# Advancing the design, analysis, and interpretation of acute respiratory distress syndrome trials using modern statistical tools

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $717,154

## Abstract

PROJECT SUMMARY/ABSTRACT
Acute respiratory distress syndrome (ARDS) is a common and devastating cause of acute respiratory failure.
There are 200,000 annual ARDS cases in the U.S. (2.5-5 million globally), which account for 60,000 deaths
and enormous physical, cognitive, and psychosocial morbidity among survivors. Yet, despite more than 200
randomized clinical trials (RCTs), only two interventions – low-tidal-volume ventilation and prone positioning –
have definitively improved outcomes using a traditional frequentist, null hypothesis, p-value-based trial design
and analysis. The research team contends that assessing data in this framework may overlook informative trial
data and delay or thwart the identification of promising therapies, especially when p-values fall just short of the
0.05 threshold, which has occurred in several major ARDS trials. As an alternative methodological approach to
maximize the clinical insight gained from RCTs, the team will reanalyze 29 international and NHLBI-funded
ARDS RCTs that enrolled more than 15,000 individuals using Bayesian causal inference and machine learning
methods they have developed and validated. Most therapies they will examine are either low-cost or easily
implemented practices and thus have the potential for high impact (e.g., ventilator settings, fluid management,
corticosteroids, statins, beta-agonists, vitamin D). In Aim 1, instead of using statistical significance, they will
quantify the probability of a beneficial treatment effect and its probable magnitude. That is, instead of using a
pre-specified p-value to determine whether an intervention has at least the hypothesized mortality benefit, they
will derive the probability that a given therapy is associated with clinically relevant absolute mortality reductions
of at least 2%, 4%, and 6%. They will examine each intervention with noninformative Bayesian ‘priors’ and then
with standardized and meta-analysis-derived priors to reduce subjectivity and interrogate clinical efficacy
across the spectrum of harm and benefit possibilities. In Aim 2, they will use Bayesian Additive Regression
Trees (BART) formulations they developed to understand which ARDS patient types are most likely to benefit
from, or be harmed by, a therapy, i.e., so-called ‘heterogeneity of treatment effect’ (HTE). Unlike prior HTE
research in ARDS, their approach does not focus on one-by-one, binary splits of characteristics but rather can
identify complex, multivariable, nonlinear treatment effect modification. Aim 2a will focus on mortality and
adverse events. Aim 2b will apply a novel BART variation to identify HTE in outcomes such as ventilator
duration or hospital stay whose observation is truncated by death. By estimating causal effects on these
outcomes among always-survivors, their new method avoids biases associated with prior approaches,
enabling accurate identification of clinically meaningful subgroups. Aim 3 focuses on developing and
disseminating free, cloud-based ...

## Key facts

- **NIH application ID:** 10851700
- **Project number:** 5R01HL168202-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Michael Oscar Harhay
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $717,154
- **Award type:** 5
- **Project period:** 2023-06-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10851700

## Citation

> US National Institutes of Health, RePORTER application 10851700, Advancing the design, analysis, and interpretation of acute respiratory distress syndrome trials using modern statistical tools (5R01HL168202-02). Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nih/10851700. Licensed CC0.

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