High-dimensional generalized median adaptive lasso with application to omics data.

Recently, there has been a growing interest in variable selection for causal inference within the context of high-dimensional data. However, when the outcome exhibits a skewed distribution, ensuring the accuracy of variable selection and causal effect estimation might be challenging. Here, we introduce the generalized median adaptive lasso (GMAL) for covariate selection to achieve an accurate estimation of causal effect even when the outcome follows skewed distributions. A distinctive feature of our proposed method is that we utilize a linear median regression model for constructing penalty weights, thereby maintaining the accuracy of variable selection and causal effect estimation even when the outcome presents extremely skewed distributions. Simulation results showed that our proposed method performs comparably to existing methods in variable selection when the outcome follows a symmetric distribution. Besides, the proposed method exhibited obvious superiority over the existing methods when the outcome follows a skewed distribution. Meanwhile, our proposed method consistently outperformed the existing methods in causal estimation, as indicated by smaller root-mean-square error. We also utilized the GMAL method on a deoxyribonucleic acid methylation dataset from the Alzheimer's disease (AD) neuroimaging initiative database to investigate the association between cerebrospinal fluid tau protein levels and the severity of AD.

Heterogeneity in the Association Between the Presence of Coronary Artery Calcium and Cardiovascular Events: A Machine-Learning Approach in the MESA Study.

BACKGROUND: Coronary artery calcium (CAC) has been widely recognized as an important predictor of cardiovascular disease (CVD). Given the finite resources, it is important to identify individuals who would receive the most benefit from detecting positive CAC by screening. However, the evidence is limited as to whether the burden of positive CAC on CVD differs by multidimensional individual characteristics. We sought to investigate the heterogeneity in the association between positive CAC and incident CVD. METHODS: This cohort study included adults from MESA (Multi-Ethnic Study of Atherosclerosis) ages ≥45 years and free of cardiovascular disease. After propensity score matching in a 1:1 ratio, we applied a machine learning causal forest model to (1) evaluate the heterogeneity in the association between positive CAC and incident CVD, and (2) predict the increase in CVD risk at 10-years when CAC>0 (versus CAC=0) at the individual level. We then compared the estimated increase in CVD risk when CAC>0 to the absolute 10-year atherosclerotic CVD (ASCVD) risk calculated by the 2013 American College of Cardiology/American Heart Association pooled cohort equations. RESULTS: Across 3328 adults in our propensity score-matched analysis, our causal forest model showed the heterogeneity in the association between CAC>0 and incident CVD. We found a dose-response relationship of the estimated increase in CVD risk when CAC>0 with higher 10-year ASCVD risk. Almost all individuals (2293 of 2428 [94.4%]) with borderline risk of ASCVD or higher showed ≥2.5% increase in CVD risk when CAC>0. Even among 900 adults with low ASCVD risk, 689 (69.2%) showed ≥2.5% increase in CVD risk when CAC>0; these individuals were more likely to be male, Hispanic, and have unfavorable CVD risk factors than others. CONCLUSIONS: The expected increases in CVD risk when CAC>0 were heterogeneous across individuals. Moreover, nearly 70% of people with low ASCVD risk showed a large increase in CVD risk when CAC>0, highlighting the need for CAC screening among such low-risk individuals. Future studies are needed to assess whether targeting individuals for CAC measurements based on not only the absolute ASCVD risk but also the expected increase in CVD risk when CAC>0 improves cardiovascular outcomes.

Treatment Modalities and Outcomes in Brainstem Cavernous Malformations: A Large Multicenter Observational Cohort Study.

