Background/Aims: Secondary analyses of randomized clinical trials often seek to identify subgroups with differential treatment effects. These discoveries can help guide individual treatment decisions based on patient characteristics and identify populations for which additional treatments are needed. Traditional analyses require researchers to pre-specify potential subgroups to reduce the risk of reporting spurious results. There is a need for methods that can detect such subgroups without a priori specification while allowing researchers to control the probability of falsely detecting heterogeneous subgroups when treatment effects are uniform across the study population.Methods: We propose a permutation procedure for tuning parameter selection that allows for type I error control when testing for heterogeneous treatment effects framed within the Virtual Twins procedure for subgroup identification. We verify that the type I error rate can be controlled at the nominal rate and investigate the power for detecting heterogeneous effects when present through extensive simulation studies. We apply our method to a secondary analysis of data from a randomized trial of very low nicotine content cigarettes.Results: In the absence of type I error control, the observed type I error rate for Virtual Twins was between 99% and 100%. In contrast, models tuned via the proposed permutation were able to control the type I error rate and detect heterogeneous effects when present. An application of our approach to a recently completed trial of very low nicotine content cigarettes identified several variables with potentially heterogeneous treatment effects.Conclusions: The proposed permutation procedure allows researchers to engage in secondary analyses of clinical trials for treatment effect heterogeneity while maintaining the type I error rate without pre-specifying subgroups.

Background: Long‐term services and supports (LTSS) serve > 9 million adults in the United States. We examined prevalence and quality of life (QoL)‐related outcomes of consumer‐reported unmet LTSS needs in publicly‐funded LTSS. Methods: We pooled cross‐sectional data from the 2016–2017, 2017–2018, 2018–2019, 2021–2022, and 2022–2023 National Core Indicators–Aging and Disability Adult Consumer Surveys. We included 61,829 adults (34% with age < 65 years; 66% female; 27 states). Survey weights generated population‐representative estimates. We ascertained unmet LTSS needs by response to: “Do the long‐term care services you receive meet your current needs and goals?” (dichotomized‐ yes vs. no). We estimated weighted prevalence of unmet LTSS needs overall and across subpopulations, and examined associations with two self‐reported QoL‐related outcomes (dichotomized‐ yes/no): (1) being active in community; (2) satisfied with how one spends one's day. Prevalence ratios (PRs) were estimated using weighted Poisson regression with robust variance estimators, adjusting for demographics, health and functional status, multimorbidity, funding program, residence setting, state, and survey year. Results: Among respondents, 18,004 (weighted‐estimate, 29%) reported unmet LTSS needs. Prevalence of unmet needs varied across sociodemographic strata, care settings (higher in community‐based than in residential‐care settings), and funding programs (lower in PACE than in Medicaid programs). Individuals with unmet needs were significantly less likely to report being active in the community (PR, 0.65; 95% CI, 0.62–0.68) and satisfied with how they spend their day (PR, 0.62; 95% CI, 0.60–0.64). Post hoc analyses revealed potential dose–response associations between increasing degree of unmet LTSS needs and outcomes. Conclusions: Consumer‐reported unmet LTSS needs are frequent, vary greatly across care settings and funding programs, and are associated with poorer QoL‐related outcomes; highlighting substantial system‐level gaps in the fragmented LTSS landscape and the need for coordinated investments and structural reforms to better meet the needs of individuals relying on these services.

Access to home- and community-based services (HCBS) is critical for aging in place; yet many older adults continue to experience unmet needs. While individual-level factors are better-studied, less is known about how neighborhood-level place-based factors (PBFs, e.g., poverty, housing conditions, transportation, and internet access) shape access to and adequacy of HCBS. This study addresses that gap by examining the added explanatory value of PBFs in predicting HCBS use and unmet needs. We analyzed data from 6558 community-dwelling adults aged ≥ 65 years using the 2022–2023 National Core Indicators–Aging & Disability Adult Consumer Survey. Outcomes included use of six HCBS types, consumer-reported unmet needs for each type, and overall unmet HCBS needs. PBFs were measured at the ZIP code level using the 2016–2020 American Community Survey. Nested logistic regression models estimated incremental variance (McFadden’s R²) explained by PBFs, adjusting for individual demographics, health status, state, and proxy response. Adding PBFs increased explained variance by 7.98–22.70% for HCBS use, 35.92–48.00% for unmet needs by service type, and 51.85% for overall unmet HCBS needs. PBFs meaningfully influence both access to and adequacy of HCBS. Using PBFs to guide resource allocation and targeting modifiable PBFs could improve HCBS access and efficiency.