Phase 2 trial of tremelimumab in patients with metastatic urothelial cancer previously treated with programmed death 1/programmed death ligand 1 blockade.

INTRODUCTION: Programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) blockade has changed the landscape of treatment for metastatic urothelial cancer, but single-agent cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) blockade in metastatic urothelial cancer has been underexplored. A prior phase 2 trial of tremelimumab in PD-1/PD-L1-blockade naive patients with metastatic urothelial cancer revealed activity comparable to that observed with PD-1/PD-L1 blockade raising the hypothesis that these classes of immune checkpoint inhibitors might be non-cross-resistant. METHODS: The current phase 2 trial treated patients with PD-1/PD-L1 blockade-resistant metastatic urothelial cancer with single-agent tremelimumab (750 mg intravenously every 28 days for up to 7 cycles). The primary end point was objective response rate. RESULTS: Twenty-six patients were enrolled and 24 patients were evaluable for response. The objective response rate was 8.3%, composed of a total of two partial responses that lasted 10.9 and 24.0 months. Stable disease was observed in another 20.8% of patients, with a median duration of stable disease of 5.4 months. Diarrhea occurred in 15 patients (58%), elevated hepatic transaminases occurred in seven patients (27%), and adrenal insufficiency occurred in two patients (8%); one patient died after experiencing immune-related hepatitis. CONCLUSIONS: High dose CTLA-4 blockade in patients with PD-1/PD-L1-resistant metastatic urothelial cancer has modest activity and is associated with treatment-related toxicity similar to prior reports.

A Pilot Study of Pembrolizumab in Combination With Y90 Radioembolization in Subjects With Poor Prognosis Hepatocellular Carcinoma.

BACKGROUND: Combination checkpoint inhibition therapy with yttrium-90 (Y90) radioembolization represents an emerging area of interest in the treatment of advanced hepatocellular carcinoma (HCC). HCRN GI15-225 is an open-label, single-arm multicenter, pilot study (NCT03099564). METHODS: Eligible patients had poor prognosis, localized HCC defined as having portal vein thrombus, multifocal disease, and/or diffuse disease that were not eligible for liver transplant or surgical resection. Patients received pembrolizumab 200 mg intravenously every 3 weeks in conjunction with glass yttrium-90 (Y90) radioembolization TheraSphere. Primary endpoint was 6-month progression-free survival (PFS6) per RECIST 1.1. Secondary endpoints included time to progression (TTP), objective response rate (ORR), overall survival (OS), and safety/tolerability. RESULTS: Between October 23, 2017 and November 24, 2020, 29 patients were enrolled: 2 were excluded per protocol. Fifteen of the remaining 27 patients were free of progression at 6 months (55.6%; 95% CI, 35.3-74.5) with median PFS 9.95 months (95% CI, 4.14-15.24) and OS 27.30 months (95% CI, 10.15-39.52). One patient was not evaluable for response due to death; among the remaining 26 patients, ORR was 30.8% (95% CI, 14.3-51.8) and DCR was 84.6% (95% CI, 65.1-95.6). CONCLUSION: In patients with localized, poor prognosis HCC, pembrolizumab in addition to glass Y90 radioembolization demonstrated promising efficacy and safety consistent with prior observations (ClinicalTrials.gov Identifier: NCT03099564; IRB Approved: 16-3255 approved July 12, 2016).

A generalizability score for aggregate causal effect.

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

Risk Factors Associated With Distress Among Postoperative Patients in an Academic Gynecologic Oncology Practice.

BACKGROUND: Distress among gynecologic oncology patients correlates with poor clinical outcomes and decreased quality of life. The purpose of this study was to determine risk factors for elevated NCCN Distress Thermometer (DT) results among postoperative gynecologic oncology patients. PATIENTS AND METHODS: We performed a retrospective chart review of all postoperative visits over a 5-year period. NCCN DT results were analyzed as both discretized values (DT ≤3 = low distress; DT 4-8 = moderate distress; DT ≥9 = high distress) and continuous variables. Patients with a DT score ≥4 were referred to social work. Univariate and multivariate regression analyses were performed to compare NCCN DT results with clinical and sociodemographic variables. Statistical significance was P<.05. RESULTS: In total, 1,795 NCCN DT results were included, with uterine (37.72%) being the most common disease site. Benign pathology was known prior to completion of the NCCN DT in 13.15% of patients. Most patients (71.75%) endorsed low levels of distress. Moderate/High levels of distress were reported by 28.25% of patients. Increasing levels of distress were significantly associated with younger age (P=.006), history of depression (P≤.001), status as a current smoker (P=.028), and history of asthma (P=.041). Knowledge of benign pathology was associated with low levels of distress (P=.002). Procedure type and disease site were not associated with distress. CONCLUSIONS: More than one-fourth of postoperative patients in a gynecologic oncology practice reported moderate or high distress. Distress was highest among those with malignancy regardless of disease site or surgical intervention. Benign pathology correlated with decreased distress. Identified associations with distress provide opportunities for prevention, early intervention, and tailored counseling.

