Characterizing attitudes related to future child-bearing in young women diagnosed with early-stage breast cancer.

PURPOSE: This study characterizes attitudes and decision-making around the desire for future children in young women newly diagnosed with early-stage breast cancer and assesses how clinical factors and perceived risk may impact these attitudes. METHODS: This is a prospective study in women < 45 years with newly diagnosed stage 1-3 breast cancer. Patients completed a REDCap survey on fertility and family-building in the setting of hypothetical risk scenarios. Patient, tumor, and treatment characteristics were collected through surveys and medical record. RESULTS: Of 140 study patients [median age = 41.4 (range 23-45)], 71 (50.7%) were interested in having children. Women interested in future childbearing were younger than those who were not interested (mean = 35.2 [SD = 5.2] vs 40.9 years [3.90], respectively, p < 0.001), and more likely to be childless (81% vs 31%, p < 0.001). 54 women (77.1% of patients interested in future children) underwent/planned to undergo oocyte/embryo cryopreservation before chemotherapy. Interest in future childbearing decreased with increasing hypothetical recurrence risk, however 17% of patients wanted to have children despite a 75-100% hypothetical recurrence risk. 24.3% of patients wanted to conceive < 2 years from diagnosis, and 35% of patients with hormone receptor positive tumors were not willing to complete 5 years of hormone therapy. CONCLUSION: Many young women diagnosed with early-stage breast cancer prioritize childbearing. Interest in having a biologic child was not associated with standard prognostic risk factors. Interest decreased with increasing hypothetical recurrence risk, though some patients remained committed to future childbearing despite near certain hypothetical risk. Individual risk assessment should be included in family-planning discussions throughout the continuum of care as it can influence decision-making.

The role of ethnic enclaves and neighborhood socioeconomic status in invasive breast cancer incidence rates among Asian American, Native Hawaiian, and Pacific Islander females in California.

PURPOSE: Few studies have examined whether the incidence rates of invasive breast cancer among Asian American, Native Hawaiian, and Pacific Islander (AANHPI) populations differ by the neighborhood social environment. Thus, we examined associations of ethnic enclave and neighborhood socioeconomic status (nSES) with breast cancer incidence rates among AANHPI females in California. METHODS: A total of 14,738 AANHPI females diagnosed with invasive breast cancer in 2008-2012 were identified from the California Cancer Registry. AANHPI ethnic enclaves (culturally distinct neighborhoods) and nSES were assessed at the census tract level using 2007-2011 American Community Survey data. Breast cancer age-adjusted incidence rates and incidence rate ratios (IRRs) were estimated for AANHPI ethnic enclave, nSES, and their joint effects. Subgroup analyses were conducted by stage of disease. RESULTS: The incidence rate of breast cancer among AANHPI females living in lowest ethnic enclave neighborhoods (quintile (Q)1) were 1.21 times (95% Confidence Interval (CI) 1.11, 1.32) that of AANHPI females living highest ethnic enclave neighborhoods (Q5). In addition, AANHPI females living in highest vs. lowest SES neighborhoods had higher incidence rates of breast cancer (Q5 vs. Q1 IRR = 1.30, 95% CI 1.22 to 1.40). The incidence rate of breast cancer among AANHPI females living in low ethnic enclave + high SES neighborhoods was 1.32 times (95% CI 1.25, 1.39) that of AANHPI females living in high ethnic enclave + low SES neighborhoods. Similar patterns of associations were observed for localized and advanced stage disease. CONCLUSION: For AANHPI females in California, incidence rates of breast cancer differed by nSES, ethnic enclave, when considered independently and jointly. Future studies should examine whether the impact of these neighborhood-level factors on breast cancer incidence rates differ across specific AANHPI ethnic groups and investigate the pathways through which they contribute to breast cancer incidence.

Differences in Acute Postoperative Opioid Use by English Proficiency, Race, and Ethnicity After Total Knee and Hip Arthroplasty.

