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.

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.

A ketogenic diet has been proposed as a potential supportive therapy for cancer patients, although its long-term influence on survival rates remain controversial. In our previous report, we presented promising results for 37 of 55 patients with advanced cancer enrolled between 2013 and 2018 who remained on a ketogenic diet for at least 3 months. We followed all 55 patients until March 2023 and analyzed the data up to March 2022. For the 37 patients with previously reported promising results, the median follow-up period was 25 (range of 3-104) months and 28 patients died. The median overall survival (OS) in this subset of 37 patients was 25.1 months and the 5-year survival rate was 23.9%. We also evaluated the association between the duration of the ketogenic diet and outcome in all 55 patients, except for 2 patients with insufficient data. The patients were divided into two groups: those who followed the diet for ≥12 months (n = 21) and those who followed it for <12 months (n = 32). The median duration of the ketogenic diet was 37 (range of 12-99) months for the ≥12 months group and 3 (range of 0-11) months for the <12 months group. During the follow-up period, 41 patients died (10/21 in the ≥12 months group and 31/32 in the <12 months group). The median OS was 19.9 months (55.1 months in the ≥12 months group and 12 months in the <12 months group). Following the inverse probability of treatment weighting to align the background factors of the two groups and make them comparable, the adjusted log-rank test showed a significantly better OS rate in the group that continued the ketogenic diet for a longer period (p < 0.001, adjusted log-rank test). These results indicate that a longer continuation of the ketogenic diet improved the prognosis of advanced cancer patients.

The original concerns about the consequences of adolescent fertility assumed that pregnancy is a turning point, which altered teens\' life trajectories in terms of school progress, human capital accumulation and labor force participation, placing them on a path of vulnerability. However, several years of research have shown that teenagers who become pregnant are not a random sample of the population, but a selective sample, more likely to have limited socioeconomic resources and other characteristics that made them a vulnerable group to begin with. This paper studies the association between adolescent fertility and high school dropout in Chile taking that selectivity in consideration. We analyze the dropout of teen men and women, considering their socioeconomic status, sociodemographic characteristics, and characteristics of their sexual debut. Data comes the VIII Chilean Survey of the Youth, a nationally representative survey of people 15-29 years old applied in 2015. In order to deal with selectivity issues, we use a combination of propensity score weighting techniques and adjusted generalized linear models for estimating the effect of teen parenthood on high school dropout (ATT), for men and women separately. Our best estimates of the effect of teen parenting on the probability of high school dropout is 16-18 percent for women and ten percent for men, which implies that the educational setback of parenthood for women is about twice as high as the setback of men. These findings suggest the need of policies and interventions aimed both to reduce adolescent fertility, but also to facilitate the high school completion of those who already are parents.

UNASSIGNED: It has been believed that breast-conserving therapy (lumpectomy plus adjuvant radiation, Lum + RT) and mastectomy without radiation (Mast + NoRT) have equivalent survival outcomes. However, there is a need to re-evaluate the role of lumpectomy plus adjuvant radiation due to changed breast cancer management over time. This study aimed to conduct a population-based study that compare long-term oncologic survival outcomes after Lum + RT vs Mast + NoRT.
UNASSIGNED: The Surveillance, Epidemiology and End Results database was used to identify female breast cancer patients with a primary localized breast cancer diagnosis from 1988 to 2018. The standardized incidence/mortality ratio (SIR/SMR) for breast cancer recurrence (BCR) and breast cancer-specific death (BSD) was estimated by the SEER*Stat program. Cumulative incidences of BCR and BSD were assessed using Gray\'s method. We evaluated the effects of Lum + RT vs. Mast + NoRT on breast cancer recurrence-free survival (BRFS) and breast cancer-specific survival (BCSS). Fine-Gray competing risk model analyses, propensity score-adjusted Kaplan-Meier analyses and Cox proportional hazards model analyses were applied.
UNASSIGNED: A total of 205,788 women were included in the study. Patients who underwent Lum + RT had higher SIR of BCR (4.14 [95% confidence interval, CI: 3.94-4.34] vs. 1.11 [95% CI: 1.07-1.14]) and lower SMR (9.89 [95% CI: 9.71-10.08] vs. 17.07 [95% CI: 16.82-17.33]) than patients who underwent Mast + NoRT. Lum + RT was associated with higher competing risk of BCR (adjusted hazard ratio [HR]: 1.996, 95% CI: 1.925-2.069, p < 0.001) and lower competing risk of BSD when compared to Mast + RT (adjusted HR: 0.584, 95% CI: 0.572-0.597, p < 0.001). Multivariate Cox regression analysis revealed similar results (adjusted HR after PSW for BRFS: 1.792, 95% CI 1.716-1.871, p < 0.001; adjusted HR after PSW for BCSS: 0.706, 95% CI 0.688-0.725, p < 0.001). These findings persisted in the sensitivity and subgroup analyses.
UNASSIGNED: The present study further confirmed superior long-term survival with lumpectomy plus adjuvant radiation over mastectomy independent of patient characteristics including age, race, time period, historic subtype, tumor size, historic grade and stage, indicating that this benefit may result from the treatment itself.

