Analyzing mastectomy and reconstruction weight in immediate autologous breast reconstruction: A preliminary study.

BACKGROUND: This study aims to explore the ideal breast size by assessing the relationship between mastectomy to free flap weight ratio and complications as well as patient-reported outcomes in autologous breast reconstruction (ABR). METHOD: A retrospective review of patients undergoing bilateral immediate ABR with mastectomy and flap weights available was completed. Patients were divided into three groups based on the ratio of mastectomy to flap weights. The patients were grouped as "maintained" if the flap weight was within 10% of the mastectomy weight. Patients with a weight difference greater than 10% were used to declare "downsized" or "upsized." Outcomes included complications and four domains of the BREAST-Q at 1-year postoperatively. RESULTS: Three hundred and fifty-nine patients were included in the analysis, of which 112 were downsized, 91 maintained, and 156 upsized, respectively. Presence of complications did not significantly differ among the groups. At 1-year postoperatively, Sexual Well-being significantly differed (p = 0.033). Between preoperative and 1 year, patients who upsized experienced an improvement in Satisfaction with Breasts by 16 points (p < 0.001), while patients who downsized experienced a decline in Physical Well-being of the Chest by 7 points (p = 0.016). Multivariable linear regression model showed that Sexual Well-being was 13 points lower in the downsized cohort than in the maintained cohort (ß = -13, 95% confidence interval: -21 to -5.4; p = 0.001). CONCLUSION: Although complication rates do not significantly differ between the three cohorts, patients who downsize may have lower Sexual Well-being postoperatively. Surgeons should consider our preliminary findings to counsel patients preoperatively about the predicted breast size and the impact of downsizing on sexual health.

Nonresponse data in sexual well-being among breast reconstruction patients-who are we overlooking?

BACKGROUND: Missing data can affect the representativeness and accuracy of survey results, and sexual health-related surveys are especially at a higher risk of nonresponse due to their sensitive nature and stigma. The purpose of this study was to evaluate the proportion of patients who do not complete the BREAST-Q Sexual Well-being relative to other BREAST-Q modules and compare responders versus nonresponders of Sexual Well-being. We secondarily examined variables associated with Sexual Well-being at 1-year. METHODS: A retrospective analysis of patients who underwent breast reconstruction from January 2018 to December 2021 and completed any of the BREAST-Q modules postoperatively at 1-year was performed. RESULTS: The 2941 patients were included. Of the four BREAST-Q domains, Sexual Well-being had the highest rate of nonresponse (47%). Patients who were separated (vs. married, OR = 0.69), whose primary language was not English (vs. English, OR = 0.60), and had Medicaid insurance (vs. commercial, OR = 0.67) were significantly less likely to complete the Sexual Well-being. Postmenopausal patients were significantly more likely to complete the survey than premenopausal patients. Lastly, autologous reconstruction patients were 2.93 times more likely to respond than implant-based reconstruction patients (p < 0.001) while delayed (vs. immediate, OR = 0.70, p = 0.022) and unilateral (vs. bilateral, OR = 0.80, p = 0.008) reconstruction patients were less likely to respond. History of psychiatric diagnosis, aromatase inhibitors, and immediate breast reconstruction were significantly associated with lower Sexual Well-being at 1-year. CONCLUSION: Sexual Well-being is the least frequently completed BREAST-Q domain, and there are demographic and clinical differences between responders and nonresponders. We encourage providers to recognize patterns in nonresponse data for Sexual-Well-being to ensure that certain patient population's sexual health concerns are not overlooked.

Disparity Reduction in United States Breast Reconstruction: An Analysis From 2005-2017 Using 3 Nationwide Data Sets.

BACKGROUND: Following passage of the Women's Health and Cancer Rights Act (WHCRA), a steady rise in breast reconstruction rates was reported; however, a recent update is lacking. This study aimed to evaluate longitudinal trends in breast reconstruction (BR) rates in the U.S. and relevant sociodemographic factors. METHODS: Mastectomy cases with/without BR from 2005 through 2017 were abstracted from the National Surgical Quality Improvement Program (NSQIP), Surveillance, Epidemiology, and End Results (SEER) Program, and National Cancer Database (NCDB). BR rates were examined using Poisson regression. Multivariable logistic regression analysis of NCDB data was used to identify predictors of reconstruction. Race and insurance distributions were evaluated over time. RESULTS: Of 1,554,381 mastectomy patients, 507,631 (32.7%) received BR. Annual reconstruction rates per 1000 mastectomies increased from 2005 to 2012 (NSQIP: Incidence Rate Ratio (IRR) 1.077; SEER: 1.090; NCDB: 1.092) and stabilized from 2013 to 2017. NCDB data showed that patients who were younger (≤59 years), privately insured, had fewer comorbidities, and underwent contralateral prophylactic mastectomy were more likely to undergo BR (all p<0.001). Over time, the increase in BR rates was higher among Black (252.3%) and Asian (366.4%) patients than White patients (137.3%). BR rates increased more among Medicaid (418.6%) and Medicare (302.8%) patients than privately insured (125.3%) patients. CONCLUSIONS: This analysis demonstrates stabilization in immediate BR rates over the last decade; reasons behind this stabilization are likely multifactorial. Disparities based on race and insurance type have decreased, with a more equitable distribution of BR rates.

