Analysis of haemodynamics surrounding blood transfusions after the arterial switch operation: a pilot study utilising real-time telemetry high-frequency data capture.

BACKGROUND: Packed red blood cell transfusions occur frequently after congenital heart surgery to augment haemodynamics, with limited understanding of efficacy. The goal of this study was to analyse the hemodynamic response to packed red blood cell transfusions in a single cohort, as "proof-of-concept" utilising high-frequency data capture of real-time telemetry monitoring. METHODS: Retrospective review of patients after the arterial switch operation receiving packed red blood cell transfusions from 15 July 2020 to 15 July 2021. Hemodynamic parameters were collected from a high-frequency data capture system (SickbayTM) continuously recording vital signs from bedside monitors and analysed in 5-minute intervals up to 6 hours before, 4 hours during, and 6 hours after packed red blood cell transfusions-up to 57,600 vital signs per packed red blood cell transfusions. Variables related to oxygen balance included blood gas co-oximetry, lactate levels, near-infrared spectroscopy, and ventilator settings. Analgesic, sedative, and vasoactive infusions were also collected. RESULTS: Six patients, at 8.5[IQR:5-22] days old and weighing 3.1[IQR:2.8-3.2]kg, received transfusions following the arterial switch operation. There were 10 packed red blood cell transfusions administered with a median dose of 10[IQR:10-15]mL/kg over 169[IQR:110-190]min; at median post-operative hour 36[IQR:10-40]. Significant increases in systolic and mean arterial blood pressures by 5-12.5% at 3 hours after packed red blood cell transfusions were observed, while renal near-infrared spectroscopy increased by 6.2% post-transfusion. No significant changes in ventilation, vasoactive support, or laboratory values related to oxygen balance were observed. CONCLUSIONS: Packed red blood cell transfusions given after the arterial switch operation increased arterial blood pressure by 5-12.5% for 3 hours and renal near-infrared spectroscopy by 6.2%. High-frequency data capture systems can be leveraged to provide novel insights into the hemodynamic response to commonly used therapies such as packed red blood cell transfusions after paediatric cardiac surgery.

Prior and posterior checking of implicit causal assumptions.

Causal inference practitioners have increasingly adopted machine learning techniques with the aim of producing principled uncertainty quantification for causal effects while minimizing the risk of model misspecification. Bayesian nonparametric approaches have attracted attention as well, both for their flexibility and their promise of providing natural uncertainty quantification. Priors on high-dimensional or nonparametric spaces, however, can often unintentionally encode prior information that is at odds with substantive knowledge in causal inference-specifically, the regularization required for high-dimensional Bayesian models to work can indirectly imply that the magnitude of the confounding is negligible. In this paper, we explain this problem and provide tools for (i) verifying that the prior distribution does not encode an inductive bias away from confounded models and (ii) verifying that the posterior distribution contains sufficient information to overcome this issue if it exists. We provide a proof-of-concept on simulated data from a high-dimensional probit-ridge regression model, and illustrate on a Bayesian nonparametric decision tree ensemble applied to a large medical expenditure survey.

Causal mediation and sensitivity analysis for mixed-scale data.

The goal of causal mediation analysis, often described within the potential outcomes framework, is to decompose the effect of an exposure on an outcome of interest along different causal pathways. Using the assumption of sequential ignorability to attain non-parametric identification, Imai et al. (2010) proposed a flexible approach to measuring mediation effects, focusing on parametric and semiparametric normal/Bernoulli models for the outcome and mediator. Less attention has been paid to the case where the outcome and/or mediator model are mixed-scale, ordinal, or otherwise fall outside the normal/Bernoulli setting. We develop a simple, but flexible, parametric modeling framework to accommodate the common situation where the responses are mixed continuous and binary, and, apply it to a zero-one inflated beta model for the outcome and mediator. Applying our proposed methods to the publicly-available JOBS II dataset, we (i) argue for the need for non-normal models, (ii) show how to estimate both average and quantile mediation effects for boundary-censored data, and (iii) show how to conduct a meaningful sensitivity analysis by introducing unidentified, scientifically meaningful, sensitivity parameters.

Bayesian additive regression trees for multivariate skewed responses.

This paper introduces a nonparametric regression approach for univariate and multivariate skewed responses using Bayesian additive regression trees (BART). Existing BART methods use ensembles of decision trees to model a mean function, and have become popular recently due to their high prediction accuracy and ease of use. The usual assumption of a univariate Gaussian error distribution, however, is restrictive in many biomedical applications. Motivated by an oral health study, we provide a useful extension of BART, the skewBART model, to address this problem. We then extend skewBART to allow for multivariate responses, with information shared across the decision trees associated with different responses within the same subject. The methodology accommodates within-subject association, and allows varying skewness parameters for the varying multivariate responses. We illustrate the benefits of our multivariate skewBART proposal over existing alternatives via simulation studies and application to the oral health dataset with bivariate highly skewed responses. Our methodology is implementable via the R package skewBART, available on GitHub.

Mediation analysis using Bayesian tree ensembles.