Randomized phase II selection design with order constrained strata.

The exploratory nature of phase II trials makes it quite common to include heterogeneous patient subgroups with different prognoses in the same trial. Incorporating such patient heterogeneity or stratification into statistical calculation for sample size can improve efficiency and reduce sample sizes in single-arm phase II trials with binary outcomes. However, such consideration is lacking in randomized phase II trials. In this paper, we propose methods that can utilize some natural order constraints that may exist in stratified population to gain statistical efficiency for randomized phase II designs. For thoroughness and simplicity, we focus on the randomized phase II selection designs in this paper, although our method can be easily generalized to the randomized phase II screening designs. We consider both binary and time-to-event outcomes in our development. Compared with methods that do not use order constraints, our method is shown to improve the probabilities of correct selection or reduce sample size in our simulation and real examples.

The influence of anatomic stage and receptor status on first recurrence for breast cancer within 5 years (AFT-01).

BACKGROUND: Risk-stratified follow-up guidelines that account for the absolute risk and timing of recurrence may improve the quality and efficiency of breast cancer follow-up. The objective of this study was to assess the relationship of anatomic stage and receptor status with timing of the first recurrence for patients with local-regional breast cancer and generate risk-stratified follow-up recommendations. METHODS: The authors conducted a secondary analysis of 8007 patients with stage I-III breast cancer who enrolled in nine Alliance legacy clinical trials from 1997 to 2013 (ClinicalTrials.gov identifier NCT02171078). Patients who received standard-of-care therapy were included. Patients who were missing stage or receptor status were excluded. The primary outcome was days from the earliest treatment start date to the date of first recurrence. The primary explanatory variable was anatomic stage. The analysis was stratified by receptor type. Cox proportional-hazards regression models produced cumulative probabilities of recurrence. A dynamic programming algorithm approach was used to optimize the timing of follow-up intervals based on the timing of recurrence events. RESULTS: The time to first recurrence varied significantly between receptor types (p < .0001). Within each receptor type, stage influenced the time to recurrence (p < .0001). The risk of recurrence was highest and occurred earliest for estrogen receptor (ER)-negative/progesterone receptor (PR)-negative/Her2neu-negative tumors (stage III; 5-year probability of recurrence, 45.5%). The risk of recurrence was lower for ER-positive/PR-positive/Her2neu-positive tumors (stage III; 5-year probability of recurrence, 15.3%), with recurrences distributed over time. Model-generated follow-up recommendations by stage and receptor type were created. CONCLUSIONS: This study supports considering both anatomic stage and receptor status in follow-up recommendations. The implementation of risk-stratified guidelines based on these data has the potential to improve the quality and efficiency of follow-up.

Local/Regional Recurrence Rates After Breast-Conserving Therapy in Patients Enrolled in Legacy Trials of the Alliance for Clinical Trials in Oncology (AFT-01).

OBJECTIVE: We sought to evaluate local/regional recurrence rates after breast-conserving surgery in a cohort of patients enrolled in legacy trials of the Alliance for Clinical Trials in Oncology and to evaluate variation in recurrence rates by receptor subtype. BACKGROUND: Multiple randomized controlled trials have demonstrated equivalent survival between breast conservation and mastectomy, albeit with higher local/regional recurrence rates after breast conservation. However, absolute rates of local/regional recurrence have been declining with multi-modality treatment. METHODS: Data from 5 Alliance for Clinical Trials in Oncology legacy trials that enrolled women diagnosed with breast cancer between 1997 and 2010 were included. Women who underwent breast-conserving surgery and standard systemic therapies (n=4,404) were included. Five-year rates of local/regional recurrence were estimated from Kaplan-Meier curves. Patients were censored at the time of distant recurrence (if recorded as the first recurrence), death, or last follow-up. Multivariable Cox proportional hazards models were used to identify factors associated with time to local/regional recurrence, including patient age, tumor size, lymph node status, and receptor subtype. RESULTS: Overall 5-year recurrence was 4.6% (95% CI=4.0-5.4%). Five-year recurrence rates were lowest in those with ER+ or PR+ tumors (Her2+ 3.4% [95% CI 2.0-5.7%], Her2- 4.0% [95% CI 3.2-4.9%]) and highest in the triple-negative subtype (7.1% [95% CI 5.4-9.3%]). On multivariable analysis, increasing nodal involvement and triple-negative subtype were positively associated with recurrence ( P <0.0001). CONCLUSIONS: Rates of local/regional recurrence after breast conservation in women with breast cancer enrolled in legacy trials of the Alliance for Clinical Trials in Oncology are significantly lower than historic estimates. This data can better inform patient discussions and surgical decision-making.

