cat("\n--- Results for Grid Point 2 ---\n")
print(inference_results[[2]]$mapo_summary)
print(inference_results[[2]]$mpdr_summary)

Bayesian Mixed Treatment-State Causal Model (MTSCM)

Description

Fits a Bayesian Mixed Treatment-State Causal Model (MTSCM) tailored for longitudinal settings with irregular visits. This model is specifically designed for bounded continuous outcomes with mass at both boundaries, such as the proportion of periodontal pockets exceeding 3 mm.

Usage

causal_inference_model(
  df,
  y_name,
  continuous_name,
  categorical_name,
  treatment_name,
  previous_status_name,
  subjectID_name,
  num_warmup,
  num_samples,
  model_type,
  thin = 1,
  L = 50,
  alpha = 0.95,
  beta = 1.25,
  leaf_model_scale = 0.3/50,
  cutpoint_grid_size = 100,
  max_depth = 10,
  credible_interval_level = 0.95,
  print_progress = TRUE,
  random_seed = 100,
  calculate_causal_estimand = FALSE,
  previous_status_grid_size = 100
)

Arguments

Details

Outcome Model Description: The MTSCM utilizes a double-censored Tobit regression structure with subject-level random effects to capture within-subject correlation. It uses tree-based ensemble priors (encompassing standard BART and Soft BART) to flexibly model complex, non-linear interactions without parametric restrictions. Using a latent Gaussian variable Z_{i,j+1}, the data generating process for the outcome Y_{i,j+1} is formulated as:

Z_{i,j+1} = f(A_{ij}, Y_{ij}, \mathbf{X}_{ij}) + U_i + \epsilon_{ij}, \quad \epsilon_{ij} \sim N(0, \sigma^2).

Y_{i,j+1} = \begin{cases} 0 & \text{if } Z_{i,j+1} \leq 0, \\ Z_{i,j+1} & \text{if } 0 < Z_{i,j+1} < 1, \\ 1 & \text{if } Z_{i,j+1} \geq 1. \end{cases}

Causal Estimand Description: The framework estimates causal effects using the G-computation formula to marginalize conditional expectations over the covariate distribution.

Mixed Average Potential Outcome (MAPO): The MAPO represents the expected potential outcome across the population for a given treatment a and previous continuous disease state y^{\star}. It is defined mathematically as:

\theta(a,y^{\star}) = \frac{\sum_{i=1}^{N} \sum_{j=0}^{n_i - 1} \mathbb{E}(Y_{i,j+1} \mid A_{ij} = a, Y_{ij} = y^{\star}, \mathbf{X}_{ij} = \mathbf{x}_{ij})}{\sum_{i=1}^{N} n_{i}}.

Mixed Probability of Disease Resolution (MPDR): The MPDR estimates the population-level probability of achieving a zero disease burden under a specific treatment a and previous disease state y^{\star}. It is computed as:

\theta^{(0)}(a,y^{\star}) = \frac{\sum_{i=1}^{N} \sum_{j=0}^{n_i - 1} \mathbb{P}(Y_{i,j+1} = 0 \mid A_{ij} = a, Y_{ij} = y^{\star}, \mathbf{X}_{ij} = \mathbf{x}_{ij})}{\sum_{i=1}^{N} n_{i}}.

Value

a list containing posterior inference of causal estimands.

Examples

df1 <- data_generation(random_seed = 100,
                       N = 100,
                       sigma = 0.2,
                       sigma_u = 0.1)

inference_output <- causal_inference_model(df = df1,
                                           y_name = "current_value",
                                           continuous_name = c("previous_value",
                                                               "confounder"),
                                           categorical_name = c("treatment"),
                                           treatment_name = "treatment",
                                           previous_status_name = "previous_value",
                                           subjectID_name = "subjectID",
                                           num_warmup = 2,
                                           num_samples = 2,
                                           model_type = "Tobit-XBART",
                                           thin = 1,
                                           L = 5,
                                           alpha = 0.95,
                                           beta = 1.25,
                                           leaf_model_scale = 0.3/5,
                                           cutpoint_grid_size = 100,
                                           max_depth = 10,
                                           credible_interval_level = 0.95,
                                           random_seed = 100,
                                           calculate_causal_estimand = FALSE,
                                           previous_status_grid_size = 2)

Data Generation Program

Description

Generates simulated data for evaluating the causal inference models.

Usage

data_generation(random_seed, N, sigma, sigma_u)

Arguments

Value

data_generation returns a simulated data.frame.

Examples

df1 <- data_generation(random_seed = 100,
                       N = 100,
                       sigma = 0.2,
                       sigma_u = 0.1)

print(head(df1))