BACKGROUND: There is increasing interest in documenting disparities in pain management for racial and ethnic minorities and patients with language barriers. Previous studies have found differential prescription patterns of opioids for racial and ethnic minority group and patients having limited English proficiency (LEP) after arthroplasty. However, there is a knowledge gap regarding how the intersection of these sociodemographic factors is associated with immediate postoperative pain management. This study aimed to explore language and racial-ethnic disparities in short-term opioid utilization after total hip and knee arthroplasty. METHODS: We conducted a retrospective cohort study of adult patients who underwent total hip and knee arthroplasty from 2015 to 2019 at an urban medical center. The primary predictor variables included LEP status and racial-ethnic category, and the primary outcome variables were oral morphine equivalents (OMEs) during 2 distinct postoperative periods: the first 12 hours after surgery and from the end of surgery to the end of postoperative day (POD) 1. Patient characteristics and perioperative metrics were described by language status, race, and ethnicity using nonparametric tests, as appropriate. We performed an adjusted generalized estimating equation to assess the total effect of the intersection of LEP and racial-ethnic categories on short-term postoperative opioid use in mean ratios (MRs). RESULTS: This study included a total of 4090 observations, in which 7.9% (323) patients had LEP. Patients reported various racial-ethnic categories, with 72.7% (2975) non-Hispanic White, and minority groups including non-Hispanic Asian and Pacific Islander (AAPI), Hispanic/Latinx, non-Hispanic Black/African American, and Others. Patients self-identifying as non-Hispanic AAPI received fewer OME regardless of LEP status during the first 12 hours postoperatively (MR for English proficient [EP], 0.12 [95% confidence interval, CI, 0.08-0.18]; MR for LEP, 0.22 [95% CI, 0.13-0.37]) and from end of surgery to the end of POD 1 (MR for EP, 0.24 [95% CI, 0.16-0.37]; MR for LEP, 0.42, [95% CI, 0.24-0.73]) than EP non-Hispanic White. Hispanic/Latinx patients with LEP received lower amounts of OME during the first postoperative 12 hours (MR, 0.29; 95% CI, 0.17-0.53) and from end of surgery to the end of POD 1 (MR, 0.42; 95% CI 0.23-0.79) than EP non-Hispanic White. Furthermore, within the non-Hispanic White group, those with LEP received fewer OME within the first 12 hours (MR, 0.33; 95% CI, 0.13-0.83). CONCLUSIONS: We identified an association between LEP, racial-ethnic identity, and short-term postoperative OME utilization after total knee and hip arthroplasty. The observed differences in opioid utilization imply there may be language and racial-ethnic disparities in acute pain management and perioperative care.

Neighborhood attributes and cardiovascular disease risk in breast cancer survivors: The Pathways Study.

BACKGROUND: Breast cancer survivors are at a higher risk of cardiovascular disease (CVD) morbidity and mortality compared with the general population. The impact of objective social and built neighborhood attributes on CVD risk in a cohort of female breast cancer survivors was examined. METHODS: The 3975 participants came from the Pathways Study, a prospective cohort of women with invasive breast cancer from an integrated health care system in northern California. Women diagnosed with breast cancer from 2006 through 2013 were enrolled on average approximately 2 months after diagnosis. Their baseline addresses were geocoded and appended to neighborhood attributes for racial/ethnic composition, socioeconomic status (SES), population density, urbanization, crime, traffic density, street connectivity, parks, recreational facilities, and retail food environment. Incident CVD events included ischemic heart disease, heart failure, cardiomyopathy, or stroke. Cox proportional hazards models estimated associations of neighborhood attributes with CVD risk, which accounted for clustering by block groups. Fully adjusted models included sociodemographic, clinical, and behavioral factors. RESULTS: During follow-up through December 31, 2018, 340 participants (8.6%) had CVD events. A neighborhood racial/ethnic composition measure, percent of Asian American/Pacific Islander residents (lowest quintile hazard ratio [HR], 1.85; 95% CI, 1.03-3.33), and crime index (highest quartile HR, 1.48; 95% CI, 1.08-2.03) were associated with the risk of CVD events independent of individual SES, hormone receptor status, treatment, cardiometabolic comorbidities, body mass index, and physical activity. CONCLUSIONS: With the application of a socio-ecological framework, how residential environments shape health outcomes in women with breast cancer and affect CVD risk in this growing population can be understood.