Structure-preserving integrated analysis for risk stratification with application to cancer staging.

To provide appropriate and practical level of health care, it is critical to group patients into relatively few strata that have distinct prognosis. Such grouping or stratification is typically based on well-established risk factors and clinical outcomes. A well-known example is the American Joint Committee on Cancer staging for cancer that uses tumor size, node involvement, and metastasis status. We consider a statistical method for such grouping based on individual patient data from multiple studies. The method encourages a common grouping structure as a basis for borrowing information, but acknowledges data heterogeneity including unbalanced data structures across multiple studies. We build on the "lasso-tree" method that is more versatile than the well-known classification and regression tree method in generating possible grouping patterns. In addition, the parametrization of the lasso-tree method makes it very natural to incorporate the underlying order information in the risk factors. In this article, we also strengthen the lasso-tree method by establishing its theoretical properties for which Lin and others (2013. Lasso tree for cancer staging with survival data. Biostatistics 14, 327-339) did not pursue. We evaluate our method in extensive simulation studies and an analysis of multiple breast cancer data sets.

Relative contrast estimation and inference for treatment recommendation.

When there are resource constraints, it may be necessary to rank individualized treatment benefits to facilitate the prioritization of assigning different treatments. Most existing literature on individualized treatment rules targets absolute conditional treatment effect differences as a metric for the benefit. However, there can be settings where relative differences may better represent such benefit. In this paper, we consider modeling such relative differences formed as scale-invariant contrasts between the conditional treatment effects. By showing that all scale-invariant contrasts are monotonic transformations of each other, we posit a single index model for a particular relative contrast. We then characterize semiparametric estimating equations, including the efficient score, to estimate index parameters. To achieve semiparametric efficiency, we propose a two-step approach that minimizes a doubly robust loss function for initial estimation and then performs a one-step efficiency augmentation procedure. Careful theoretical and numerical studies are provided to show the superiority of our proposed approach.

Entropy balancing for causal generalization with target sample summary information.

In this paper, we focus on estimating the average treatment effect (ATE) of a target population when individual-level data from a source population and summary-level data (e.g., first or second moments of certain covariates) from the target population are available. In the presence of the heterogeneous treatment effect, the ATE of the target population can be different from that of the source population when distributions of treatment effect modifiers are dissimilar in these two populations, a phenomenon also known as covariate shift. Many methods have been developed to adjust for covariate shift, but most require individual covariates from a representative target sample. We develop a weighting approach based on the summary-level information from the target sample to adjust for possible covariate shift in effect modifiers. In particular, weights of the treated and control groups within a source sample are calibrated by the summary-level information of the target sample. Our approach also seeks additional covariate balance between the treated and control groups in the source sample. We study the asymptotic behavior of the corresponding weighted estimator for the target population ATE under a wide range of conditions. The theoretical implications are confirmed in simulation studies and a real-data application.

Studentized permutation method for comparing two restricted mean survival times with small sample from randomized trials.

Recent observations, especially in cancer immunotherapy clinical trials with time-to-event outcomes, show that the commonly used proportional hazard assumption is often not justifiable, hampering an appropriate analysis of the data by hazard ratios. An attractive alternative advocated is given by the restricted mean survival time (RMST), which does not rely on any model assumption and can always be interpreted intuitively. Since methods for the RMST based on asymptotic theory suffer from inflated type-I error under small sample sizes, a permutation test was proposed recently leading to more convincing results in simulations. However, classical permutation strategies require an exchangeable data setup between comparison groups which may be limiting in practice. Besides, it is not possible to invert related testing procedures to obtain valid confidence intervals, which can provide more in-depth information. In this paper, we address these limitations by proposing a studentized permutation test as well as respective permutation-based confidence intervals. In an extensive simulation study, we demonstrate the advantage of our new method, especially in situations with relatively small sample sizes and unbalanced groups. Finally, we illustrate the application of the proposed method by re-analyzing data from a recent lung cancer clinical trial.

