A Randomized Trial of Two Remote Health Care Delivery Models on the Uptake of Genetic Testing and Impact on Patient-Reported Psychological Outcomes in Families With Pancreatic Cancer: The Genetic Education, Risk Assessment, and Testing (GENERATE) Study.

BACKGROUND & AIMS: Genetic testing uptake for cancer susceptibility in family members of patients with cancer is suboptimal. Among relatives of patients with pancreatic ductal adenocarcinoma (PDAC), The GENetic Education, Risk Assessment, and TEsting (GENERATE) study evaluated 2 online genetic education/testing delivery models and their impact on patient-reported psychological outcomes. METHODS: Eligible participants had ≥1 first-degree relative with PDAC, or ≥1 first-/second-degree relative with PDAC with a known pathogenic germline variant in 1 of 13 PDAC predisposition genes. Participants were randomized by family, between May 8, 2019, and June 1, 2021. Arm 1 participants underwent a remote interactive telemedicine session and online genetic education. Arm 2 participants were offered online genetic education only. All participants were offered germline testing. The primary outcome was genetic testing uptake, compared by permutation tests and mixed-effects logistic regression models. We hypothesized that Arm 1 participants would have a higher genetic testing uptake than Arm 2. Validated surveys were administered to assess patient-reported anxiety, depression, and cancer worry at baseline and 3 months postintervention. RESULTS: A total of 424 families were randomized, including 601 participants (n = 296 Arm 1; n = 305 Arm 2), 90% of whom completed genetic testing (Arm 1 [87%]; Arm 2 [93%], P = .014). Arm 1 participants were significantly less likely to complete genetic testing compared with Arm 2 participants (adjusted ratio [Arm1/Arm2] 0.90, 95% confidence interval 0.78-0.98). Among participants who completed patient-reported psychological outcomes questionnaires (Arm 1 [n = 194]; Arm 2 [n = 206]), the intervention did not affect mean anxiety, depression, or cancer worry scores. CONCLUSIONS: Remote genetic education and testing can be a successful and complementary option for delivering genetics care. (Clinicaltrials.gov, number NCT03762590).

Association of Oncologist Participation in Medicare's Oncology Care Model With Patient Receipt of Novel Cancer Therapies.

Importance: Medicare's Oncology Care Model (OCM) was an alternative payment model that tied performance-based payments to cost and quality goals for participating oncology practices. A major concern about the OCM regarded inclusion of high-cost cancer therapies, which could potentially disincentivize oncologists from prescribing novel therapies. Objective: To examine whether oncologist participation in the OCM changed the likelihood that patients received novel therapies vs alternative treatments. Design, Setting, and Participants: This cohort study of Surveillance, Epidemiology, and End Results (SEER) Program data and Medicare claims compared patient receipt of novel therapies for patients treated by oncologists participating vs not participating in the OCM in the period before (January 2015-June 2016) and after (July 2016-December 2018) OCM initiation. Participants included Medicare fee-for-service beneficiaries in SEER registries who were eligible to receive 1 of 10 novel cancer therapies that received US Food and Drug Administration approval in the 18 months before implementation of the OCM. The study excluded the Hawaii registry because complete data were not available at the time of the data request. Patients in the OCM vs non-OCM groups were matched on novel therapy cohort, outcome time period, and oncologist specialist status. Analysis was conducted between July 2021 and April 2022. Exposures: Oncologist participation in the OCM. Main Outcomes and Measures: Preplanned analyses evaluated patient receipt of 1 of 10 novel therapies vs alternative therapies specific to the patient's cancer for the overall study sample and for racial subgroups. Results: The study included 2839 matched patients (760 in the OCM group and 2079 in the non-OCM group; median [IQR] age, 72.7 [68.3-77.6] years; 1591 women [56.0%]). Among patients in the non-OCM group, 33.2% received novel therapies before and 40.1% received novel therapies after the start of the OCM vs 39.9% and 50.3% of patients in the OCM group (adjusted difference-in-differences, 3.5 percentage points; 95% CI, -3.7 to 10.7 percentage points; P = .34). In subgroup analyses, second-line immunotherapy use in lung cancer was greater among patients in the OCM group vs non-OCM group (adjusted difference-in-differences, 17.4 percentage points; 95% CI, 4.8-30.0 percentage points; P = .007), but no differences were seen in other subgroups. Over the entire study period, patients with oncologists participating in the OCM were more likely to receive novel therapies than those with oncologists who were not participating (odds ratio, 1.47; 95% CI, 1.09-1.97; P = .01). Conclusions and Relevance: This study found that participation in the OCM was not associated with oncologists' prescribing novel therapies to Medicare beneficiaries with cancer. These findings suggest that OCM financial incentives did not decrease patient access to novel therapies.