Social Determinants of Health and Patient-reported Outcomes Following Autologous Breast Reconstruction, Using Insurance as a Proxy.

BACKGROUND: Insurance type can serve as a surrogate marker for social determinants of health and can influence many aspects of the breast reconstruction experience. We aimed to examine the impact of insurance coverage on patients reported outcomes with the BREAST-Q (patient reported outcome measure for breast reconstruction patients, in patients receiving) in patients receiving deep inferior epigastric artery perforator (DIEP) flap breast reconstruction. METHODS: We retrospectively examined patients who received DIEP flaps at our institution from 2010 to 2019. Patients were divided into categories by insurance: commercial, Medicaid, or Medicare. Demographic factors, surgical factors, and complication data were recorded. Descriptive statistics, Fisher's exact, Kruskal-Wallis rank sum tests, and generalized estimating equations were performed to identify associations between insurance status and five domains of the BREAST-Q Reconstructive module. RESULTS: A total of 1,285 patients were included, of which 1,011 (78.7%) had commercial, 89 (6.9%) had Medicaid, and 185 (14.4%) had Medicare insurances. Total flap loss rates were significantly higher in the Medicare and Medicaid patients as compared to commercial patients; however, commercial patients had a higher rate of wound dehiscence as compared to Medicare patients. With all other factors controlled for, patients with Medicare had lower Physical Well-being of the Chest (PWBC) than patients with commercial insurance (ß = - 3.1, 95% confidence interval (CI): -5.0, -1.2, p = 0.002). There were no significant associations between insurance classification and other domains of the BREAST-Q. CONCLUSION: Patients with government-issued insurance had lower success rates of autologous breast reconstruction. Further, patients with Medicare had lower PWBC than patients with commercial insurance regardless of other factors, while other BREAST-Q metrics did not differ. Further investigation as to the causes of such variation is warranted in larger, more diverse cohorts.

Reducing Disparities: Regional Anesthesia Blocks for Mastectomy with Reconstruction Within Standardized Regional Anesthesia Pathways.

BACKGROUND: Recent data suggest disparities in receipt of regional anesthesia prior to breast reconstruction. We aimed to understand factors associated with block receipt for mastectomy with immediate tissue expander (TE) reconstruction in a high-volume ambulatory surgery practice with standardized regional anesthesia pathways. PATIENTS AND METHODS: Patients who underwent mastectomy with immediate TE reconstruction from 2017 to 2022 were included. All patients were considered eligible for and were offered preoperative nerve blocks as part of routine anesthesia care. Interpreters were used for non-English speaking patients. Patients who declined a block were compared with those who opted for the procedure. RESULTS: Of 4213 patients who underwent mastectomy with immediate TE reconstruction, 91% accepted and 9% declined a nerve block. On univariate analyses, patients with the lowest rate of block refusal were white, non-Hispanic, English speakers, patients with commercial insurance, and patients undergoing bilateral reconstruction. The rate of block refusal went down from 12 in 2017 to 6% in 2022. Multivariable logistic regression demonstrated that older age (p = 0.011), Hispanic ethnicity (versus non-Hispanic; p = 0.049), Medicaid status (versus commercial insurance; p < 0.001), unilateral surgery (versus bilateral; p = 0.045), and reconstruction in earlier study years (versus 2022; 2017, p < 0.001; 2018, p < 0.001; 2019, p = 0.001; 2020, p = 0.006) were associated with block refusal. CONCLUSIONS: An established preoperative regional anesthesia program with blocks offered to all patients undergoing mastectomy with TE reconstruction can result in decreased racial disparities. However, continued differences in age, ethnicity, and insurance status justify future efforts to enhance preoperative educational efforts that address patient hesitancies in these subpopulations.

Methods in regression analysis in surgical oncology research-best practice guidelines.