Clinical characteristics and outcomes in patients with metastatic breast cancer and pseudocirrhosis: a single center retrospective cohort study.

PURPOSE: Pseudocirrhosis is a term used to describe changes in hepatic contour that mimic cirrhosis radiographically, but lack the classic pathologic features of cirrhosis. This radiographic finding is frequently found in patients with metastatic breast cancer (MBC), but the risk factors and clinical consequences are poorly understood. METHODS: In this retrospective study, we identified patients with MBC and pseudocirrhosis who were treated at a single center from 2002 to 2021. We used chart extraction and radiology review to determine demographic characteristics, treatment history, imaging features, and complications of pseudocirrhosis. RESULTS: 120 patients with MBC and pseudocirrhosis were identified with the following BC subtypes: hormone receptor (HR) positive, HER2 negative (n = 99, 82.5%), HR+/HER2+ (n = 14, 11.7%), HR- /HER2+ (n = 3, 2.5%), and triple negative (TNBC; n = 4, 3.3%). All patients had liver metastases and 82.5% (n = 99) had > 15 liver lesions. Thirty-six patients (30%) presented with de novo metastatic disease. Median time from MBC diagnosis to pseudocirrhosis was 29.2 months. 50% of patients had stable or responding disease at the time of pseudocirrhosis diagnosis. Sequelae of pseudocirrhosis included radiographic ascites (n = 97, 80.8%), gastric/esophageal varices (n = 68, 56.7%), splenomegaly (n = 26, 21.7%), GI bleeding (n = 12, 10.0%), and hepatic encephalopathy (n = 11, 9.2%). Median survival was 7.9 months after pseudocirrhosis diagnosis. Radiographic ascites was associated with shorter survival compared to no radiographic ascites (42.8 vs. 76.2 months, p = < 0.001). CONCLUSIONS: This is the largest case series of patients with MBC and pseudocirrhosis. Nearly all patients had HR+ MBC and extensive liver metastases. Survival was short after pseudocirrhosis and prognosis worse with radiographic ascites.

Combining SOS1 and MEK Inhibitors in a Murine Model of Plexiform Neurofibroma Results in Tumor Shrinkage.

Individuals with neurofibromatosis type 1 develop rat sarcoma virus (RAS)-mitogen-activated protein kinase-mitogen-activated and extracellular signal-regulated kinase (RAS-MAPK-MEK)-driven nerve tumors called neurofibromas. Although MEK inhibitors transiently reduce volumes of most plexiform neurofibromas in mouse models and in neurofibromatosis type 1 (NF1) patients, therapies that increase the efficacy of MEK inhibitors are needed. BI-3406 is a small molecule that prevents Son of Sevenless (SOS)1 interaction with Kirsten rat sarcoma viral oncoprotein (KRAS)-GDP, interfering with the RAS-MAPK cascade upstream of MEK. Single agent SOS1 inhibition had no significant effect in the DhhCre;Nf1 fl/fl mouse model of plexiform neurofibroma, but pharmacokinetics (PK)-driven combination of selumetinib with BI-3406 significantly improved tumor parameters. Tumor volumes and neurofibroma cell proliferation, reduced by MEK inhibition, were further reduced by the combination. Neurofibromas are rich in ionized calcium binding adaptor molecule 1 (Iba1)+ macrophages; combination treatment resulted in small and round macrophages, with altered cytokine expression indicative of altered activation. The significant effects of MEK inhibitor plus SOS1 inhibition in this preclinical study suggest potential clinical benefit of dual targeting of the RAS-MAPK pathway in neurofibromas. SIGNIFICANCE STATEMENT: Interfering with the RAS-mitogen-activated protein kinase (RAS-MAPK) cascade upstream of mitogen activated protein kinase kinase (MEK), together with MEK inhibition, augment effects of MEK inhibition on neurofibroma volume and tumor macrophages in a preclinical model system. This study emphasizes the critical role of the RAS-MAPK pathway in controlling tumor cell proliferation and the tumor microenvironment in benign neurofibromas.

Simultaneous selection and incorporation of consistent external aggregate information.