Smoking Status, Nicotine Medication, Vaccination, and COVID-19 Hospital Outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) Study.

INTRODUCTION: Available evidence is mixed concerning associations between smoking status and COVID-19 clinical outcomes. Effects of nicotine replacement therapy (NRT) and vaccination status on COVID-19 outcomes in smokers are unknown. METHODS: Electronic health record data from 104 590 COVID-19 patients hospitalized February 1, 2020 to September 30, 2021 in 21 U.S. health systems were analyzed to assess associations of smoking status, in-hospital NRT prescription, and vaccination status with in-hospital death and ICU admission. RESULTS: Current (n = 7764) and never smokers (n = 57 454) did not differ on outcomes after adjustment for age, sex, race, ethnicity, insurance, body mass index, and comorbidities. Former (vs never) smokers (n = 33 101) had higher adjusted odds of death (aOR, 1.11; 95% CI, 1.06-1.17) and ICU admission (aOR, 1.07; 95% CI, 1.04-1.11). Among current smokers, NRT prescription was associated with reduced mortality (aOR, 0.64; 95% CI, 0.50-0.82). Vaccination effects were significantly moderated by smoking status; vaccination was more strongly associated with reduced mortality among current (aOR, 0.29; 95% CI, 0.16-0.66) and former smokers (aOR, 0.47; 95% CI, 0.39-0.57) than for never smokers (aOR, 0.67; 95% CI, 0.57, 0.79). Vaccination was associated with reduced ICU admission more strongly among former (aOR, 0.74; 95% CI, 0.66-0.83) than never smokers (aOR, 0.87; 95% CI, 0.79-0.97). CONCLUSIONS: Former but not current smokers hospitalized with COVID-19 are at higher risk for severe outcomes. SARS-CoV-2 vaccination is associated with better hospital outcomes in COVID-19 patients, especially current and former smokers. NRT during COVID-19 hospitalization may reduce mortality for current smokers. IMPLICATIONS: Prior findings regarding associations between smoking and severe COVID-19 disease outcomes have been inconsistent. This large cohort study suggests potential beneficial effects of nicotine replacement therapy on COVID-19 outcomes in current smokers and outsized benefits of SARS-CoV-2 vaccination in current and former smokers. Such findings may influence clinical practice and prevention efforts and motivate additional research that explores mechanisms for these effects.

Nf1-dependent tumors require a microenvironment containing Nf1+/-- and c-kit-dependent bone marrow.

Interactions between tumorigenic cells and their surrounding microenvironment are critical for tumor progression yet remain incompletely understood. Germline mutations in the NF1 tumor suppressor gene cause neurofibromatosis type 1 (NF1), a common genetic disorder characterized by complex tumors called neurofibromas. Genetic studies indicate that biallelic loss of Nf1 is required in the tumorigenic cell of origin in the embryonic Schwann cell lineage. However, in the physiologic state, Schwann cell loss of heterozygosity is not sufficient for neurofibroma formation and Nf1 haploinsufficiency in at least one additional nonneoplastic lineage is required for tumor progression. Here, we establish that Nf1 heterozygosity of bone marrow-derived cells in the tumor microenvironment is sufficient to allow neurofibroma progression in the context of Schwann cell Nf1 deficiency. Further, genetic or pharmacologic attenuation of c-kit signaling in Nf1+/- hematopoietic cells diminishes neurofibroma initiation and progression. Finally, these studies implicate mast cells as critical mediators of tumor initiation.

Clinical utility of a regional precision medicine molecular tumor board and challenges to implementation.

