# Causal effects of time-varying treatments on recurrent event outcomes

> **NIH NIH P50** · CHILDREN'S HOSP OF PHILADELPHIA · 2022 · $123,379

## Abstract

PROJECT SUMMARY
Pragmatic trials and comparative effectiveness research often require causal inference methods to establish
cause-effect relationships between interventions and outcomes. With treatments that can change over time, it
is crucial to account for time-dependent confounders to accurately estimate treatment effects. Statistical
methods to do so have not been developed for recurrent time-to-event outcomes, although such outcomes are
often observed in children with chronic kidney disease. This project aims to fill this gap by developing a novel
class of statistical methods to estimate the effects of time-varying treatments on recurrent event outcomes. A
marginal structural model approach will be applied to the proportional rates model, conditional gap time model,
and conditional frailty model to estimate both recurrent event rates and risks of subsequent outcomes after the
first outcome event. This project will develop theoretical properties of each model and test its performance
using Monte Carlo simulation studies. The new models will then be applied to real data from observational
cohort studies and electronic health record databases to answer important clinical questions for children with
kidney diseases for the first time. Specifically, this project will enable estimation of the effect of time-varying
renin-angiotensin-aldosterone system inhibitor dose on the rate of proteinuria remissions among children
enrolled in the Chronic Kidney Disease in Children (CKiD) study; the effect of time-varying corticosteroid use
on time from one infection-related acute care event to the next event among patients with glomerular disease
in the Cure Glomerulonephropathy (CureGN) study; and the effect of time-varying calcium-based and non-
calcium-based phosphate binder use on the time from one skeletal fracture to the next fracture among a
heterogeneous population of children with chronic kidney disease in PEDSnet. User-friendly statistical software
and associated documentation for implementation of the models will be developed to facilitate their use in a
wide range of applications. The tools established by this project will open many new avenues of study for
analyzing longitudinal data from observational studies, electronic health record databases, and pragmatic
trials. The accurate and precise estimation of time-varying treatment effects on recurrent outcomes will inform
improvements in clinical care for children with kidney diseases.

## Key facts

- **NIH application ID:** 10529736
- **Project number:** 2P50DK114786-06
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** JARCY ZEE
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $123,379
- **Award type:** 2
- **Project period:** 2017-09-18 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10529736

## Citation

> US National Institutes of Health, RePORTER application 10529736, Causal effects of time-varying treatments on recurrent event outcomes (2P50DK114786-06). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10529736. Licensed CC0.

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