BACKGROUND: Using real working examples, we provide strategies and address challenges in linear and logistic regression to demonstrate best practice guidelines and pitfalls of regression modeling in surgical oncology research. METHODS: To demonstrate our best practices, we reviewed patients who underwent tissue expander breast reconstruction between 2019 and 2021. We assessed predictive factors that affect BREAST-Q Physical Well-Being of the Chest (PWB-C) scores at 2 weeks with linear regression modeling and overall complications and malrotation with logistic regression modeling. Model fit and performance were assessed. RESULTS: The 1986 patients were included in the analysis. In linear regression, age [ß = 0.18 (95% CI: 0.09, 0.28); p < 0.001], single marital status [ß = 2.6 (0.31, 5.0); p = 0.026], and prepectoral pocket dissection [ß = 4.6 (2.7, 6.5); p < 0.001] were significantly associated with PWB-C at 2 weeks. For logistic regression, BMI [OR = 1.06 (95% CI: 1.04, 1.08); p < 0.001], age [OR = 1.02 (1.01, 1.03); p = 0.002], bilateral reconstruction [OR = 1.39 (1.09, 1.79); p = 0.009], and prepectoral dissection [OR = 1.53 (1.21, 1.94); p < 0.001] were associated with increased likelihood of a complication. CONCLUSION: We provide focused directives for successful application of regression techniques in surgical oncology research. We encourage researchers to select variables with clinical judgment, confirm appropriate model fitting, and consider clinical plausibility for interpretation when utilizing regression models in their research.

Accelerated Partial Breast Irradiation for HER2-Positive Early-Stage Breast Cancer.

PURPOSE: 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. METHODS AND MATERIALS: 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. RESULTS: 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. CONCLUSIONS: 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.

Practical considerations for sandwich variance estimation in two-stage regression settings.

We present a practical approach for computing the sandwich variance estimator in two-stage regression model settings. As a motivating example for two-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has been rarely applied in regression calibration, despite it requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is likely due to requiring specialized statistical coding. 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 to a resampling variance approach for both settings. Finally, we further compare these two variance estimation approaches for data examples from the Women's Health Initiative (WHI) and Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or over-covered in certain settings, particularly when there was high correlation between covariates or large measurement error.

Missing Data in Patient-Reported Outcomes Research: Utilizing Multiple Imputation to Address an Unavoidable Problem.

BACKGROUND: Patient-reported outcomes (PROs) have become a focus in postoperative surgical care. Unfortunately, studies using PROs can be subject to missing data, which may lead to biases or inaccurate conclusions. Multiple imputation (MI) is a statistical method for addressing missing data in clinical research. The aim of this study was to explore MI as a way to address missing data in PRO research. METHODS: A working example of MI using real-world data was performed using the BREAST-Q PRO measure in postmastectomy reconstruction. A retrospective review of immediate tissue expander breast reconstruction patients in 2019 was conducted to compare BREAST-Q physical well-being of the chest scores between prepectoral and subpectoral cohorts at 2 weeks postoperatively. The observed dataset and three hypothetical missingness situations were created to assess how increasing missingness affects MI results. RESULTS: Overall, 916 patients were included in the analysis. When excluding patients with missing information and solely performing analysis on the completed cases, prepectoral patients had significantly higher physical well-being of the chest scores at 2 weeks postoperatively; however, this trend was reversed with increasing missingness scenarios, where subpectoral patients had higher scores. In comparison, all MI results showed that prepectoral patients had higher scores on average compared with subpectoral patients regardless of missingness scenario. CONCLUSIONS: MI demonstrated consistent results with increasing missingness scenarios, whereas performing analysis in higher missingness scenarios without MI led to varying results. This working example emphasizes the need for missing data methodology to be considered in PRO research.

Very low-dose radiotherapy for extranodal marginal zone lymphoma of bronchus-associated lymphoid tissue.

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.

Clinical-genomic determinants of immune checkpoint blockade response in head and neck squamous cell carcinoma.

BACKGROUNDRecurrent 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.METHODSWe 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.RESULTSHierarchical 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.CONCLUSIONThese 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.FUNDINGFundación 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.

Feasibility and Clinical Utility of Prediction Models for Breast Cancer-Related Lymphedema Incorporating Racial Differences in Disease Incidence.