PURPOSE: Molecular tumor boards provide precision treatment recommendations based on cancer genomic profile. However, practical barriers limit their benefits. We studied the clinical utility of the precision medicine molecular tumor board (PMMTB) and described challenges with PMMTB implementation. METHODS: An observational cohort study included patients reviewed by the PMMTB between September 2015 to December 2017. Patients who had consented to the registry study were included. The primary endpoint of this study was time on treatment (ToT) ratio. Clinical utility was established if the primary endpoint had least 15% of patients achieving a ToT ratio of ≥1.3. RESULTS: Overall, 278 patients were presented to the PMMTB and 113 cases were included in the final analysis. The PMMTB identified at least one nonstandard of care (SOC) clinically actionable mutation for 69.0% (78/113) of cases. In patients who received non-SOC treatment, 43.8% (7/16) achieved a ToT ratio of 1.3 or more (p < 0.001). Fifty-nine patients did not receive non-SOC recommendations. Reasons for not pursuing treatment included 35.6% having response to current treatment, 20.3% died prior to starting or considering PMMTB recommendations, 13.6% pursued other treatment options based on clinician discretion, another 10.2% pursued other treatment options because clinical trials recommended were not geographically accessible, 8.5% had rapid decline of performance status, 6.8% lacked of financial support for treatment, and 5.1% were excluded from clinical trials due to abnormal laboratory values. CONCLUSION: The regional PMMTB non-SOC recommendations benefitted a majority of patients and additional processes were implemented to assist with non-SOC treatment accessibility.

Adjuvant immunotherapy in patients with high-risk muscle-invasive urothelial carcinoma: The potential impact of informative censoring.

BACKGROUND: The results of 2 studies exploring adjuvant immune checkpoint inhibition (aCPI) in high-risk muscle-invasive urothelial cancer have yielded conflicting results. A trial employing placebo as the control arm demonstrated a significant prolongation in disease-free survival (DFS) whereas a trial employing observation as the control arm (IMvigor010) demonstrated no prolongation in DFS with CPI. Here, the authors aimed to estimate the aCPI benefit and to model the potential impact of informative censoring on trial results. METHODS: Survival data from 1518 patients was reconstructed from Kaplan-Meier curves. A network meta-analysis approach was used to estimate aCPI benefit through the restricted mean disease-free survival time (RMDFST). To estimate the potential impact of informative censoring on IMvigor010, a simulation was performed. The minimum proportion of informative censoring on the observation arm that could account for the lack of observed improvement in DFS was estimated. Random variability from the time of censoring to progression was modeled using the exponential distribution. RESULTS: Patients receiving aCPI had better DFS: ΔRMDFST at 36 months of 2.2 (95% CI, 0.6-3.7, P = .006) months relative to observation/placebo. In IMvigor010, in the observation arm, 20.5% of patients were censored due to consent withdrawal, protocol violation and/or noncompliance, or lost to follow-up versus 8.2% in the treatment arm. On simulation, it was found that the lack of observed improvement in DFS could have resulted from as few as 14% of the censored patients on observation arm not being censored at random (simulated DFS with 14% informative censoring hazard ratio, 0.83; 95% CI, 0.69-0.99; P = .049). CONCLUSIONS: Phase 3 trials comparing adjuvant therapies to observation are at risk for informative censoring that could potentially impact interpretation of study results.

Sufficient dimension reduction for populations with structured heterogeneity.

A key challenge in building effective regression models for large and diverse populations is accounting for patient heterogeneity. An example of such heterogeneity is in health system risk modeling efforts where different combinations of comorbidities fundamentally alter the relationship between covariates and health outcomes. Accounting for heterogeneity arising combinations of factors can yield more accurate and interpretable regression models. Yet, in the presence of high-dimensional covariates, accounting for this type of heterogeneity can exacerbate estimation difficulties even with large sample sizes. To handle these issues, we propose a flexible and interpretable risk modeling approach based on semiparametric sufficient dimension reduction. The approach accounts for patient heterogeneity, borrows strength in estimation across related subpopulations to improve both estimation efficiency and interpretability, and can serve as a useful exploratory tool or as a powerful predictive model. In simulated examples, we show that our approach often improves estimation performance in the presence of heterogeneity and is quite robust to deviations from its key underlying assumptions. We demonstrate our approach in an analysis of hospital admission risk for a large health system and demonstrate its predictive power when tested on further follow-up data.

Which test for crossing survival curves? A user's guideline.

BACKGROUND: The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards. METHODS: We restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p-values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards. RESULTS: We reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible. CONCLUSIONS: The evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test.

Impactability Modeling for Reducing Medicare Accountable Care Organization Payments and Hospital Events in High-Need High-Cost Patients: Longitudinal Cohort Study.