Interest has grown in synthesizing participant level data of a study with relevant external aggregate information. Several efficient and flexible procedures have been developed under the assumption that the internal study and the external sources concern the same population. This homogeneity condition, albeit commonly being imposed, is hard to check due to limitedly available external information in aggregate data forms. Bias may be introduced when the assumption is violated. In this article, we propose a penalized likelihood approach that avoids undesirable bias by simultaneously selecting and synthesizing consistent external aggregate information. The proposed approach provides a general framework which incorporate consistent external information from heterogeneous study populations as long as the conditional distribution of the dependent variable under investigation is same and differences in the independent variable distributions are properly accounted for via a semi-parametric density ratio model. The proposed approach also properly accounts for the sampling errors in the external information. A two-step estimator and an optimization algorithm are proposed for computation. We establish the selection and estimation consistency and the asymptotic normality of the two-step estimator. The proposed approach is illustrated with an analysis of gestational weight gain management studies.

Bayesian adaptive design for covariate-adaptive historical control information borrowing.

Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study-level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate-adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate-dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo-controlled randomized trials on vertebral fracture risk in post-menopausal women.

Effect of Social Needs Case Management on Hospital Use Among Adult Medicaid Beneficiaries : A Randomized Study.

BACKGROUND: Case management programs assisting patients with social needs may improve health and avoid unnecessary health care use, but little is known about their effectiveness. OBJECTIVE: This large-scale study assessed the population-level impact of a case management program designed to address patients' social needs. DESIGN: Single-site randomized encouragement design with administrative enrollment from an eligible population and intention-to-treat analysis. Study participants were enrolled between August 2017 and December 2018 and followed for 1 year. (ClinicalTrials.gov: NCT04000074). SETTING: Contra Costa County, an economically and culturally diverse community in the San Francisco Bay Area. PARTICIPANTS: 57 972 randomized enrollments of adult Medicaid patients at elevated risk for health care use (top 15%) to the intervention or control group. INTERVENTION: Enrollees were offered 12 months of social needs case management, which provided more intensive services to patients with higher demonstrated needs. MEASUREMENTS: Medical use was measured via emergency department (ED) visits and inpatient admissions, some of which were classified as avoidable. RESULTS: Participants in the intervention group visited the ED at ratios of 0.96 (95% CI, 0.91 to 1.00) for all visits and 0.97 (CI, 0.92 to 1.03) for avoidable visits relative to the control group. The intervention group was hospitalized at ratios of 0.89 (CI, 0.81 to 0.98) for all admissions and 0.72 (CI, 0.55 to 0.88) for avoidable admissions. LIMITATIONS: Only 40% of the intervention group engaged with the program. The program was in continual development during the trial period. CONCLUSION: Although social needs case management programs may reduce health care use, these savings may not cover full program costs. More work is needed to identify ways to increase patient uptake and define characteristics of successful programs. PRIMARY FUNDING SOURCE: Contra Costa Health Services via the Medicaid waiver program.

Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multicentre pooled analysis of 5161 patients.