Importance: Breast cancer-related lymphedema (BCRL) is a common complication of axillary lymph node dissection (ALND) but can also develop after sentinel lymph node biopsy (SLNB). Several models have been developed to predict the risk of disease development before and after surgery; however, these models have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, low sensitivity or specificity, and lack of risk assessment for patients treated with SLNB. Objective: To create simple and accurate prediction models for BCRL that can be used to estimate preoperative or postoperative risk. Design, Setting, and Participants: In this prognostic study, women with breast cancer who underwent ALND or SLNB from 1999 to 2020 at Memorial Sloan Kettering Cancer Center and the Mayo Clinic were included. Data were analyzed from September to December 2022. Main Outcomes and Measures: Diagnosis of lymphedema based on measurements. Two predictive models were formulated via logistic regression: a preoperative model (model 1) and a postoperative model (model 2). Model 1 was externally validated using a cohort of 34 438 patients with an International Classification of Diseases diagnosis of breast cancer. Results: Of 1882 included patients, all were female, and the mean (SD) age was 55.6 (12.2) years; 80 patients (4.3%) were Asian, 190 (10.1%) were Black, 1558 (82.8%) were White, and 54 (2.9%) were another race (including American Indian and Alaska Native, other race, patient refused to disclose, or unknown). A total of 218 patients (11.6%) were diagnosed with BCRL at a mean (SD) follow-up of 3.9 (1.8) years. The BCRL rate was significantly higher among Black women (42 of 190 [22.1%]) compared with all other races (Asian, 10 of 80 [12.5%]; White, 158 of 1558 [10.1%]; other race, 8 of 54 [14.8%]; P < .001). Model 1 included age, weight, height, race, ALND/SLNB status, any radiation therapy, and any chemotherapy. Model 2 included age, weight, race, ALND/SLNB status, any chemotherapy, and patient-reported arm swelling. Accuracy was 73.0% for model 1 (sensitivity, 76.6%; specificity, 72.5%; area under the receiver operating characteristic curve [AUC], 0.78; 95% CI, 0.75-0.81) at a cutoff of 0.18, and accuracy was 81.1% for model 2 (sensitivity, 78.0%; specificity, 81.5%; AUC, 0.86; 95% CI, 0.83-0.88) at a cutoff of 0.10. Both models demonstrated high AUCs on external (model 1: 0.75; 95% CI, 0.74-0.76) or internal (model 2: 0.82; 95% CI, 0.79-0.85) validation. Conclusions and Relevance: In this study, preoperative and postoperative prediction models for BCRL were highly accurate and clinically relevant tools comprised of accessible inputs and underscored the effects of racial differences on BCRL risk. The preoperative model identified high-risk patients who require close monitoring or preventative measures. The postoperative model can be used for screening of high-risk patients, thus decreasing the need for frequent clinic visits and arm volume measurements.

Efficacy of Immediate Lymphatic Reconstruction to Decrease Incidence of Breast Cancer-related Lymphedema: Preliminary Results of Randomized Controlled Trial.

OBJECTIVE: To conduct a randomized controlled trial (RCT) on the efficacy of immediate lymphatic reconstruction (ILR) for decreasing the incidence of breast cancer-related lymphedema (BCRL) after axillary lymph node dissection (ALND). BACKGROUND: Despite encouraging results in small studies, an appropriately powered RCT on ILR has not been performed. METHODS: Women undergoing ALND for breast cancer were randomized in the operating room 1:1 to either ILR, if technically feasible, or no ILR (control). The ILR group underwent lymphatic anastomosis to a regional vein using microsurgical techniques; control group had no repair and cut lymphatics were ligated. Relative volume change (RVC), bioimpedance, quality of life (QoL), and compression use were evaluated at baseline and every 6 months postoperatively up to 24 months. Indocyanine green (ICG) lymphography was performed at baseline and 12 and 24 months postoperatively. The primary outcome was the incidence of BCRL, defined as ≥10% RVC from baseline in the affected extremity at 12-, 18-, or 24-month follow-up. RESULTS: Of 72 patients randomized to ILR and 72 to control from January 2020 to March 2023, our preliminary analysis includes 99 patients with 12-month follow-up, 70 with 18-month follow-up, and 40 with 24-month follow-up. The cumulative incidence of BCRL was 9.5% in the ILR group and 32% in the control group ( P =0.014). The ILR group had lower bioimpedance values, decreased compression usage, better lymphatic function on ICG lymphography, and better QoL than the control group. CONCLUSIONS: Preliminary results of our RCT show that ILR after ALND decreases BCRL incidence. Our goal is to finish the accrual of 174 patients with 24-month follow-up.

An augmented likelihood approach for the Cox proportional hazards model with interval-censored auxiliary and validated outcome data-with application to the Hispanic Community Health Study/Study of Latinos.

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.

Issues in Implementing Regression Calibration Analyses.

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.

Nutritional Blood Concentration Biomarkers in the Hispanic Community Health Study/Study of Latinos: Measurement Characteristics and Power.

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 R2 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.

An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures.

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.