BACKGROUND: Impactability modeling promises to help solve the nationwide crisis in caring for high-need high-cost patients by matching specific case management programs with patients using a "benefit" or "impactability" score, but there are limitations in tailoring each model to a specific program and population. OBJECTIVE: We evaluated the impact on Medicare accountable care organization savings from developing a benefit score for patients enrolled in a historic case management program, prospectively implementing the score, and evaluating the results in a new case management program. METHODS: We conducted a longitudinal cohort study of 76,140 patients in a Medicare accountable care organization with multiple before-and-after measures of the outcome, using linked electronic health records and Medicare claims data from 2012 to 2019. There were 489 patients in the historic case management program, with 1550 matched comparison patients, and 830 patients in the new program, with 2368 matched comparison patients. The historic program targeted high-risk patients and assigned a centrally located registered nurse and social worker to each patient. The new program targeted high- and moderate-risk patients and assigned a nurse physically located in a primary care clinic. Our primary outcomes were any unplanned hospital events (admissions, observation stays, and emergency department visits), count of event-days, and Medicare payments. RESULTS: In the historic program, as expected, high-benefit patients enrolled in case management had fewer events, fewer event-days, and an average US $1.15 million reduction in Medicare payments per 100 patients over the subsequent year when compared with the findings in matched comparison patients. For the new program, high-benefit high-risk patients enrolled in case management had fewer events, while high-benefit moderate-risk patients enrolled in case management did not differ from matched comparison patients. CONCLUSIONS: Although there was evidence that a benefit score could be extended to a new case management program for similar (ie, high-risk) patients, there was no evidence that it could be extended to a moderate-risk population. Extending a score to a new program and population should include evaluation of program outcomes within key subgroups. With increased attention on value-based care, policy makers and measure developers should consider ways to incorporate impactability modeling into program design and evaluation.

Impact of initial chemotherapy regimen on outcomes for patients with double-expressor lymphoma: A multi-center analysis.

Diffuse large B-cell lymphoma featuring overexpression of MYC and B-Cell Lymphoma 2 (double expressor lymphoma, DEL) is associated with poor outcomes. Existing evidence suggesting improved outcomes for DEL with the use of more intensive regimens than R-CHOP is restricted to younger patients and based on limited evidence from low patient numbers. We retrospectively evaluated the impact of intensive frontline regimens versus R-CHOP in a multicenter analysis across 7 academic medical centers in the United States. We collected 90 cases of DEL, 46 out of 90 patients (51%) received R-CHOP and 44/90 (49%) received an intensive regimen, which was predominantly DA-EPOCH-R. Treatment cohorts were evenly balanced for demographics and disease characteristics, though the intensive group had a higher lactate dehydrogenase (LDH, 326 vs. 230 U/L p = 0.06) and presence of B-symptoms (50% vs. 22%, p = 0.01) compared to the R-CHOP cohort. There was no difference in PFS (median 53 vs. 38 months, p = 0.49) or overall survival (67 vs. not reached months, p = 0.14) between the R-CHOP and intensive therapy cohorts, respectively. On multivariate analysis, intensive therapy was associated with a hazard ratio of 2.35 (95% CI 0.74-7.41), though this was not statistically significant. Additionally, a subgroup analysis of intermediate high-risk lymphoma defined by IPI ≥3 did not identify a difference in survival outcomes between regimens. We conclude that in our multi-center cohort there is no evidence supporting the use of intensive regimens over R-CHOP, suggesting that R-CHOP remains the standard of care for treating DEL.

Tailored optimal posttreatment surveillance for cancer recurrence.

A substantial rise in the number of cancer survivors has led to urgent management questions regarding effective posttreatment imaging-based surveillance strategies for cancer recurrence. Current surveillance guidelines provided by a number of professional societies all warn against overly aggressive surveillance, especially for low-risk patients, but all fail to provide more specific directions to accommodate underlying heterogeneity of cancer recurrence. Therefore it is imperative to develop data-driven strategies that can tailor the surveillance schedules to recurrence risk in this era of stricter insurance regulations, provider shortages, and rising costs of health care. Due to a lack of statistical methods for optimizing surveillance scheduling in presence of competing risks, we propose a general approach that uses an intuitive loss function for optimization of early detection of recurrence before death. The proposed strategies can tailor to patient risks of recurrence, in terms of both intensity and amount of surveillance. Using general three-state Markov models, our method is flexible and includes earlier works as special cases. We illustrate our method in both simulation studies and an application to breast cancer surveillance.