BACKGROUND: Symptomatic brainstem cavernous malformations (BSCMs) pose a high risk of morbidity and mortality due to recurrent hemorrhage, warranting aggressive management. However, few studies have compared the effectiveness of different treatment modalities for BSCMs. We aimed to assess the association of treatment modalities with recurrent hemorrhage and neurological outcomes in patients with BSCM. METHODS: We conducted a retrospective cohort study using an observational registry database covering population of southwest and southeast China. Adult patients with BSCM were included and followed up between March 1, 2011, to March 31, 2023. We compared outcomes between microsurgery and stereotactic radiosurgery (SRS) in propensity score-matched case pairs, incorporating demographic, medical history, and lesion characteristics. The outcomes studied included recurrent hemorrhage and poor prognosis (defined as a Glasgow Outcome Scale score, <4). Absolute rate differences and hazard ratios (HRs) with 95% CIs were calculated using Cox models. RESULTS: Among 736 diagnosed patients with BSCM, 96 (48 matched pairs) were included after exclusions and propensity score matching (mean age, 43.1 [SD, 12.1] years; 50% women). During the median 5-year follow-up, no significant differences in recurrent hemorrhage (4.2% [microsurgery] versus 14.6% [SRS], HR, 3.90 [95% CI, 0.46-32.65]; P=0.21) and poor prognosis (12.5% [microsurgery] versus 8.3% [SRS], HR, 0.29 [95% CI, 0.08-1.08]; P=0.07) were observed between microsurgery and SRS recipients. Furthermore, either microsurgery or SRS correlated with fewer recurrent hemorrhage (HR, 0.09 [95% CI, 0.02-0.39]; P=0.001; HR, 0.21 [95% CI, 0.07-0.69]; P=0.01) compared with conservative treatment. CONCLUSIONS: In this study, both microsurgery and SRS were safe and effective for BSCM, demonstrated comparable outcomes in recurrent hemorrhage and poor prognosis. However, interpretation should be cautious due to the potential for residual confounding. REGISTRATION: URL: https://www.chictr.org.cn/; Unique identifier: ChiCTR2300070907.

The Role of Multiple Birth and Birth Complications in the Association Between Assisted Reproductive Technology Conception and Autism Diagnosis.

In recent decades, the use of assisted reproductive technology (ART) has increased rapidly. To assess the relationship between ART and autism diagnosis, we linked California birth records from 2000 through 2016 with contemporaneous records from the National ART Surveillance System (NASS) and autism caseload records from California's Department of Developmental Services from 2000 through November 2019. All 95,149 birth records that were successfully linked to a NASS record, indicating an ART birth, were matched 1:1 using propensity scores to non-ART births. We calculated the hazard risk ratio (HRR) for autism diagnosis and the proportions of the relationship between ART conception and autism diagnosis mediated by multiple birth pregnancy and related birth complications. The HRR for autism diagnosis following ART compared with non-ART conception is 1.26 (95% CI, 1.17-1.35). Multiple birth, preterm birth, and Cesarean delivery jointly mediate 77.9% of the relationship between ART conception and autism diagnosis. Thus, increased use of single embryo transfer in the United States to reduce multiple births and related birth complications may be a strategy to address the risk of autism diagnosis among ART-conceived children.

Multiple imputation for propensity score analysis with covariates missing at random: some clarity on within and across methods.

In epidemiology and social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imputation for propensity score analysis is not completely clear. This paper aims to bring clarity on the consistency (or lack thereof) of methods that have been proposed, focusing on the within approach (where the effect is estimated separately in each imputed dataset and then the multiple estimates are combined) and the across approach (where typically propensity scores are averaged across imputed datasets before being used for effect estimation). We show that the within method is valid and can be used with any causal effect estimator that is consistent in the full-data setting. Existing across methods are inconsistent, but a different across method that averages the inverse probability weights across imputed datasets is consistent for propensity score weighting. We also comment on methods that rely on imputing a function of the missing covariate rather than the covariate itself, including imputation of the propensity score and of the probability weight. Based on consistency results and practical flexibility, we recommend generally using the standard within method. Throughout, we provide intuition to make the results meaningful to the broad audience of applied researchers.

Invited Commentary: Mixing multiple imputation and bootstrapping for variance estimation.

In 2023, Martinez et al. examined trends in the inclusion, conceptualization, operationalization and analysis of race and ethnicity among studies published in US epidemiology journals. Based on a random sample of papers (N=1,050) published from 1995-2018, the authors describe the treatment of race, ethnicity, and ethnorace in the analytic sample (N=414, 39% of baseline sample) over time. Between 32% and 19% of studies in each time stratum lacked race data; 61% to 34% lacked ethnicity data. The review supplies stark evidence of the routine omission and variability of measures of race and ethnicity in epidemiologic research. Informed by public health critical race praxis (PHCRP), this commentary discusses the implications of four problems the findings suggest pervade epidemiology: 1) a general lack of clarity about what race and ethnicity are; 2) the limited use of critical race or other theory; 3) an ironic lack of rigor in measuring race and ethnicity; and, 4) the ordinariness of racism and white supremacy in epidemiology. The identified practices reflect neither current publication guidelines nor the state of the knowledge on race, ethnicity and racism; therefore, we conclude by offering recommendations to move epidemiology toward more rigorous research in an increasingly diverse society.

