# Heterogeneity in Treatment Effect Timing in Geriatrics and Palliative Care Studies

> **NIH VA I01** · VA BOSTON HEALTH CARE SYSTEM · 2024 · —

## Abstract

Background: In real-world studies of geriatric and palliative care programs, policies, or treatments, treatment
initiation may need to be staggered across units in ways that are outside of investigators’ control. If differences
across cohorts or in organizational characteristics associated with both treatment timing and outcome are not
controlled for in analyses, they may obscure estimates of true treatment effects. Heterogeneous treatment
timing is inadequately addressed in most existing methods. Current methods to account for treatment effect
timing heterogeneity do not allow a treatment’s effect to be isolated from effects of confounders associated
with both timing and outcomes.
Significance: Staggered rollouts of policies and practice changes are common within VA — the timing at
which a new intervention (e.g., Veteran Directed Care) is rolled out cannot always be controlled.
Innovation and Impact: A potential solution involves inverse probability of treatment weights (IPTW) to adjust
for confounding across treatment groups defined by receipt timing, but IPTWs lead to biased estimates in
cross-sectional evaluations comparing multiple treatments. We have developed an alternative method, vector-
based kernel weighting (VBKW), that outperforms IPTW in cross-sectional evaluations. The degree to which
VBKW reduces bias and improves efficiency over IPTW in longitudinal applications has not yet been explored.
Entropy balancing (EB) weights also may produce unbiased and efficient treatment effect estimates when
combined with DD. Researchers need practical guidance for when weighting adjustments with DD may be a
superior analytic method to account for treatment timing heterogeneity in a difference-in-differences study, or
for when traditional two-way fixed effects (TWFE) approaches may be sufficient.
Specific Aims: We aim to compare bias and efficiency of estimates using VBKW, IPTW, EB, and TWFE
approaches in analyses of retrospective cohort studies with staggered treatment timing within a cohort (Aim 1)
and on data from longitudinal panel studies where treatment effects may vary across and within cohorts of data
collection (Aim 2). We will identify the degree of heterogeneity required for VBKW, IPTW, EB, and TWFE to
lead to different inferences (Aim 3).
Methodology: We will use Monte Carlo (MC) and plasmode simulations to evaluate bias, efficiency, covariate
balance, and processing time for each strategy in data obtained from observational studies with staggered
treatment timing. MC simulations on investigator-generated data (n=600, 900, 9600) will allow us to examine
the impact of different analytic scenarios (e.g., sample distribution across treatment timing groups, dynamic
effects) on the relative performance of estimators. Plasmode simulations will allow us to verify that our results
are robust to data generating process and will be derived from an observational analysis of Veterans’ self-
directed care services (using Corporate Data Warehouse dat...

## Key facts

- **NIH application ID:** 10752630
- **Project number:** 5I01HX003602-02
- **Recipient organization:** VA BOSTON HEALTH CARE SYSTEM
- **Principal Investigator:** Melissa M Garrido
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2022-11-01 → 2026-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10752630

## Citation

> US National Institutes of Health, RePORTER application 10752630, Heterogeneity in Treatment Effect Timing in Geriatrics and Palliative Care Studies (5I01HX003602-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10752630. Licensed CC0.

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