Many therapies are available for the treatment of relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) after ≥ 2 lines of therapy, albeit with scant evidence on the comparative effectiveness of these therapies. This study used inverse probability of treatment weighting to indirectly compare treatment outcomes of epcoritamab from the EPCORE NHL-1 trial with individual patient data from clinical practice cohorts treated with chemoimmunotherapy (CIT) and novel therapies (polatuzumab-based regimens, tafasitamab-based regimens, and chimeric antigen receptor T-cell [CAR T] therapies) for third-line or later R/R large B-cell lymphoma (LBCL) and DLBCL. In this analysis, epcoritamab demonstrated significantly better response rates and overall survival rates than CIT, polatuzumab-based regimens, and tafasitamab-based regimens. No statistically significant differences in response rates or survival were found for epcoritamab compared with CAR T in R/R LBCL.

Distracted driving is a threat to traffic safety that can result in more traffic crashes. Although previous studies have been conducted to explore the relationship between driver distraction and hazardous driving actions, few studies are available to identify the causation between them. Thus, the study intended to evaluate the causal effects of distraction on hazardous driving actions at intersections based on the crash data extracted from the Crash Report Sampling System (2021-2022). The multinomial logit model was employed to reveal the factors contributing to driver distraction. Then, propensity score weighting was adopted to balance the factor distributions between distraction and non-distraction cases to identify the causal effects on hazardous actions. Results indicated that 1) the propensity of distraction is relevant to factors such as the driver\'s age, gender, vehicle type, speed limit, area, weather, and light condition, 2) driver distraction can significantly increase the probability of risky actions including speeding, running red lights, failing to obey stop signs, failing to yield, following too closely, and 3) the causal effects show great diversity for different distraction types. The findings serve to understand the influence mechanism of distraction on specific crash risks and develop countermeasures to reduce distraction and hazardous driving actions.

BACKGROUND: Korea\'s child meal support program (CMSP) aims to reduce food and nutrition insecurity (FNI) and improve health among children from low-income households.
OBJECTIVE: We examined the impact of different types of CMSP on children\'s FNI and health in Korea, analyzing meal frequency and healthful food consumption (FNI), and general health and depression (health) among child meal card (CMC) and facility meal service (FMS) participants compared with nonparticipants.
METHODS: The 2018 Comprehensive Survey on Korean Children data were analyzed. Precisely, 847 children from low-income households aged 9-17 were categorized into CMC (n = 331), FMS (n = 209), and income-eligible nonparticipants (n = 307). Propensity score-weighted generalized linear models assessed CMSP\'s impact on FNI and health. Stratified generalized linear models examined heterogeneity in FNI-health associations by CMSP status.
RESULTS: CMC participants reported more frequent breakfast consumption (odds ratio [OR] = 0.662, p < 0.05) but poorer self-rated general health (OR = 1.890, p < 0.05); FMS participants were less likely to have three meals (OR = 1.814, p < 0.05), fruits and vegetables (OR = 2.194, p < 0.001), and protein-rich foods daily (OR = 1.695, p < 0.05) than nonparticipants. Health risks associated with healthful food consumption and meal frequency were more pronounced among CMC and FMS/nonparticipants, respectively.
CONCLUSIONS: CMSP had a limited impact on reducing FNI and improving health among children from low-income households. CMC appeared more effective than FMS in alleviating FNI, notwithstanding potential health concerns. Food assistance programs should seek comprehensive enhancements in children\'s food and nutrition security and health.

How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon\'s entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators. We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population. Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption.