Targeted Learning with an Undersmoothed Lasso Propensity Score Model for Large-Scale Covariate Adjustment in Healthcare Database Studies.

Lasso regression is widely used for large-scale propensity score (PS) estimation in healthcare database studies. In these settings, previous work has shown that undersmoothing (overfitting) Lasso PS models can improve confounding control, but it can also cause problems of non-overlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale Lasso PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap. Here, we used simulations to evaluate the performance of using collaborative-controlled targeted learning to data-adaptively select the degree of undersmoothing when fitting large-scale PS models within both singly and doubly robust frameworks to reduce bias in causal estimators. Simulations showed that collaborative learning can data-adaptively select the degree of undersmoothing to reduce bias in estimated treatment effects. Results further showed that when fitting undersmoothed Lasso PS-models, the use of cross-fitting was important for avoiding non-overlap in covariate distributions and reducing bias in causal estimates.

Thiazide Diuretics and Risk of Colorectal Cancer: A Population-Based Cohort Study.

Evidence from clinical trials and observational studies on the association between thiazide diuretics and colorectal cancer risk is conflicting. We aimed to determine whether thiazide diuretics are associated with an increased colorectal cancer risk compared with dihydropyridine calcium channel blockers (dCCBs). A population-based, new-user cohort was assembled using the UK Clinical Practice Research Datalink. Between 1990-2018, we compared thiazide diuretic initiators with dCCB initiators and estimated hazard ratios (HR) with 95% confidence intervals (CIs) of colorectal cancer using Cox proportional hazard models. Models were weighted using standardized morbidity ratio weights generated from calendar time-specific propensity scores. The cohort included 377,760 thiazide diuretic initiators and 364,300 dCCB initiators, generating 3,619,883 person-years of follow-up. Compared with dCCBs, thiazide diuretics were not associated with colorectal cancer (weighted HR = 0.97, 95% CI: 0.90, 1.04). Secondary analyses yielded similar results, although an increased risk was observed among patients with inflammatory bowel disease (weighted HR = 2.45, 95% CI: 1.13, 5.35) and potentially polyps (weighted HR = 1.46, 95% CI: 0.93, 2.30). Compared with dCCBs, thiazide diuretics were not associated with an overall increased colorectal cancer risk. While these findings provide some reassurance, research is needed to corroborate the elevated risks observed among patients with inflammatory bowel disease and history of polyps.

Characterizing Imbalance in the Tails of the Propensity Score Distribution.

Understanding characteristics of patients with propensity scores in the tails of the propensity score (PS) distribution has relevance for inverse-probability-of-treatment-weighted and PS-based estimation in observational studies. Here we outline a method for identifying variables most responsible for extreme propensity scores. The approach is illustrated in 3 scenarios: 1) a plasmode simulation of adult patients in the National Ambulatory Medical Care Survey (2011-2015) and 2) timing of dexamethasone initiation and 3) timing of remdesivir initiation in patients hospitalized for coronavirus disease 2019 from February 2020 through January 2021. PS models were fitted using relevant baseline covariates, and tails of the PS distribution were defined using asymmetric first and 99th percentiles. After fitting of the PS model in each original data set, values of each key covariate were permuted and model-agnostic variable importance measures were examined. Visualization and variable importance techniques were helpful in identifying variables most responsible for extreme propensity scores and may help identify individual characteristics that might make patients inappropriate for inclusion in a study (e.g., off-label use). Subsetting or restricting the study sample based on variables identified using this approach may help investigators avoid the need for trimming or overlap weights in studies.

A Cautionary Note on Using Propensity Score Calibration to Control for Unmeasured Confounding Bias When the Surrogacy Assumption Is Absent.