Development and Validation of the PREMMplus Model for Multigene Hereditary Cancer Risk Assessment.

PURPOSE: With the availability of multigene panel testing (MGPT) for hereditary cancer risk assessment, clinicians need to assess the likelihood of pathogenic germline variants (PGVs) across numerous genes in parallel. This study's aim was to develop and validate a clinical prediction model (PREMMplus) for MGPT risk assessment. MATERIALS AND METHODS: PREMMplus was developed in a single-institution cohort of 7,280 individuals who had undergone MGPT. Logistic regression models with Least Absolute Shrinkage and Selection Operator regularization were used to examine candidate predictors (age, sex, ethnicity, and personal/family history of 18 cancers/neoplasms) to estimate one's likelihood of carrying PGVs in 19 genes (broadly categorized by phenotypic overlap and/or relative penetrance: 11 category A [APC, BRCA1/2, CDH1, EPCAM, MLH1, MSH2, MSH6, biallelic MUTYH, PMS2, and TP53] and eight category B genes [ATM, BRIP1, CDKN2A, CHEK2, PALB2, PTEN, RAD51C, and RAD51D]). Model performance was validated in nonoverlapping data sets of 8,691 and 14,849 individuals with prior MGPT ascertained from clinic- and laboratory-based settings, respectively. RESULTS: PREMMplus (score ≥ 2.5%) had 93.9%, 91.7%, and 89.3% sensitivity and 98.3%, 97.5%, and 97.8% negative-predictive value (NPV) for identifying category A gene PGV carriers in the development and validation cohorts, respectively. PREMMplus assessment (score ≥ 2.5%) had 89.9%, 85.6%, and 84.2% sensitivity and 95.0%, 93.5%, and 93.5% NPV, respectively, for identifying category A/B gene PGV carriers. Decision curve analyses support MGPT for individuals predicted to have ≥ 2.5% probability of a PGV. CONCLUSION: PREMMplus accurately identifies individuals with PGVs in a diverse spectrum of cancer susceptibility genes with high sensitivity/NPV. Individuals with PREMMplus scores ≥ 2.5% should be considered for MGPT.

Patterns of Medical Care Cost by Service Type for Patients With Recurrent and De Novo Advanced Cancer.

OBJECTIVES: There is limited knowledge about the cost patterns of patients who receive a diagnosis of de novo and recurrent advanced cancers in the United States. METHODS: Data on patients who received a diagnosis of de novo stage IV or recurrent breast, colorectal, or lung cancer between 2000 and 2012 from 3 integrated health systems were used to estimate average annual costs for total, ambulatory, inpatient, medication, and other services during (1) 12 months preceding de novo or recurrent diagnosis (preindex) and (2) diagnosis month through 11 months after (postindex), from the payer perspective. Generalized linear regression models estimated costs adjusting for patient and clinical factors. RESULTS: Patients who developed a recurrence <1 year after their initial cancer diagnosis had significantly higher total costs in the preindex period than those with recurrence ≥1 year after initial diagnosis and those with de novo stage IV disease across all cancers (all P < .05). Patients with de novo stage IV breast and colorectal cancer had significantly higher total costs in the postindex period than patients with cancer recurrent in <1 year and ≥1 year (all P < .05), respectively. Patients in de novo stage IV and those with recurrence in ≥1 year experienced significantly higher postindex costs than the preindex period (all P < .001). CONCLUSIONS: Our findings reveal distinct cost patterns between patients with de novo stage IV, recurrent <1-year, and recurrent ≥1-year cancer, suggesting unique care trajectories that may influence resource use and planning. Future cost studies among patients with advanced cancer should account for de novo versus recurrent diagnoses and timing of recurrence to obtain estimates that accurately reflect these care pattern complexities.

Geospatial Disparities in the Treatment of Curable Breast Cancer Across the US.