BACKGROUND: Previous studies have independently validated the prognostic relevance of residual cancer burden (RCB) after neoadjuvant chemotherapy. We used results from several independent cohorts in a pooled patient-level analysis to evaluate the relationship of RCB with long-term prognosis across different phenotypic subtypes of breast cancer, to assess generalisability in a broad range of practice settings. METHODS: In this pooled analysis, 12 institutes and trials in Europe and the USA were identified by personal communications with site investigators. We obtained participant-level RCB results, and data on clinical and pathological stage, tumour subtype and grade, and treatment and follow-up in November, 2019, from patients (aged ≥18 years) with primary stage I-III breast cancer treated with neoadjuvant chemotherapy followed by surgery. We assessed the association between the continuous RCB score and the primary study outcome, event-free survival, using mixed-effects Cox models with the incorporation of random RCB and cohort effects to account for between-study heterogeneity, and stratification to account for differences in baseline hazard across cancer subtypes defined by hormone receptor status and HER2 status. The association was further evaluated within each breast cancer subtype in multivariable analyses incorporating random RCB and cohort effects and adjustments for age and pretreatment clinical T category, nodal status, and tumour grade. Kaplan-Meier estimates of event-free survival at 3, 5, and 10 years were computed for each RCB class within each subtype. FINDINGS: We analysed participant-level data from 5161 patients treated with neoadjuvant chemotherapy between Sept 12, 1994, and Feb 11, 2019. Median age was 49 years (IQR 20-80). 1164 event-free survival events occurred during follow-up (median follow-up 56 months [IQR 0-186]). RCB score was prognostic within each breast cancer subtype, with higher RCB score significantly associated with worse event-free survival. The univariable hazard ratio (HR) associated with one unit increase in RCB ranged from 1·55 (95% CI 1·41-1·71) for hormone receptor-positive, HER2-negative patients to 2·16 (1·79-2·61) for the hormone receptor-negative, HER2-positive group (with or without HER2-targeted therapy; p<0·0001 for all subtypes). RCB score remained prognostic for event-free survival in multivariable models adjusted for age, grade, T category, and nodal status at baseline: the adjusted HR ranged from 1·52 (1·36-1·69) in the hormone receptor-positive, HER2-negative group to 2·09 (1·73-2·53) in the hormone receptor-negative, HER2-positive group (p<0·0001 for all subtypes). INTERPRETATION: RCB score and class were independently prognostic in all subtypes of breast cancer, and generalisable to multiple practice settings. Although variability in hormone receptor subtype definitions and treatment across patients are likely to affect prognostic performance, the association we observed between RCB and a patient's residual risk suggests that prospective evaluation of RCB could be considered to become part of standard pathology reporting after neoadjuvant therapy. FUNDING: National Cancer Institute at the US National Institutes of Health.

COVID-19 Outcomes Among Patients With Cancer: Observations From the University of California Cancer Consortium COVID-19 Project Outcomes Registry.

BACKGROUND: The risks associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated illness, coronavirus disease 2019 (COVID-19), among patients with a cancer diagnosis have not been fully characterized. This study leverages data from a multi-institutional cohort study, the University of California Cancer COVID Consortium, to evaluate outcomes associated with SARS-CoV-2 infection among patients with cancer. METHODS: Clinical data were collected from March to November 2020 and included patient demographics, cancer history and treatment, SARS-CoV-2 exposure and testing, and COVID-19 clinical management and outcomes. Multivariate ordinal logistic regression permitting unequal slopes was used to evaluate the impact of demographic, disease, and treatment factors on SARS-CoV-2 related hospitalization, intensive care unit (ICU) admission, and mortality. FINDINGS: Among all evaluated patients (n = 303), 147 (48%) were male, 118 (29%) were older adults (≥65 years old), and 104 (34%) were non-Hispanic white. A subset (n = 63, 21%) had hematologic malignancies and the remaining had solid tumors. Patients were hospitalized for acute care (n = 79, 26%), ICU-level care (n = 28, 9%), or died (n = 21, 7%) due to COVID-19. Patients with ≥2 comorbidities were more likely to require acute care (odds ratio [OR] 2.09 [95% confidence interval (CI), 1.23-3.55]). Cough was identified as a significant predictor of ICU hospitalization (OR 2.16 [95% CI, 1.03-4.57]). Importantly, mortality was associated with an active cancer diagnosis (OR 3.64 [95% CI, 1.40-9.5]) or advanced age (OR 3.86 [95% CI, 1.2-12.44]). INTERPRETATION: This study observed that patients with active cancer or advanced age are at an increased risk of death from COVID-19. These study observations can inform risk counseling related to COVID-19 for patients with a cancer diagnosis.

Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach.

With the increasing availability of data in the public domain, there has been a growing interest in exploiting information from external sources to improve the analysis of smaller scale studies. An emerging challenge in the era of big data is that the subject-level data are high dimensional, but the external information is at an aggregate level and of a lower dimension. Moreover, heterogeneity and uncertainty in the auxiliary information are often not accounted for in information synthesis. In this paper, we propose a unified framework to summarize various forms of aggregated information via estimating equations and develop a penalized empirical likelihood approach to incorporate such information in logistic regression. When the homogeneity assumption is violated, we extend the method to account for population heterogeneity among different sources of information. When the uncertainty in the external information is not negligible, we propose a variance estimator adjusting for the uncertainty. The proposed estimators are asymptotically more efficient than the conventional penalized maximum likelihood estimator and enjoy the oracle property even with a diverging number of predictors. Simulation studies show that the proposed approaches yield higher accuracy in variable selection compared with competitors. We illustrate the proposed methodologies with a pediatric kidney transplant study.