While empirical studies have observed that homeownership is associated with improved mental health conditions, research indicates that this relationship might vary by race. Moreover, such a White-Black disparity in the impacts of homeownership on mental health could be complexed by poverty status, as maintaining one\'s homeownership could be a financial burden for people living in poverty status, defined by the US official poverty threshold. We add to the existing literature by analyzing the impacts of homeownership on psychological distress, simultaneously disaggregating by race and poverty status using survey data from the Panel Study on Income Dynamics from the 2017 and 2019 waves (N = 7059). Propensity score weighting and doubly robust estimation are applied to estimate causal inference for the impact of 2017 homeownership on 2019 psychological distress using negative binomial models. First, we found the impacts of homeownership on reducing psychological distress are significant for White Americans, not for Black Americans. Second, we found such a White-Black disparity is only observable for populations not living in poverty. On the other hand, for populations living in poverty, homeownership no longer lowers psychological distress for either race. Findings suggest that financial support and mental health support are needy to address inequality in the impacts of homeownership on mental health, which could simultaneously vary by poverty status and race. Implications are discussed.

Propensity score (PS) analyses are increasingly popular in behavioral sciences. Two issues often add complexities to PS analyses, including missing data in observed covariates and clustered data structure. In previous research, methods for conducting PS analyses with considering either issue alone were examined. In practice, the two issues often co-occur; but the performance of methods for PS analyses in the presence of both issues has not been evaluated previously. In this study, we consider PS weighting analysis when data are clustered and observed covariates have missing values. A simulation study is conducted to evaluate the performance of different missing data handling methods (complete-case, single-level imputation, or multilevel imputation) combined with different multilevel PS weighting methods (fixed- or random-effects PS models, inverse-propensity-weighting or the clustered weighting, weighted single-level or multilevel outcome models). The results suggest that the bias in average treatment effect estimation can be reduced, by better accounting for clustering in both the missing data handling stage (such as with the multilevel imputation) and the PS analysis stage (such as with the fixed-effects PS model, clustered weighting, and weighted multilevel outcome model). A real-data example is provided for illustration.

BACKGROUND: Studies on the association between vegetarian diets and nonalcoholic fatty liver disease (NAFLD) are limited and have inconsistent results. This study aims to explore the association between vegetarian diets and NAFLD and compare the stage of fibrosis between vegetarians and nonvegetarians in a US representative sample.
METHODS: Cross-sectional data from 23,130 participants aged ≥20 years were obtained from the National Health and Nutrition Examination Survey, 2005-2018. Vegetarian status was classified based on two 24-h dietary recalls. We examined the association between vegetarian diets and the risk of NAFLD using the propensity score weighting method.
RESULTS: Vegetarian diets were significantly associated with decreases in hepatic steatosis index (HSI), US fatty liver index and nonalcoholic fatty liver disease fibrosis score with mean differences of -2.70 (95% confidence interval [CI]: -3.69, -1.70), -3.03 (95% CI: -7.15, -0.91) and -0.12 (95% CI: -0.26, -0.01), respectively. While modelling the risk of NAFLD, we estimated that vegetarians were 53% less likely to have NAFLD assessed by HSI (odds ratios [OR]: 0.47; 95% CI: 0.34, 0.65). The effect of vegetarian diets was higher among individuals with lower waist circumferences (OR: 0.20) than among those with higher waist circumferences (OR: 0.53, p interaction ${p}_{\\text{interaction}}\\,$ = 0.004). However, the association was largely attenuated after adjusting for body mass index and diabetes status. No significant association was identified between vegetarian diets and advanced fibrosis.
CONCLUSIONS: Vegetarian diets were associated with a lower prevalence of NAFLD among US adults, and the association appeared to be stronger in people with lower waist circumferences. Further studies are warranted to replicate our findings.

