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In this paper, we present a practical approach for computing the sandwich variance estimator in 2-stage regression model settings. As a motivating example for 2-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has rarely been applied in regression calibration, despite its requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is probably because it requires specialized statistical coding. Here we first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich estimator to a resampling variance approach for both settings. Finally, we further compare these 2 variance estimation approaches in data examples from the Women's Health Initiative (1993–2005) and the Hispanic Community Health Study/Study of Latinos (2008–2011). In our simulations, the sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or overcovered in certain settings, particularly when there was high correlation between covariates or large measurement error. [ABSTRACT FROM AUTHOR]

HER2-positive (HER2+) breast cancer historically exhibited the most adverse local outcomes and, consequently, has had limited representation among trials of partial breast irradiation (PBI). We hypothesized that with contemporary HER2-targeted agents, patients may now exhibit excellent disease control outcomes after adjuvant PBI. Using a prospectively maintained institutional database, we identified patients with HER2+ breast cancer treated with breast conserving surgery (BCS) and PBI from 2000 to 2022. Salient clinicopathologic and treatment parameters were collected. All patients received external beam PBI to 40 Gy in 10 daily fractions. Cumulative incidence functions were calculated to estimate the incidence of local recurrence (LR) with the competing risk of death. We identified 1248 patients who underwent PBI at our institution, of whom the study cohort comprised 52 (4%) with HER2+ breast cancer (median age, 64 years; range, 44-87). Nearly all had T1 tumors (98%; median size, 12 mm [range, 1-21 mm]). Most had estrogen receptor positive disease (88%), and all patients had negative surgical margins. Nearly all underwent sentinel node biopsy (94%), with the remainder undergoing no surgical axillary evaluation. Forty-two (81%) received chemotherapy; 40 (77%), hormone therapy; and 42 (81%), HER2-directed therapy, most commonly trastuzumab. At 143.8 person-years of follow-up (range, 7-226 months for each patient), we observed 2 LR events at 14 and 26 months, respectively, yielding a 2-year LR rate of 3.8%. No regional or distant recurrences were observed, nor were any breast-specific mortality events. Two deaths were observed, both without evidence of disease. Among a cohort of patients with HER2+ early-stage breast cancer managed with BCS and PBI, we observed a 2-year LR rate of 3.8%, with no regional or distant recurrences and excellent overall survival. These findings require confirmation with longer follow-up among larger cohorts but appear consistent with the excellent results of contemporary randomized trials of PBI unselected for HER2 status. [ABSTRACT FROM AUTHOR]

Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008–2011), self-reported information on diet was collected from all participants (n  = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014–2017) and third (2020–2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n  = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010–2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration–based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R 2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes. [ABSTRACT FROM AUTHOR]

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008–2011) and simulations. We conclude with recommendations for how to perform regression calibration. [ABSTRACT FROM AUTHOR]

In large epidemiologic studies, it is typical for an inexpensive, non-invasive procedure to be used to record disease status during regular follow-up visits, with less frequent assessment by a gold standard test. Inexpensive outcome measures like self-reported disease status are practical to obtain, but can be error-prone. Association analysis reliant on error-prone outcomes may lead to biased results; however, restricting analyses to only data from the less frequently observed error-free outcome could be inefficient. We have developed an augmented likelihood that incorporates data from both error-prone outcomes and a gold standard assessment. We conduct a numerical study to show how we can improve statistical efficiency by using the proposed method over standard approaches for interval-censored survival data that do not leverage auxiliary data. We extend this method for the complex survey design setting so that it can be applied in our motivating data example. Our method is applied to data from the Hispanic Community Health Study/Study of Latinos to assess the association between energy and protein intake and the risk of incident diabetes. In our application, we demonstrate how our method can be used in combination with regression calibration to additionally address the covariate measurement error in self-reported diet. [ABSTRACT FROM AUTHOR]