Perioperative Normal Saline Administration and Delayed Graft Function in Patients Undergoing Kidney Transplantation: A Retrospective Cohort Study.

BACKGROUND: Perioperative normal saline administration remains common practice during kidney transplantation. The authors hypothesized that the proportion of balanced crystalloids versus normal saline administered during the perioperative period would be associated with the likelihood of delayed graft function. METHODS: The authors linked outcome data from a national transplant registry with institutional anesthesia records from 2005 to 2015. The cohort included adult living and deceased donor transplants, and recipients with or without need for dialysis before transplant. The primary exposure was the percent normal saline of the total amount of crystalloids administered perioperatively, categorized into a low (less than or equal to 30%), intermediate (greater than 30% but less than 80%), and high normal saline group (greater than or equal to 80%). The primary outcome was the incidence of delayed graft function, defined as the need for dialysis within 1 week of transplant. The authors adjusted for the following potential confounders and covariates: transplant year, total crystalloid volume, surgical duration, vasopressor infusions, and erythrocyte transfusions; recipient sex, age, body mass index, race, number of human leukocyte antigen mismatches, and dialysis vintage; and donor type, age, and sex. RESULTS: The authors analyzed 2,515 records. The incidence of delayed graft function in the low, intermediate, and high normal saline group was 15.8% (61/385), 17.5% (113/646), and 21% (311/1,484), respectively. The adjusted odds ratio (95% CI) for delayed graft function was 1.24 (0.85 to 1.81) for the intermediate and 1.55 (1.09 to 2.19) for the high normal saline group compared with the low normal saline group. For deceased donor transplants, delayed graft function in the low, intermediate, and high normal saline group was 24% (54/225 [reference]), 28.6% (99/346; adjusted odds ratio, 1.28 [0.85 to 1.93]), and 30.8% (277/901; adjusted odds ratio, 1.52 [1.05 to 2.21]); and for living donor transplants, 4.4% (7/160 [reference]), 4.7% (14/300; adjusted odds ratio, 1.15 [0.42 to 3.10]), and 5.8% (34/583; adjusted odds ratio, 1.66 [0.65 to 4.25]), respectively. CONCLUSIONS: High percent normal saline administration is associated with delayed graft function in kidney transplant recipients.

Predictive generalized varying-coefficient longitudinal model.

We propose a nonparametric bivariate varying coefficient generalized linear model to predict a mean response trajectory in the future given an individual's characteristics at present or an earlier time point in a longitudinal study. Given the measurement time of the predictors, the coefficients vary as functions of the future time over which the prediction of the mean response is concerned and illustrate the dynamic association between the future response and the earlier measured predictors. We use a nonparametric approach that takes advantage of features of both the kernel and the spline methods for estimation. The resulting coefficient estimator is asymptotically consistent under mild regularity conditions. We also develop a new bootstrap approach to construct simultaneous confidence bands for statistical inference about the coefficients and the predicted response trajectory based on the coverage rate of bootstrap estimates. We use the Framingham Heart Study to illustrate the methodology. The proposed procedure is applied to predict the probability trajectory of hypertension risk given individuals' health condition in early adulthood and to examine the impact of risk factors in early adulthood on a long-term risk of hypertension over several decades.

Synthesizing external aggregated information in the penalized Cox regression under population heterogeneity.

