# Novel causal inference methods to inform clinical decision on when to discontinue symptomatic treatment for patients with dementia > **NIH NIH R03** · HARVARD PILGRIM HEALTH CARE, INC. · 2022 · $166,000 ## Abstract PROJECT SUMMARY/ABSTRACT Appropriate use of acetylcholinesterase (AChEIs) and memantine can meaningfully improve the health outcomes and quality of life among people with Alzheimer’s disease-related dementia (ADRD). Deprescribing of these symptomatic medications can help mitigate medication burden and associated adverse events in this population, particularly given the high level of multimorbidity and pill burden. However, no current US guideline exists on deprescribing of these medications in ADRD. Existing non-US guideline recommendations are largely consensus-based and should be strengthened through higher levels of evidence. Two pivotal questions need to be answered first: 1) what is the long-term effect of symptomatic dementia medications? and 2) when is suitable to discontinue these medications? Ideally, answers to these questions would come from randomized controlled trials, but conducting trials evaluating multiple treatment duration or discontinuation strategies simultaneously with large enough sample sizes in each arm would be cost-prohibitive. Observational data from dementia medication use in the real-world setting provides a unique opportunity. However, treatment duration or discontinuation strategies necessarily involve interventions on time-varying treatment decisions. Evaluating the time-varying medication use on health and patient-centered outcomes must appropriately control for complex time-varying confounding that renders conventional regression invalid. Novel causal inference methods, including Robins’ g-formula and a three-step weighting approach (cloning, censoring, weighting) can appropriately account for such time-varying confounding and generate estimates of absolute risks while preventing immortal time bias. By emulating the valid analyses of trials, causal analyses of observational data are also cost-efficient and have greater generalizability. Using data collected in a large survey linked with electronic health databases, we will characterize the utilization pattern of symptomatic dementia medications and examine factors that influenced treatment discontinuation (Aim 1). We will then use novel causal inference methods to estimate the long-term effect of continuous treatment (Aim 2), and to evaluate different treatment discontinuation strategies (Aim 3) with regard to incidence of clinical and patient-centered outcomes and health service utilization. We will use data from the Health and Retirement Study (HRS)-Medicare linked dataset. The nationally representative, longitudinal, NIA-funded HRS survey provides validated measures on cognitive impairment and dementia. The linkage to Medicare provides extensive information on medication, clinical characteristics, and health care utilization. The expected outcome of this study is an understanding of the effects of long-term use of dementia medications and the impact of different treatment discontinuation strategies on outcomes. The findings of this study will provide a scient... ## Key facts - **NIH application ID:** 10322425 - **Project number:** 5R03AG070661-02 - **Recipient organization:** HARVARD PILGRIM HEALTH CARE, INC. - **Principal Investigator:** Xiaojuan Li - **Activity code:** R03 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $166,000 - **Award type:** 5 - **Project period:** 2021-01-01 → 2024-12-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10322425 ## Citation > US National Institutes of Health, RePORTER application 10322425, Novel causal inference methods to inform clinical decision on when to discontinue symptomatic treatment for patients with dementia (5R03AG070661-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10322425. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*