There is growing interest in developing causal inference methods for multi-valued treatments with a focus on pairwise average treatment effects. Here we focus on a clinically important, yet less-studied estimand: causal drug-drug interactions (DDIs), which quantifies the degree to which the causal effect of drug A is altered by the presence versus the absence of drug B. Confounding adjustment when studying the effects of DDIs can be accomplished via inverse probability of treatment weighting (IPTW), a standard approach originally developed for binary treatments and later generalized to multi-valued treatments. However, this approach generally results in biased results when the propensity score model is misspecified. Motivated by the need for more robust techniques, we propose two empirical likelihood-based weighting approaches that allow for specifying a set of propensity score models, with the second method balancing user-specified covariates directly, by incorporating additional, nonparametric constraints. The resulting estimators from both methods are consistent when the postulated set of propensity score models contains a correct one; this property has been termed multiple robustness. In this paper, we derive two multiply-robust estimators of the causal DDI, and develop inference procedures. We then evaluate their finite sample performance through simulation. The results demonstrate that the proposed estimators outperform the standard IPTW method in terms of both robustness and efficiency. Finally, we apply the proposed methods to evaluate the impact of renin-angiotensin system inhibitors (RAS-I) on the comparative nephrotoxicity of nonsteroidal anti-inflammatory drugs (NSAID) and opioids, using data derived from electronic medical records from a large multi-hospital health system.

What is already known about the topic?Autistic youths increasingly enter universities. We know from existing research that autistic students are at risk of dropping out or studying delays. Using machine learning and historical information of students, researchers can predict the academic success of bachelor students. However, we know little about what kind of information can predict whether autistic students will succeed in their studies and how accurate these predictions will be.What does this article add?In this research, we developed predictive models for the academic success of 101 autistic bachelor students. We compared these models to 2,465 students with other health conditions and 25,077 students without health conditions. The research showed that the academic success of autistic students was predictable. Moreover, these predictions were more precise than predictions of the success of students without autism.For the success of the first bachelor year, concerns with aptitude and study choice were the most important predictors. Participation in pre-education and delays at the beginning of autistic students\' studies were the most influential predictors for second-year success and delays in the second and final year of their bachelor\'s program. In addition, academic performance in high school was the strongest predictor for degree completion in 3 years.Implications for practice, research, or policyThese insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students.

UNASSIGNED: Anemia is a worldwide common condition during pregnancy, conferring a number of health risks to mothers. However, very little is known about the association between severity of anemia and severe maternal outcomes. This study aimed to assess the association between severity of anemia during pregnancy and the risk of severe maternal outcomes.
UNASSIGNED: This retrospective cohort study was based on data from China\'s National Maternal Near Miss Surveillance System for the period 2017-2018, which included 18 hospitals in southern China. Pregnant women admitted for delivery were divided into 4 groups based on severity of anemia during pregnancy: no anemia, mild anemia, moderate anemia, and severe anemia groups. Severe maternal outcomes were a composite of life-threatening conditions (ie, organ dysfunction) as defined by the WHO criteria, occurring at any time after admission until discharge or death. Modified Poisson regression analyses and propensity score-weighted regression analyses were used to estimate the relative risks (RRs) and 95% confidence intervals (CIs) of severe maternal outcomes among women with anemia of varying severity during pregnancy.
UNASSIGNED: The incidence of severe maternal outcomes was 0.3% (417/138,556) in total, and the rates were 0.1% (85/99,755), 0.2% (30/18,502), 1.2% (234/19,697) and 11.3% (68/602) in no anemia, mild anemia, moderate anemia and severe anemia group, respectively. Compared with no anemia, the adjusted RR for severe maternal outcomes was 4.19 (95% CI, 3.20-5.50) in moderate anemia group and 22.12 (95% CI, 15.43-31.69) in severe anemia group; the weighted RR was 1.01 (95% CI, 1.01-1.01) in moderate anemia group and 1.11 (95% CI, 1.07-1.14) in severe anemia group.
UNASSIGNED: Moderate to severe anemia during pregnancy was independently associated with an increased risk of severe maternal outcomes. Maternal health care providers and pregnant women themselves should give more attention to the prevention and treatment of anemia during pregnancy, especially moderate to severe anemia.