Synthesizing external aggregated information has been proven useful in improving estimation efficiency when conducting statistical analysis using a limited amount of data. In this paper, we develop a unified framework for combining information from high-dimensional individual-level data and potentially low-dimensional external aggregate data under the Cox model. We summarize various forms of external aggregated information by population estimating equations and propose a penalized empirical likelihood approach to borrow information from these estimating equations. The proposed methods possess the flexibility to handle the case where individual-level data and external aggregate data are from heterogeneous populations. Specifically, a penalized empirical likelihood ratio test is developed to check for the potential heterogeneity, and a semiparametric density ratio model is postulated to account for the heterogeneity. Moreover, we study the impact of uncertainty in the auxiliary information on the efficiency gain and propose a modified variance estimator to adjust for the uncertainty. The proposed estimators enjoy the oracle property and are asymptotically more efficient than the penalized partial likelihood estimator that does not exploit the external aggregated information. Simulation studies show improvement in both estimation efficiency and variable selection over the competitors. The proposed approaches are applied to the analysis of a pediatric kidney transplant study for illustration.

Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection.

The goal of the optimal treatment regime is maximizing treatment benefits via personalized treatment assignments based on the observed patient and treatment characteristics. Parametric regression-based outcome learning approaches require exploring complex interplay between the outcome and treatment assignments adjusting for the patient and treatment covariates, yet correctly specifying such relationships is challenging. Thus, a robust method against misspecified models is desirable in practice. Parsimonious models are also desired to pursue a concise interpretation and to avoid including spurious predictors of the outcome or treatment benefits. These issues have not been comprehensively addressed in the presence of competing risks. Recognizing that competing risks and group variables are frequently present, we propose a doubly robust estimation with adaptive L 1 penalties to select important variables at both group and within-group levels for competing risks data. The proposed method is applied to hematopoietic cell transplantation data to personalize the graft source choice for treatment-related mortality (TRM). While the existing medical literature attempts to find a uniform solution ignoring the heterogeneity of the graft source effects on TRM, the analysis results show the effect of the graft source on TRM could be different depending on the patient-specific characteristics.

Residual tumor volume discriminates prognosis after surgery for neuroendocrine liver metastasis.

BACKGROUND AND OBJECTIVES: We developed objective measurements of preoperative and residual tumor volume, and debulking rate, to evaluate their prognostic value for neuroendocrine liver metastasis (NELM). METHODS: Seventy-three patients who underwent surgery for NELM were analyzed retrospectively. Indices of preoperative and postoperative residual tumor volume (pre-volume index [VI] and post-VI) were calculated as the sum of the cubes of individual tumor diameters on preoperative and postoperative imaging, respectively. The debulking rate (%) was calculated as 100 - 100 × post-VI/pre-VI. The classification and regression trees method was used to classify pre-VI and post-VI. RESULTS: Overall survival (OS) was discriminated by preoperative tumor volume (5-year OS rates, 87.8% for low pre-VI and 60.1% for high pre-VI; P = .037) and residual tumor volume (5-year OS rates, 88.1% for low post-VI and 24.8% for high post-VI; P < .001). In contrast, debulking rates of 100%, ≥90%, and <90% did not discriminate OS (5-year OS rates, 88.0%, 61.9%, and 58.9%, respectively, not significant). In multivariate analysis, residual tumor volume (high post-VI, hazard ratio, 6.40; 95% confidence interval, 1.45-32.3) was an independent prognostic factor for OS. CONCLUSIONS: Objective measurement of tumor volume demonstrates that residual tumor volume is prognostic after surgery for NELM.

Critical appraisal of Bayesian dynamic borrowing from an imperfectly commensurate historical control.

Bayesian dynamic borrowing designs facilitate borrowing information from historical studies. Historical data, when perfectly commensurate with current data, have been shown to reduce the trial duration and the sample size, while inflation in the type I error and reduction in the power have been reported, when imperfectly commensurate. These results, however, were obtained without considering that Bayesian designs are calibrated to meet regulatory requirements in practice and even no-borrowing designs may use information from historical data in the calibration. The implicit borrowing of historical data suggests that imperfectly commensurate historical data may similarly impact no-borrowing designs negatively. We will provide a fair appraiser of Bayesian dynamic borrowing and no-borrowing designs. We used a published selective adaptive randomization design and real clinical trial setting and conducted simulation studies under varying degrees of imperfectly commensurate historical control scenarios. The type I error was inflated under the null scenario of no intervention effect, while larger inflation was noted with borrowing. The larger inflation in type I error under the null setting can be offset by the greater probability to stop early correctly under the alternative. Response rates were estimated more precisely and the average sample size was smaller with borrowing. The expected increase in bias with borrowing was noted, but was negligible. Using Bayesian dynamic borrowing designs may improve trial efficiency by stopping trials early correctly and reducing trial length at the small cost of inflated type I error.