Conventional propensity score methods encounter challenges when unmeasured confounding is present, as it becomes impossible to accurately estimate the gold-standard propensity score when data on certain confounders are unavailable. Propensity score calibration (PSC) addresses this issue by constructing a surrogate for the gold-standard propensity score under the surrogacy assumption. This assumption posits that the error-prone propensity score, based on observed confounders, is independent of the outcome when conditioned on the gold-standard propensity score and the exposure. However, this assumption implies that confounders cannot directly impact the outcome and that their effects on the outcome are solely mediated through the propensity score. This raises concerns regarding the applicability of PSC in practical settings where confounders can directly affect the outcome. While PSC aims to target a conditional treatment effect by conditioning on a subject's unobservable propensity score, the causal interest in the latter case lies in a conditional treatment effect conditioned on a subject's baseline characteristics. Our analysis reveals that PSC is generally biased unless the effects of confounders on the outcome and treatment are proportional to each other. Furthermore, we identify 2 sources of bias: 1) the noncollapsibility of effect measures, such as the odds ratio or hazard ratio and 2) residual confounding, as the calibrated propensity score may not possess the properties of a valid propensity score.

Comparing traditional and causal inference methodologies for evaluating impacts of long-term air pollution exposure on hospitalization with Alzheimer's disease and related dementias.

Alzheimer's disease and related dementias (ADRD) present a growing public health burden in the United States. One actionable risk factor for ADRD is air pollution: multiple studies have found associations between air pollution and exacerbation of ADRD. Our study builds on previous studies by applying modern statistical causal inference methodologies-generalized propensity score (GPS) weighting and matching-on a large, longitudinal dataset. We follow 50 million Medicare enrollees to investigate impacts of three air pollutants-fine particular matter (PM${}_{2.5}$), nitrogen dioxide (NO${}_2$), and summer ozone (O${}_3$)-on elderly patients' rate of first hospitalization with ADRD diagnosis. Similar to previous studies using traditional statistical models, our results found increased hospitalization risks due to increased PM${}_{2.5}$ and NO${}_2$ exposure, with less conclusive results for O${}_3$. In particular, our GPS weighting analysis finds IQR increases in PM${}_{2.5}$, NO${}_2$, or O${}_3$ exposure results in hazard ratios of 1.108 (95% CI: 1.097-1.119), 1.058 (1.049-1.067), or 1.045 (1.036-1.054), respectively. GPS matching results are similar for PM${}_{2.5}$ and NO${}_2$ with attenuated effects for O${}_3$. Our results strengthen arguments that long-term PM${}_{2.5}$ and NO${}_2$ exposure increases risk of hospitalization with ADRD diagnosis. Additionally, we highlight strengths and limitations of causal inference methodologies in observational studies with continuous treatments. Keywords: Alzheimer's disease and related dementias, air pollution, Medicare, causal inference, generalized propensity score.

A Propensity Score Approach and a Partitioned Approach for the Self-Controlled Case Series Design to Evaluate Safety of a 2-dose Vaccine Series: Application to myocarditis/pericarditis following mRNA COVID-19 vaccination.

The assumption that serious adverse events (SAEs) do not affect subsequent exposure might not hold when evaluating 2-dose vaccine safety through a self-controlled case series (SCCS) design. To address this, we developed: 1) propensity score SCCS (PS-SCCS) using a propensity score model involving SAEs during the risk interval after dose 1 (${R}_1\Big)$, and 2) partitioned SCCS (P-SCCS) estimating relative incidence (RI) separately for doses 1 and 2. In simulations, both provided unbiased RIs. Conversely, standard SCCS overestimated RI after dose 2. We applied these approaches to assess myocarditis/pericarditis risks after 2-dose mRNA COVID-19 vaccination in 12-39-year-olds. For BNT162b2, PS-SCCS yielded RIs of 1.85 (95% CI, 0.75-4.59) and 11.05 (95% CI, 6.53-18.68) 14 days after doses 1 and 2 respectively; standard SCCS provided similar RI after dose 1 and RI of 12.92 (95% CI, 7.56-22.09) after dose 2. For mRNA-1273, standard SCCS showed RIs of 1.96 (95% CI, 0.56-6.91) after dose 1 and 7.87 (95% CI, 3.33-18.57) after dose 2. As no mRNA-1273 recipients with SAEs during ${R}_1$ received dose 2, P-SCCS was used, yielding similar RI after dose 1 and RI of 6.48 (95% CI, 2.83-14.83) after dose 2. mRNA vaccines were associated with elevated myocarditis/pericarditis risks following dose 2 in 12-39-year-olds.

Proton Pump Inhibitors and Cyclin-Dependent Kinase 4/6 Inhibitors in Patients With Breast Cancer.