IMPORTANCE: Patient factors help explain disparities in breast cancer treatments and outcomes. OBJECTIVE: To determine the extent to which geospatial variation in initial breast cancer care can be attributed to region vs patient factors with the aim of guiding quality improvement efforts. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective population-based cohort study from January 1, 2007, through December 31, 2016, using the Surveillance, Epidemiology, and End Results (SEER)-Medicare database that included 31 571 patients diagnosed with stage I to III breast cancer from 2007 through 2013. Five metrics of care delivery were defined: stage I at diagnosis, chemotherapy receipt, radiation therapy receipt, endocrine therapy (ET) initiation (year 1), and ET continuation (years 3-5). Data analysis was performed from January to June 2021. EXPOSURES: Stage I diagnosis and treatment with chemotherapy, radiation therapy, or ET. MAIN OUTCOMES AND MEASURES: For each metric, total variance was attributed proportionally to 4 domains-random, patient factors (eg, age, race and ethnicity, socioeconomic status), region (health service area [HSA]), and unexplained-using hierarchical multivariable modeling. RESULTS: Of 31 571 total patients (median [IQR] age, 71 [68-75] years), 19 391 (61.4%) had stage I disease at diagnosis. Among eligible patients, 17 297 of 21 190 (81.6%) received radiation therapy, 7204 of 9903 (72.8%) received chemotherapy, 13 115 of 26 855 (48.8%) initiated ET, and 13 944 of 26 855 (52.1%) continued ET. Geospatial density (ie, heat) maps highlight regional performance patterns. For all 5 metrics, region/HSA explained more observed variation (24%-48%) than patient factors (1%-4%); the largest share of variation was unexplained (35%-54%). The metrics with the largest proportion of total variance attributed to region/HSA were ET initiation and continuation (28% and 39%, respectively). CONCLUSIONS AND RELEVANCE: In this cohort study, there was substantial unexplained geospatial variation in initial breast cancer care. The variance attributed to region/HSA was multifold larger than that explained by patient factors. The importance of patient factors such as race and ethnicity notwithstanding, future quality improvement efforts should focus on reducing unwarranted geospatial variation, especially including optimizing the delivery of ET in low-performing regions.

Comparison of Colorectal and Endometrial Microsatellite Instability Tumor Analysis and Premm5 Risk Assessment for Predicting Pathogenic Germline Variants on Multigene Panel Testing.

PURPOSE: Tumor testing for microsatellite instability and/or mismatch repair-deficiency (MSI/IHC) and clinical prediction models effectively screen for Lynch syndrome (LS)-associated colorectal cancer (CRC) and endometrial cancer (EC), but they have not been assessed for their ability to identify non-LS forms of inherited risk. The aim of this study was to compare MSI/IHC and the PREMM5 prediction model to identify carriers of LS and non-LS pathogenic variants (PVs) among patients with CRC and EC. PATIENTS AND METHODS: Data were retrospectively analyzed from two single-institution cohorts: 706 patients with CRC and/or EC referred for genetic evaluation/testing (high-risk cohort) and 1,058 consecutively ascertained patients with CRC (oncology clinic cohort), unselected for familial risk. All participants underwent germline multigene panel testing. PREMM5 scores were calculated from personal/family cancer history. The primary outcome was the proportion of individuals with germline PVs (LS PVs, high-penetrance PVs, and any PVs) who had abnormal MSI/IHC testing and/or PREMM5 score ≥ 2.5%. RESULTS: MSI/IHC and PREMM5 had comparable sensitivity for identifying LS carriers in high-risk (89.3% v 85.7%; P = .712) and oncology clinic patients (96.6% v 96.6%; P = 1.000), although MSI/IHC had significantly superior specificity for LS (81.3% v 20.1%; P < .001; 92.3% v 24.3%; P < .001). In both cohorts, PREMM5 had superior sensitivity to MSI/IHC at identifying patients with any high-penetrance PVs and any low-, moderate-, and high-penetrance PVs. Among patients with normal MSI/IHC, PREMM5 identified 84.2% and 83.3% of high-risk patients with CRC/EC and oncology clinic CRC patients with high-penetrance PVs, respectively. CONCLUSION: MSI/IHC and PREMM5 effectively identify patients with CRC and/or EC with LS, although MSI/IHC has better specificity for LS. Because PREMM5 identifies non-LS, high-penetrance germline PVs, patients with CRC and/or EC with PREMM5 score ≥ 2.5%, including those with normal MSI/IHC, should be offered multigene panel testing.

