# HETEROGENEITY IN TREATMENT EFFECT TIMING IN GERIATRICS AND PALLIATIVE CARE STUDIES

> **NIH NIH R56** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $412,500

## Abstract

In real-world studies of geriatric palliative care programs, policies, or treatments, treatment initiation may need
to be staggered across units in ways that are outside of an investigator's 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. Current methods for accounting for treatment
effect timing heterogeneity either do not account for confounding or may introduce additional bias due to
regression to the mean. 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.
 We propose to evaluate the bias and efficiency of estimates in data generated from observational designs
with staggered data collection and/or staggered treatment timing. We aim to: 1) Compare the bias and
efficiency of estimated average treatment effects using VBKW, IPTW, and no adjustment in difference-in-
difference analyses of retrospective cohort studies with staggered treatment timing within a cohort, 2) Compare
the bias and efficiency of estimates using VBKW, IPTW, and no adjustment on data from longitudinal panel
studies where treatment effects may vary across and within waves of data collection, and 3) Identify the degree
of treatment effect heterogeneity required for VBKW and IPTW to lead to different inferences.
 We will use Monte Carlo (MC) and plasmode simulations to obtain estimates of average treatment effects
with VBKW, IPTW, or no adjustment of data obtained from observational studies with staggered data collection
and/or staggered treatment timing. MC simulations on investigator-generated data (n=900, 9600) will allow us
to examine the impact of different analytic scenarios (e.g., sample distribution across treatment timing groups,
number of distinct times in which treatment is initiated) on the relative performance of VBKW and IPTW
estimates. 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' participant-directed care services and days in
the community (n=848,500 person-months, 38 medical centers) and the Health and Retirement Study (HRS).
We will evaluate bias, efficiency, and covariate balance.
 We will identify when VBKW or IPTW is superior for estimating the effect of a treatment provided at different
times. We will summarize our results in practical guidance for investigators. Our results will improve
investigators' ability to generate ...

## Key facts

- **NIH application ID:** 10228270
- **Project number:** 1R56AG068178-01
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Melissa M Garrido
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $412,500
- **Award type:** 1
- **Project period:** 2020-09-17 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10228270

## Citation

> US National Institutes of Health, RePORTER application 10228270, HETEROGENEITY IN TREATMENT EFFECT TIMING IN GERIATRICS AND PALLIATIVE CARE STUDIES (1R56AG068178-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10228270. Licensed CC0.

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