Measurement error arises commonly in clinical research settings that rely on data from electronic health records or large observational cohorts. In particular, self‐reported outcomes are typical in cohort studies for chronic diseases such as diabetes in order to avoid the burden of expensive diagnostic tests. Dietary intake, which is also commonly collected by self‐report and subject to measurement error, is a major factor linked to diabetes and other chronic diseases. These errors can bias exposure‐disease associations that ultimately can mislead clinical decision‐making. We have extended an existing semiparametric likelihood‐based method for handling error‐prone, discrete failure time outcomes to also address covariate error. We conduct an extensive numerical study to compare the proposed method to the naive approach that ignores measurement error in terms of bias and efficiency in the estimation of the regression parameter of interest. In all settings considered, the proposed method showed minimal bias and maintained coverage probability, thus outperforming the naive analysis which showed extreme bias and low coverage. This method is applied to data from the Women's Health Initiative to assess the association between energy and protein intake and the risk of incident diabetes mellitus. Our results show that correcting for errors in both the self‐reported outcome and dietary exposures leads to considerably different hazard ratio estimates than those from analyses that ignore measurement error, which demonstrates the importance of correcting for both outcome and covariate error. [ABSTRACT FROM AUTHOR]

Extranodal marginal zone lymphoma of bronchus-associated lymphoid tissue (BALT) is a rare cancer for which optimal treatment strategies are undefined. Retrospective analyses suggest excellent outcomes with surgical resection for localized BALT lymphoma; however, the role of radiotherapy remains underexplored. We report the largest-to-date single-center analysis of 13 primary BALT lymphoma patients treated with radiotherapy. Of 15 treated lesions, we report a 100% response rate with complete response (CR) achieved in 67% of lesions. Among 10 lesions treated with very low-dose radiotherapy (VLDRT; 4 Gray [Gy]), 6 (60%) achieved a CR; among 5 lesions treated with full-dose radiotherapy (24–36 Gy), 4 (80%) achieved a CR. There were no local recurrences. Only one patient, treated with 30 Gy, developed an acute grade 3/4 toxic effect. There were no events of radiation-induced secondary malignancies. Our institutional experience indicates that radiotherapy, including VLDRT, is a safe and effective treatment for primary BALT lymphoma. [ABSTRACT FROM AUTHOR]

BACKGROUND. Recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is generally an incurable disease, with patients experiencing median survival of under 10 months and significant morbidity. While immune checkpoint blockade (ICB) drugs are effective in approximately 20% of patients, the remaining experience limited clinical benefit and are exposed to potential adverse effects and financial costs. Clinically approved biomarkers, such as tumor mutational burden (TMB), have a modest predictive value in HNSCC. METHODS. We analyzed clinical and genomic features, generated using whole-exome sequencing, in 133 ICB-treated patients with R/M HNSCC, of whom 69 had virus-associated and 64 had non-virus-associated tumors. RESULTS. Hierarchical clustering of genomic data revealed 6 molecular subtypes characterized by a wide range of objective response rates and survival after ICB therapy. The prognostic importance of these 6 subtypes was validated in an external cohort. A random forest-based predictive model, using several clinical and genomic features, predicted progression-free survival (PFS), overall survival (OS), and response with greater accuracy than did a model based on TMB alone. Recursive partitioning analysis identified 3 features (systemic inflammatory response index, TMB, and smoking signature) that classified patients into risk groups with accurate discrimination of PFS and OS. CONCLUSION. These findings shed light on the immunogenomic characteristics of HNSCC tumors that drive differential responses to ICB and identify a clinical-genomic classifier that outperformed the current clinically approved biomarker of TMB. This validated predictive tool may help with clinical risk stratification in patients with R/M HNSCC for whom ICB is being considered. FUNDING. Fundación Alfonso Martín Escudero, NIH R01 DE027738, US Department of Defense CA210784, The Geoffrey Beene Cancer Research Center, The MSKCC Population Science Research Program, the Jayme Flowers Fund, the Sebastian Nativo Fund, and the NIH/NCI Cancer Center Support Grant P30 CA008748. [ABSTRACT FROM AUTHOR]