Spending for Advanced Cancer Diagnoses: Comparing Recurrent Versus De Novo Stage IV Disease.

PURPOSE: Spending for patients with advanced cancer is substantial. Past efforts to characterize this spending usually have not included patients with recurrence (who may differ from those with de novo stage IV disease) or described which services drive spending. METHODS: Using SEER-Medicare data from 2008 to 2013, we identified patients with breast, colorectal, and lung cancer with either de novo stage IV or recurrent advanced cancer. Mean spending/patient/month (2012 US dollars) was estimated from 12 months before to 11 months after diagnosis for all services and by the type of service. We describe the absolute difference in mean monthly spending for de novo versus recurrent patients, and we estimate differences after controlling for type of advanced cancer, year of diagnosis, age, sex, comorbidity, and other factors. RESULTS: We identified 54,982 patients with advanced cancer. Before diagnosis, mean monthly spending was higher for recurrent patients (absolute difference: breast, $1,412; colorectal, $3,002; lung, $2,805; all P < .001), whereas after the diagnosis, it was higher for de novo patients (absolute difference: breast, $2,443; colorectal, $4,844; lung, $2,356; all P < .001). Spending differences were driven by inpatient, physician, and hospice services. Across the 2-year period around the advanced cancer diagnosis, adjusted mean monthly spending was higher for de novo versus recurrent patients (spending ratio: breast, 2.39 [95% CI, 2.05 to 2.77]; colorectal, 2.64 [95% CI, 2.31 to 3.01]; lung, 1.46 [95% CI, 1.30 to 1.65]). CONCLUSION: Spending for de novo cancer was greater than spending for recurrent advanced cancer. Understanding the patterns and drivers of spending is necessary to design alternative payment models and to improve value.

Medical Care Costs for Recurrent versus De Novo Stage IV Cancer by Age at Diagnosis.

OBJECTIVE: To address the knowledge gap regarding medical care costs for advanced cancer patients, we compared costs for recurrent versus de novo stage IV breast, colorectal, and lung cancer patients. DATA SOURCES/STUDY SETTING: Virtual Data Warehouse (VDW) information from three Kaiser Permanente regions: Colorado, Northwest, and Washington. STUDY DESIGN: We identified patients aged ≥21 with de novo or recurrent breast (nde novo  = 352; nrecurrent  = 765), colorectal (nde novo  = 1,072; nrecurrent  = 542), and lung (nde novo  = 4,041; nrecurrent  = 340) cancers diagnosed 2000-2012. We estimated average total monthly and annual costs in the 12 months preceding, month of, and 12 months following the index de novo/recurrence date, stratified by age at diagnosis (<65, ≥65). Generalized linear repeated-measures models controlled for demographics and comorbidity. PRINCIPAL FINDINGS: In the pre-index period, monthly costs were higher for recurrent than for de novo breast (<65: +$2,431; ≥65: +$1,360), colorectal (<65: +$3,219; ≥65: +$2,247), and lung cancer (<65: +$3,086; ≥65: +$2,260) patients. Conversely, during the index and post-index periods, costs were higher for de novo patients. Average total annual pre-index costs were five- to ninefold higher for recurrent versus de novo patients <65. CONCLUSIONS: Cost differences by type of advanced cancer and by age suggest heterogeneous patterns of care that merit further investigation.

Development, Validation, and Dissemination of a Breast Cancer Recurrence Detection and Timing Informatics Algorithm.

Background: This study developed, validated, and disseminated a generalizable informatics algorithm for detecting breast cancer recurrence and timing using a gold standard measure of recurrence coupled with data derived from a readily available common data model that pools health insurance claims and electronic health records data. Methods: The algorithm has two parts: to detect the presence of recurrence and to estimate the timing of recurrence. The primary data source was the Cancer Research Network Virtual Data Warehouse (VDW). Sixteen potential indicators of recurrence were considered for model development. The final recurrence detection and timing models were determined, respectively, by maximizing the area under the ROC curve (AUROC) and minimizing average absolute error. Detection and timing algorithms were validated using VDW data in comparison with a gold standard recurrence capture from a third site in which recurrences were validated through chart review. Performance of this algorithm, stratified by stage at diagnosis, was compared with other published algorithms. All statistical tests were two-sided. Results: Detection model AUROCs were 0.939 (95% confidence interval [CI] = 0.917 to 0.955) in the training data set (n = 3370) and 0.956 (95% CI = 0.944 to 0.971) and 0.900 (95% CI = 0.872 to 0.928), respectively, in the two validation data sets (n = 3370 and 3961, respectively). Timing models yielded average absolute prediction errors of 12.6% (95% CI = 10.5% to 14.5%) in the training data and 11.7% (95% CI = 9.9% to 13.5%) and 10.8% (95% CI = 9.6% to 12.2%) in the validation data sets, respectively, and were statistically significantly lower by 12.6% (95% CI = 8.8% to 16.5%, P < .001) than those estimated using previously reported timing algorithms. Similar covariates were included in both detection and timing algorithms but differed substantially from previous studies. Conclusions: Valid and reliable detection of recurrence using data derived from electronic medical records and insurance claims is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for breast cancer patients and those who develop recurrence.