BACKGROUND: Proton pump inhibitors (PPIs) reduce the bioavailability of several anticancer drugs. The impact of PPIs co-administered with cyclin-dependent kinase 4 and 6 inhibitors is controversial. We aimed to clarify whether the concomitant use of PPIs impacts palbociclib and abemaciclib effectiveness in breast cancer treatment. PATIENTS AND METHODS: This multicenter, retrospective, observational study, conducted across 4 medical institutions in Japan, consecutively included patients with endocrine-resistant metastatic breast cancer, receiving palbociclib or abemaciclib between December 2017 and August 2022. Propensity score-matched analyses were performed. Treatment efficacy and safety with and without PPIs were compared. Progression-free survival and overall survival were estimated using the Kaplan-Meier method and compared using a log-rank test. A Cox proportional hazards model was used to estimate the hazard ratio. RESULTS: The study included 240 patients. After 1:1 matching, 112 patients were treated with and without PPIs. The median progression-free survival period was 1.2 years in the PPI group and 1.3 years in the non-PPI group (hazard ratio, 1.19; 95% CI, 0.70-2.02). The median overall survival period was 3.6 years in the PPI group, whereas it was not reached in the non-PPI group (hazard ratio, 1.23; 95% CI, 0.61-2.47). Consistent results were obtained for subgroups receiving palbociclib (n = 177) and abemaciclib (n = 63) without propensity score matching. Adverse event incidence and severity were similar in both groups. CONCLUSION: The effectiveness of cyclin-dependent kinase 4/6 inhibitors is unlikely to be affected by concomitant PPI use. Future prospective pharmacokinetic studies are warranted.

Overall survival from tebentafusp versus nivolumab plus ipilimumab in first-line metastatic uveal melanoma: a propensity score-weighted analysis.

BACKGROUND: Tebentafusp demonstrated a superior overall survival (OS) benefit [hazard ratio (HR) 0.51] compared to investigator's choice (82% pembrolizumab) in a randomized, phase III trial (IMCgp100-202; N = 378) in untreated metastatic uveal melanoma (mUM). The 1-year OS rates for tebentafusp and pembrolizumab were 73% and 59%, respectively. In the single-arm GEM1402 (N = 52), the 1-year OS rate for nivolumab plus ipilimumab (N+I) in mUM was 52%. Due to limitations in conducting randomized trials in mUM, we compared OS on tebentafusp or pembrolizumab (IMCgp100-202) to N+I (GEM1402) in untreated mUM using propensity scoring methods. PATIENTS AND METHODS: Analyses were adjusted using propensity score-based inverse probability of treatment weighting (IPTW), balancing age, sex, baseline lactate dehydrogenase (LDH), baseline alkaline phosphatase, disease location, Eastern Cooperative Oncology Group status, and time from primary diagnosis to metastasis. OS was assessed using IPT-weighted Kaplan-Meier and Cox proportional hazard models. Sensitivity analyses using alternative missing data and weights methods were conducted. RESULTS: The primary IPTW analysis included 240 of 252 patients randomized to tebentafusp from IMCgp100-202 and 45 of 52 N+I-treated patients from GEM-1402. Key baseline covariates, including LDH, were generally well balanced before weighting. The IPTW-adjusted OS favored tebentafusp, HR 0.52 [95% confidence interval (CI) 0.35-0.78]; 1-year OS was 73% for tebentafusp versus 50% for N+I. Sensitivity analyses showed consistent superior OS for tebentafusp with all IPTW HRs ≤0.61. IPTW analysis of pembrolizumab versus N+I showed no significant difference in OS (HR 0.72; 95% CI 0.50-1.06). CONCLUSIONS: Tebentafusp was previously shown to provide an OS benefit compared to checkpoint inhibitors or chemotherapy in untreated mUM. Propensity score analysis demonstrated a similar OS benefit for tebentafusp compared with N+I. These data further support tebentafusp as the standard of care in previously untreated human leukocyte antigen (HLA)-A∗02:01+ adult patients with mUM.

Real-world outcomes of nivolumab plus ipilimumab and pembrolizumab with platinum-based chemotherapy in advanced non-small cell lung cancer: a multicenter retrospective comparative study.