Association Between Plasma Level of Collagen Type III Alpha 1 Chain and Development of Strictures in Pediatric Patients With Crohn's Disease.

BACKGROUND & AIMS: There are few serum biomarkers to identify patients with Crohn's disease (CD) who are at risk for stricture development. The extracellular matrix components, collagen type III alpha 1 chain (COL3A1) and cartilage oligomeric matrix protein (COMP), could contribute to intestinal fibrosis. We investigated whether children with inflammatory CD (B1) who later develop strictures (B2) have increased plasma levels of COL3A1 or COMP at diagnosis, compared with children who remain B1. We compared results with previously studied biomarkers, including autoantibodies against colony-stimulating factor 2 (CSF2). METHODS: We selected 161 subjects (mean age, 12.2 y; 62% male) from the Risk Stratification and Identification of Immunogenic and Microbial Markers of Rapid Disease Progression in Children with Crohn's cohort, completed at 28 sites in the United States and Canada from 2008 through 2012. The children underwent colonoscopy and upper endoscopy at diagnosis and were followed up every 6 months for 36 months; plasma samples were collected at baseline. Based on CD phenotype, children were separated to group 1 (B1 phenotype at diagnosis and follow-up evaluation), group 2 (B2 phenotype at diagnosis), or group 3 (B1 phenotype at diagnosis who developed strictures during follow-up evaluation). Plasma samples were collected from patients and 40 children without inflammatory bowel disease (controls) at baseline and analyzed by enzyme-linked immunosorbent assay to measure COL3A1 and COMP. These results were compared with those from a previous biomarker study. The Kruskal-Wallis test and the pairwise Dunn test with Bonferroni correction were used to compare differences among groups. RESULTS: The median baseline concentration of COL3A1 was significantly higher in plasma from group 3 vs group 1 (P < .01) and controls (P = .01). Median baseline plasma concentrations of COMP did not differ significantly among groups. A model comprising baseline concentrations of COL3A1 and anti-CSF2 identified patients with B2 vs B1 CD with an area under the curve of 0.80 (95% CI, 0.71-0.89); the combined concentration identified patients with strictures with a sensitivity value of 0.70 (95% CI, 0.55-0.83) and a specificity value of 0.83 (95% CI, 0.67-0.93). CONCLUSIONS: We found median plasma concentrations of COL3A1, measured by enzyme-linked immunosorbent assay at diagnosis, to be significantly higher in patients with CD who later developed strictures than in patients without strictures. The combination of concentrations of COL3A1 and anti-CSF2 might be used to identify pediatric patients at CD diagnosis who are at risk for future strictures. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00790543.

Estimating heterogeneous treatment effects for latent subgroups in observational studies.

Individuals may vary in their responses to treatment, and identification of subgroups differentially affected by a treatment is an important issue in medical research. The risk of misleading subgroup analyses has become well known, and some exploratory analyses can be helpful in clarifying how covariates potentially interact with the treatment. Motivated by a real data study of pediatric kidney transplant, we consider a semiparametric Bayesian latent model and examine its utility for an exploratory subgroup effect analysis using secondary data. The proposed method is concerned with a clinical setting where the number of subgroups is much smaller than that of potential predictors and subgroups are only latently associated with observed covariates. The semiparametric model is flexible in capturing the latent structure driven by data rather than dictated by parametric modeling assumptions. Since it is difficult to correctly specify the conditional relationship between the response and a large number of confounders in modeling, we use propensity score matching to improve the model robustness by balancing the covariates distribution. Simulation studies show that the proposed analysis can find the latent subgrouping structure and, with propensity score matching adjustment, yield robust estimates even when the outcome model is misspecified. In the real data analysis, the proposed analysis reports significant subgroup effects on steroid avoidance in kidney transplant patients, whereas standard proportional hazards regression analysis does not.