Nearly 300,000 older adults experience a hip fracture every year, the majority of which occur following a fall. Unfortunately, recovery after fall-related trauma such as hip fracture is poor, where older adults diagnosed with Alzheimer\'s disease and related dementia (ADRD) spend a particularly long time in hospitals or rehabilitation facilities during the post-operative recuperation period. Because older adults value functional recovery and spending time at home versus facilities as key outcomes after hospitalization, identifying factors that influence days spent at home after hospitalization is imperative. While several individual-level factors have been identified, the characteristics of the treating hospital have recently been identified as contributors. However, few methodological rigorous approaches are available to help overcome potential sources of bias such as hospital-level unmeasured confounders, informative hospital size, and loss to follow-up due to death. This article develops a useful tool equipped with unsupervised learning to simultaneously handle statistical complexities that are often encountered in health services research, especially when using large administrative claims databases. The proposed estimator has a closed form, thus only requiring light computation load in a large-scale study. We further develop its asymptotic properties with stabilized inference assisted by unsupervised clustering. Extensive simulation studies demonstrate superiority of the proposed estimator compared to existing estimators.

When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when using inverse probability-weighted Cox regression to estimate overall hazard ratio. However, when there is unknown heterogeneity of covariates in different sites, either approach may lead to potential bias or reduced efficiency. In this study, we proposed a method to estimate propensity score based on covariate balance-related criterion and estimate the overall hazard ratio while overcoming data sharing constraints across sites.
The proposed propensity score was generated by choosing between global and local propensity score based on covariate balance-related criterion, combining the global propensity score fitted in the entire population and the local propensity score fitted within each site. We used this proposed propensity score to estimate overall hazard ratio of distributed survival data with multiple sites, while requiring only the summary-level information across sites. We conducted simulation studies to evaluate the performance of the proposed method. Besides, we applied the proposed method to real-world data to examine the effect of radiation therapy on time to death among breast cancer patients.
The simulation studies showed that the proposed method improved the performance in estimating overall hazard ratio comparing with global and local propensity score method, regardless of the number of sites and sample size in each site. Similar results were observed under both homogeneous and heterogeneous settings. Besides, the proposed method yielded identical results to the pooled individual-level data analysis. The real-world data analysis indicated that the proposed method was more likely to find a significant effect of radiation therapy on mortality compared to the global propensity score method and local propensity score method.
The proposed covariate balance-related propensity score in multi-site distributed survival data outperformed the global propensity score estimated using data from the entire population or the local propensity score estimated within each site in estimating the overall hazard ratio. The proposed approach can be performed without individual-level data transfer between sites and would yield the same results as the corresponding pooled individual-level data analysis.

OBJECTIVE: To assess key birth outcomes in an alternative maternity care model, midwifery-based birth center care.
METHODS: The American Association of Birth Centers Perinatal Data Registry and birth certificate files, using national data collected from 2009 to 2019.
METHODS: This observational cohort study compared key clinical birth outcomes of women at low risk for perinatal complications, comparing those who received care in the midwifery-based birth center model versus hospital-based usual care. Linear regression analysis was used to assess key clinical outcomes in the midwifery-based group as compared with hospital-based usual care. The hospital-based group was selected using nearest neighbor matching, and the primary linear regressions were weighted using propensity score weights (PSWs). The key clinical outcomes considered were cesarean delivery, low birth weight, neonatal intensive care unit admission, breastfeeding, and neonatal death. We performed sensitivity analyses using inverse probability weights and entropy balancing weights. We also assessed the remaining role of omitted variable bias using a bounding methodology.
METHODS: Women aged 16-45 with low-risk pregnancies, defined as a singleton fetus and no record of hypertension or cesarean section, were included. The sample was selected for records that overlapped in each year and state. Counties were included if there were at least 50 midwifery-based birth center births and 300 total births. After matching, the sample size of the birth center cohort was 85,842 and the hospital-based cohort was 261,439.
RESULTS: Women receiving midwifery-based birth center care experienced lower rates of cesarean section (-12.2 percentage points, p < 0.001), low birth weight (-3.2 percentage points, p < 0.001), NICU admission (-5.5 percentage points, p < 0.001), neonatal death (-0.1 percentage points, p < 0.001), and higher rates of breastfeeding (9.3 percentage points, p < 0.001).
CONCLUSIONS: This analysis supports midwifery-based birth center care as a high-quality model that delivers optimal outcomes for low-risk maternal/newborn dyads.