INTRODUCTION: Nivolumab plus ipilimumab with chemotherapy (NICT) and pembrolizumab with chemotherapy (PCT) are commonly used in patients with advanced non-small cell lung cancer (NSCLC). Compared with immune checkpoint inhibitor (ICI) monotherapy, ICI combination therapy can increase immune-related toxicity instead of prolonging survival. This study aimed to compare the efficacy and safety of NICT and PCT to decide on the favorable treatment. METHODS: We conducted a multi-center retrospective cohort study on patients who underwent NICT or PCT between December 2018 and May 2022. Propensity score matching (PSM) was performed with the variables age, sex, smoking status, performance status, stage, histology, and programmed cell death ligand-1 (PD-L1). The Kaplan-Meier method was used to compare survival for the matched patients. RESULTS: Six hundred consecutive patients were included. After PSM, 81 and 162 patients were enrolled in the NICT and PCT groups, respectively. The baseline characteristics were well-balanced. The median progression-free survival was equivalent (11.6 vs. 7.4 months; P = 0.582); however, the median overall survival (OS) was significantly longer in the NICT group than in the PCT group (26.0 vs. 16.8 months; P = 0.005). Furthermore, OS was better in PD-L1-negative patients who underwent NICT than in those who underwent PCT (26.0 vs. 16.8 months; P = 0.045). Safety profiles did not differ significantly in terms of severe adverse event and treatment-related death rates (P = 0.560, and 0.722, respectively). CONCLUSIONS: Real-world data suggests that NICT could be a favorable treatment option compared with PCT for patients with advanced NSCLC. Further follow-up is needed to determine the long-term prognostic benefit.

A generalizability score for aggregate causal effect.

Scientists frequently generalize population level causal quantities such as average treatment effect from a source population to a target population. When the causal effects are heterogeneous, differences in subject characteristics between the source and target populations may make such a generalization difficult and unreliable. Reweighting or regression can be used to adjust for such differences when generalizing. However, these methods typically suffer from large variance if there is limited covariate distribution overlap between the two populations. We propose a generalizability score to address this issue. The score can be used as a yardstick to select target subpopulations for generalization. A simplified version of the score avoids using any outcome information and thus can prevent deliberate biases associated with inadvertent access to such information. Both simulation studies and real data analysis demonstrate convincing results for such selection.

Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes.

Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable estimator. A recent, increasingly common approach directly optimizes the weights toward these two objectives. However, this approach has not yet been feasible in large-scale datasets when investigators wish to flexibly balance general basis functions in an extended feature space. To address this practical problem, we describe a scalable and flexible approach to weighting that integrates a basis expansion in a reproducing kernel Hilbert space with state-of-the-art convex optimization techniques. Specifically, we use the rank-restricted Nyström method to efficiently compute a kernel basis for balancing in nearly linear time and space, and then use the specialized first-order alternating direction method of multipliers to rapidly find the optimal weights. In an extensive simulation study, we provide new insights into the performance of weighting estimators in large datasets, showing that the proposed approach substantially outperforms others in terms of accuracy and speed. Finally, we use this weighting approach to conduct a national study of the relationship between hospital profit status and heart attack outcomes in a comprehensive dataset of 1.27 million patients. We find that for-profit hospitals use interventional cardiology to treat heart attacks at similar rates as other hospitals but have higher mortality and readmission rates.

Assessing the causal effects of a stochastic intervention in time series data: are heat alerts effective in preventing deaths and hospitalizations?

The methodological development of this article is motivated by the need to address the following scientific question: does the issuance of heat alerts prevent adverse health effects? Our goal is to address this question within a causal inference framework in the context of time series data. A key challenge is that causal inference methods require the overlap assumption to hold: each unit (i.e., a day) must have a positive probability of receiving the treatment (i.e., issuing a heat alert on that day). In our motivating example, the overlap assumption is often violated: the probability of issuing a heat alert on a cooler day is near zero. To overcome this challenge, we propose a stochastic intervention for time series data which is implemented via an incremental time-varying propensity score (ItvPS). The ItvPS intervention is executed by multiplying the probability of issuing a heat alert on day $t$-conditional on past information up to day $t$-by an odds ratio $\delta_t$. First, we introduce a new class of causal estimands, which relies on the ItvPS intervention. We provide theoretical results to show that these causal estimands can be identified and estimated under a weaker version of the overlap assumption. Second, we propose nonparametric estimators based on the ItvPS and derive an upper bound for the variances of these estimators. Third, we extend this framework to multisite time series using a spatial meta-analysis approach. Fourth, we show that the proposed estimators perform well in terms of bias and root mean squared error via simulations. Finally, we apply our proposed approach to estimate the causal effects of increasing the probability of issuing heat alerts on each warm-season day in reducing deaths and hospitalizations among Medicare enrollees in 2837 US counties.