We present a general framework for causal mediation analysis using nonparametric Bayesian methods in the potential outcomes framework. Our model, which we refer to as the Bayesian causal mediation forests model, combines recent advances in Bayesian machine learning using decision tree ensembles, Bayesian nonparametric causal inference, and a Bayesian implementation of the g-formula for computing causal effects. Because of its strong performance on simulated data and because it greatly reduces researcher degrees of freedom, we argue that Bayesian causal mediation forests are highly attractive as a default approach. Of independent interest, we also introduce a new sensitivity analysis technique for mediation analysis with continuous outcomes that is widely applicable. We demonstrate our approach on both simulated and real data sets, and show that our approach obtains low mean squared error and close to nominal coverage of 95% interval estimates, even in highly nonlinear problems on which other methods fail. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Semiparametric analysis of clustered interval-censored survival data using soft Bayesian additive regression trees (SBART).

Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a robust semiparametric model for clustered interval-censored survival data under a paradigm of Bayesian ensemble learning, called soft Bayesian additive regression trees or SBART (Linero and Yang, 2018), which combines multiple sparse (soft) decision trees to attain excellent predictive accuracy. We develop a novel semiparametric hazards regression model by modeling the hazard function as a product of a parametric baseline hazard function and a nonparametric component that uses SBART to incorporate clustering, unknown functional forms of the main effects, and interaction effects of various covariates. In addition to being applicable for left-censored, right-censored, and interval-censored survival data, our methodology is implemented using a data augmentation scheme which allows for existing Bayesian backfitting algorithms to be used. We illustrate the practical implementation and advantages of our method via simulation studies and an analysis of a prostate cancer surgery study where dependence on the experience and skill level of the physicians leads to clustering of survival times. We conclude by discussing our method's applicability in studies involving high-dimensional data with complex underlying associations.

Simulation-based estimators of analytically intractable causal effects.

In causal inference problems, one is often tasked with estimating causal effects which are analytically intractable functionals of the data-generating mechanism. Relevant settings include estimating intention-to-treat effects in longitudinal problems with missing data or computing direct and indirect effects in mediation analysis. One approach to computing these effects is to use the g-formula implemented via Monte Carlo integration; when simulation-based methods such as the nonparametric bootstrap or Markov chain Monte Carlo are used for inference, Monte Carlo integration must be nested within an already computationally intensive algorithm. We develop a widely-applicable approach to accelerating this Monte Carlo integration step which greatly reduces the computational burden of existing g-computation algorithms. We refer to our method as accelerated g-computation (AGC). The algorithms we present are similar in spirit to multiple imputation, but require removing within-imputation variance from the standard error rather than adding it. We illustrate the use of AGC on a mediation analysis problem using a beta regression model and in a longitudinal clinical trial subject to nonignorable missingness using a Bayesian additive regression trees model.

Semiparametric mixed-scale models using shared Bayesian forests.

This paper demonstrates the advantages of sharing information about unknown features of covariates across multiple model components in various nonparametric regression problems including multivariate, heteroscedastic, and semicontinuous responses. In this paper, we present a methodology which allows for information to be shared nonparametrically across various model components using Bayesian sum-of-tree models. Our simulation results demonstrate that sharing of information across related model components is often very beneficial, particularly in sparse high-dimensional problems in which variable selection must be conducted. We illustrate our methodology by analyzing medical expenditure data from the Medical Expenditure Panel Survey (MEPS). To facilitate the Bayesian nonparametric regression analysis, we develop two novel models for analyzing the MEPS data using Bayesian additive regression trees-a heteroskedastic log-normal hurdle model with a "shrink-toward-homoskedasticity" prior and a gamma hurdle model.

Bayesian Approaches for Missing Not at Random Outcome Data: The Role of Identifying Restrictions.

Missing data is almost always present in real datasets, and introduces several statistical issues. One fundamental issue is that, in the absence of strong uncheckable assumptions, effects of interest are typically not nonparametrically identified. In this article, we review the generic approach of the use of identifying restrictions from a likelihood-based perspective, and provide points of contact for several recently proposed methods. An emphasis of this review is on restrictions for nonmonotone missingness, a subject that has been treated sparingly in the literature. We also present a general, fully-Bayesian, approach which is widely applicable and capable of handling a variety of identifying restrictions in a uniform manner.

A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies with Nonignorable Missingness with Application to an Acute Schizophrenia Clinical Trial.

We develop a Bayesian nonparametric model for a longitudinal response in the presence of nonignorable missing data. Our general approach is to first specify a working model that flexibly models the missingness and full outcome processes jointly. We specify a Dirichlet process mixture of missing at random (MAR) models as a prior on the joint distribution of the working model. This aspect of the model governs the fit of the observed data by modeling the observed data distribution as the marginalization over the missing data in the working model. We then separately specify the conditional distribution of the missing data given the observed data and dropout. This approach allows us to identify the distribution of the missing data using identifying restrictions as a starting point. We propose a framework for introducing sensitivity parameters, allowing us to vary the untestable assumptions about the missing data mechanism smoothly. Informative priors on the space of missing data assumptions can be specified to combine inferences under many different assumptions into a final inference and accurately characterize uncertainty. These methods are motivated by, and applied to, data from a clinical trial assessing the efficacy of a new treatment for acute Schizophrenia.