Host genetic variants, Epstein-Barr virus subtypes, and the risk of nasopharyngeal carcinoma: Assessment of interaction and mediation.

Epstein-Barr virus (EBV) and human leukocyte antigen (HLA) polymorphisms are well-known risk factors for nasopharyngeal carcinoma (NPC). However, the combined effects between HLA and EBV on the risk of NPC are unknown. We applied a causal inference framework to disentangle interaction and mediation effects between two host HLA SNPs, rs2860580 and rs2894207, and EBV variant 163364 with a population-based case-control study in NPC-endemic southern China. We discovered the strong interaction effects between the high-risk EBV subtype and both HLA SNPs on NPC risk (rs2860580, relative excess risk due to interaction [RERI] = 4.08, 95% confidence interval [CI] = 2.03-6.14; rs2894207, RERI = 3.37, 95% CI = 1.59-5.15), accounting for the majority of genetic risk effects. These results indicate that HLA genes and the high-risk EBV have joint effects on NPC risk. Prevention strategies targeting the high-risk EBV subtype would largely reduce NPC risk associated with EBV and host genetic susceptibility.

Incident Cardiovascular Disease Risk among Older Asian, Native Hawaiian and Pacific Islander Breast Cancer Survivors.

BACKGROUND: Cardiotoxicity among breast cancer survivors is associated with chemotherapy and radiation therapy. The risk of cardiovascular disease (CVD) among Asian, Native Hawaiian and Pacific Islander (ANHPI) breast cancer survivors in the United States is unknown. METHODS: We used the SEER-Medicare linked database to estimate the risk of CVD among older breast cancer survivors. International Classification of Disease diagnosis codes were used to identify incident CVD outcomes. Cox proportional hazards models were used to estimate HRs and 95% confidence intervals (CI) comparing ANHPI with Non-Hispanic White (NHW) patients with breast cancer for CVD, and among ANHPI race and ethnicity groups. RESULTS: A total of 7,122 ANHPI breast cancer survivors and 21,365 NHW breast cancer survivors were identified. The risks of incident heart failure and ischemic heart disease were lower among ANHPI compared with NHW breast cancer survivors (HRheart failure, 0.72; 95% CI, 0.61-0.84; HRheart disease, 0.74; 95% CI, 0.63-0.88). Compared with Japanese patients with breast cancer, Filipino, Asian Indian and Pakistani, and Native Hawaiian breast cancer survivors had higher risks of heart failure. ischemic heart disease and death. Among ANHPI breast cancer survivors, risk factors for heart failure included older age, higher comorbidity score, distant cancer stage and chemotherapy. CONCLUSIONS: Our results support heterogeneity in CVD outcomes among breast cancer survivors among ANHPI race and ethnicity groups. Further research is needed to elucidate the disparities experienced among ANHPI breast cancer survivors. IMPACT: Filipino, Asian Indian and Pakistani, and Native Hawaiian patients with breast cancer had higher risks of heart failure, ischemic heart disease and death among ANHPI patients with breast cancer.

Identifying Patients for Intensive Blood Pressure Treatment Based on Cognitive Benefit: A Secondary Analysis of the SPRINT Randomized Clinical Trial.

Importance: Intensive vs standard treatment to lower systolic blood pressure (SBP) reduces risk of mild cognitive impairment (MCI) or dementia; however, the magnitude of cognitive benefit likely varies among patients. Objective: To estimate the magnitude of cognitive benefit of intensive vs standard systolic BP (SBP) treatment. Design, Setting, and Participants: In this ad hoc secondary analysis of the Systolic Blood Pressure Intervention Trial (SPRINT), 9361 randomized clinical trial participants 50 years or older with high cardiovascular risk but without a history of diabetes, stroke, or dementia were followed up. The SPRINT trial was conducted between November 1, 2010, and August 31, 2016, and the present analysis was completed on October 31, 2022. Intervention: Systolic blood pressure treatment to an intensive (<120 mm Hg) vs standard (<140 mm Hg) target. Main Outcomes and Measures: The primary outcome was a composite of adjudicated probable dementia or amnestic MCI. Results: A total of 7918 SPRINT participants were included in the analysis; 3989 were in the intensive treatment group (mean [SD] age, 67.9 [9.2] years; 2570 [64.4%] men; 1212 [30.4%] non-Hispanic Black) and 3929 were in the standard treatment group (mean [SD] age, 67.9 [9.4] years; 2570 [65.4%] men; 1249 [31.8%] non-Hispanic Black). Over a median follow-up of 4.13 (IQR, 3.50-5.88) years, there were 765 and 828 primary outcome events in the intensive treatment group and standard treatment group, respectively. Older age (hazard ratio [HR] per 1 SD, 1.87 [95% CI, 1.78-1.96]), Medicare enrollment (HR per 1 SD, 1.42 [95% CI, 1.35-1.49]), and higher baseline serum creatinine level (HR per 1 SD, 1.24 [95% CI, 1.19-1.29]) were associated with higher risk of the primary outcome, while better baseline cognitive functioning (HR per 1 SD, 0.43 [95% CI, 0.41-0.44]) and active employment status (HR per 1 SD, 0.44 [95% CI, 0.42-0.46]) were associated with lower risk of the primary outcome. Risk of the primary outcome by treatment goal was estimated accurately based on similar projected and observed absolute risk differences (C statistic = 0.79). Higher baseline risk for the primary outcome was associated with greater benefit (ie, larger absolute reduction of probable dementia or amnestic MCI) of intensive vs standard treatment across the full range of estimated baseline risk. Conclusions and Relevance: In this secondary analysis of the SPRINT trial, participants with higher baseline projected risk of probable dementia or amnestic MCI gained greater absolute cognitive benefit from intensive vs standard SBP treatment in a monotonic fashion. Trial Registration: ClinicalTrials.gov Identifier: NCT01206062.