Secretory breast carcinoma: clinicopathological features and prognosis of 52 patients.

PURPOSE: Secretory breast carcinoma is a rare histological subtype of invasive breast cancer and considered with an indolent clinical behavior. This study was conducted to analyze the clinicopathological features of patients with secretory breast carcinoma (SBC), explore the outcome, and compare the prognostic difference with invasive ductal breast carcinoma (IDC). METHODS AND MATERIALS: Patients with SBC diagnosed between 2006 and 2017 from Fudan University Shanghai Cancer Center were included in the study, excluding patients with previous malignant tumor history and incomplete clinical data or follow-up records. Peculiar clinicopathological and immunohistochemical features of the cases were fully described. Clinical data of 4979 cases of IDC were also evaluated during this period. After propensity score matching, prognostic analysis of SBCs and IDCs was calculated by Kaplan-Meier method and landmark analysis method. RESULTS: The data of 52 patients diagnosed with SBC were identified from the pathological files. Among them, 47 patients were women, and 5 were men. The median age of the 52 SBCs was 46 years (mean, 48.1 years; range, 10-80 years). The tumor sizes ranged from 0.3 to 6.8 cm, with a mean of 3.5 cm. Eight patients (15.4%) had positive axillary lymph node involvement. The molecular classification was mostly triple-negative breast cancer (65.4%). Fluorescence in situ hybridization confirmed the presence of ETV6::NTRK3 rearrangement in 16 of 18 cases (88.9%). Furthermore, Kaplan-Meier survival analysis and landmark analysis demonstrated that there were no statistically significant differences in DFS and OS between SBC and IDC patients. CONCLUSION: Although SBCs are generally associated with a favorable prognosis, our work exhibited that the clinicopathological features of SBC were partly different from former understandings, indicating that therapeutic procedure should be prudent. Further studies are necessary to fully identify the clinical behavior and predictive markers to improve diagnosis and management in this unique subtype of breast cancer.

Impact of neoadjuvant chemotherapy on breast cancer-related lymphedema after axillary lymph node dissection: a retrospective cohort study.

PURPOSE: We aimed to evaluate whether neoadjuvant chemotherapy (NAC) could be a risk factor for breast cancer-related lymphedema (BCRL) associated with axillary lymph node dissection (ALND). PATIENTS AND METHODS: A total of 596 patients with cT0-4N0-3M0 breast cancer who underwent ALND and chemotherapy were retrospectively analyzed between March 2012 and March 2022. NAC was administered in 188 patients (31.5%), while up-front surgery in 408 (68.5%). Univariate and multivariable Cox regression analyses were performed to determine whether NAC was an independent risk factor for BCRL. With propensity score matching (PSM), the NAC group and up-front surgery group were matched 1:1 by age, body mass index (BMI), molecular subtypes, type of breast surgery, and the number of positive lymph nodes. Kaplan-Meier survival analyses were performed for BCRL between groups before and after PSM. Subgroup analyses were conducted to explore whether NAC differed for BCRL occurrence in people with different characteristics. RESULTS: At a median follow-up of 36.3 months, 130 patients (21.8%) experienced BCRL [NAC, 50/188 (26.60%) vs. up-front surgery, 80/408 (19.61%); P = 0.030]. Multivariable analysis identified that NAC [hazard ratio, 1.503; 95% CI (1.03, 2.19); P = 0.033] was an independent risk factor for BCRL. In addition, the hormone receptor-negative/human epidermal growth factor receptor 2-negative (HR-/HER2-) subtype, breast-conserving surgery (BCS), and increased positive lymph nodes significantly increased BCRL risk. After PSM, NAC remained a risk factor for BCRL [hazard ratio, 1.896; 95% CI (1.18, 3.04); P = 0.007]. Subgroup analyses showed that NAC had a consistent BCRL risk in most clinical subgroups. CONCLUSION: NAC receipt has a statistically significant increase in BCRL risk in patients with ALND. These patients should be closely monitored and may benefit from early BCRL intervention.