An efficient approach for optimizing the cost-effective individualized treatment rule using conditional random forest.

Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules that are often more cost-effective than one-size-fits-all treatment. Thus, it is of great interest to develop statistical tools for learning such a cost-effective individualized treatment rule under the causal inference framework that allows proper handling of potential confounding and can be applied to both trials and observational studies. In this paper, we use the concept of net-monetary-benefit to assess the trade-off between health benefits and related costs. We estimate cost-effective individualized treatment rule as a function of patients' characteristics that, when implemented, optimizes the allocation of limited healthcare resources by maximizing health gains while minimizing treatment-related costs. We employ the conditional random forest approach and identify the optimal cost-effective individualized treatment rule using net-monetary-benefit-based classification algorithms, where two partitioned estimators are proposed for the subject-specific weights to effectively incorporate information from censored individuals. We conduct simulation studies to evaluate the performance of our proposals. We apply our top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention Trial to illustrate the cost-effectiveness gains of assigning customized intensive blood pressure therapy.

Estimating the optimal individualized treatment rule from a cost-effectiveness perspective.

Optimal individualized treatment rules (ITRs) provide customized treatment recommendations based on subject characteristics to maximize clinical benefit in accordance with the objectives in precision medicine. As a result, there is growing interest in developing statistical tools for estimating optimal ITRs in evidence-based research. In health economic perspectives, policy makers consider the tradeoff between health gains and incremental costs of interventions to set priorities and allocate resources. However, most work on ITRs has focused on maximizing the effectiveness of treatment without considering costs. In this paper, we jointly consider the impact of effectiveness and cost on treatment decisions and define ITRs under a composite-outcome setting, so that we identify the most cost-effective ITR that accounts for individual-level heterogeneity through direct optimization. In particular, we propose a decision-tree-based statistical learning algorithm that uses a net-monetary-benefit-based reward to provide nonparametric estimations of the optimal ITR. We provide several approaches to estimating the reward underlying the ITR as a function of subject characteristics. We present the strengths and weaknesses of each approach and provide practical guidelines by comparing their performance in simulation studies. We illustrate the top-performing approach from our simulations by evaluating the projected 15-year personalized cost-effectiveness of the intensive blood pressure control of the Systolic Blood Pressure Intervention Trial (SPRINT) study.

Tumor-Absorbed Dose Predicts Progression-Free Survival Following (131)I-Tositumomab Radioimmunotherapy.

UNLABELLED: The study aimed at identifying patient-specific dosimetric and nondosimetric factors predicting outcome of non-Hodgkin lymphoma patients after (131)I-tositumomab radioimmunotherapy for potential use in treatment planning. METHODS: Tumor-absorbed dose measures were estimated for 130 tumors in 39 relapsed or refractory non-Hodgkin lymphoma patients by coupling SPECT/CT imaging with the Dose Planning Method (DPM) Monte Carlo code. Equivalent biologic effect was calculated to assess the biologic effects of nonuniform absorbed dose including the effects of the unlabeled antibody. Evaluated nondosimetric covariates included histology, presence of bulky disease, and prior treatment history. Tumor level outcome was based on volume shrinkage assessed on follow-up CT. Patient level outcome measures were overall response (OR), complete response (CR), and progression-free survival (PFS), determined from clinical assessments that included PET/CT. RESULTS: The estimated mean tumor-absorbed dose had a median value of 275 cGy (range, 94-711 cGy). A high correlation was observed between tracer-predicted and therapy-delivered mean tumor-absorbed doses (P < 0.001; r = 0.85). In univariate tumor-level analysis, tumor shrinkage correlated significantly with almost all of the evaluated dosimetric factors, including equivalent biologic effect. Regression analysis showed that OR, CR, and PFS were associated with the dosimetric factors and equivalent biologic effect. Both mean tumor-absorbed dose (P = 0.025) and equivalent biologic effect (P = 0.035) were significant predictors of PFS whereas none of the nondosimetric covariates were found to be statistically significant factors affecting PFS. The most important finding of the study was that in Kaplan-Meier curves stratified by mean dose, longer PFS was observed in patients receiving mean tumor-absorbed doses greater than 200 cGy than in those receiving 200 cGy or less (median PFS, 13.6 vs. 1.9 mo for the 2 dose groups; log-rank P < 0.0001). CONCLUSION: A higher mean tumor-absorbed dose was significantly predictive of improved PFS after (131)I-tositumomab radioimmunotherapy. Hence tumor-absorbed dose, which can be estimated before therapy, can potentially be used to design radioimmunotherapy protocols to improve efficacy.