Determining the Time of Cancer Recurrence Using Claims or Electronic Medical Record Data.

PURPOSE: Data from claims and electronic medical records (EMRs) are frequently used to identify clinical events (eg, cancer diagnosis, stroke). However, accurately determining the time of clinical events can be challenging, and the methods used to generate time estimates are underdeveloped. We sought to develop an approach to determine the time of a clinical event-cancer recurrence-using high-dimensional longitudinal structured data. METHODS: Manual chart abstraction provided information regarding the actual time of cancer recurrence. These data were linked to claims from Medicare or structured EMR data from the Cancer Research Network, which were used to determine time of recurrence for patients with lung or colorectal cancer. We analyzed the longitudinal profile of codes that could help determine the time of recurrence, adjusted for systematic differences between code dates and recurrence dates, and integrated time estimates from different codes to empirically derive an optimal algorithm. RESULTS: We identified twelve code groups that could help determine the time of recurrence. Using claims data for patients with lung cancer, the optimal algorithm consisted of three code groups and provided an average prediction error of 4.8 months. Using EMR data or applying this approach to patients with colorectal cancer yielded similar results. CONCLUSION: Time estimates were improved by selecting codes not necessarily the same as those used to identify recurrence, combining time estimates from multiple code groups, and adjusting for systematic bias between code dates and recurrence dates. Improving the accuracy of time estimates for clinical events can facilitate research, quality measurement, and process improvement.

Development and Validation of the PREMM5 Model for Comprehensive Risk Assessment of Lynch Syndrome.

Purpose Current Lynch syndrome (LS) prediction models quantify the risk to an individual of carrying a pathogenic germline mutation in three mismatch repair (MMR) genes: MLH1, MSH2, and MSH6. We developed a new prediction model, PREMM5, that incorporates the genes PMS2 and EPCAM to provide comprehensive LS risk assessment. Patients and Methods PREMM5 was developed to predict the likelihood of a mutation in any of the LS genes by using polytomous logistic regression analysis of clinical and germline data from 18,734 individuals who were tested for all five genes. Predictors of mutation status included sex, age at genetic testing, and proband and family cancer histories. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUC), and clinical impact was determined by decision curve analysis; comparisons were made to the existing PREMM1,2,6 model. External validation of PREMM5 was performed in a clinic-based cohort of 1,058 patients with colorectal cancer. Results Pathogenic mutations were detected in 1,000 (5%) of 18,734 patients in the development cohort; mutations included MLH1 (n = 306), MSH2 (n = 354), MSH6 (n = 177), PMS2 (n = 141), and EPCAM (n = 22). PREMM5 distinguished carriers from noncarriers with an AUC of 0.81 (95% CI, 0.79 to 0.82), and performance was similar in the validation cohort (AUC, 0.83; 95% CI, 0.75 to 0.92). Prediction was more difficult for PMS2 mutations (AUC, 0.64; 95% CI, 0.60 to 0.68) than for other genes. Performance characteristics of PREMM5 exceeded those of PREMM1,2,6. Decision curve analysis supported germline LS testing for PREMM5 scores ≥ 2.5%. Conclusion PREMM5 provides comprehensive risk estimation of all five LS genes and supports LS genetic testing for individuals with scores ≥ 2.5%. At this threshold, PREMM5 provides performance that is superior to the existing PREMM1,2,6 model in the identification of carriers of LS, including those with weaker phenotypes and individuals unaffected by cancer.

Association of Regional Intensity of Ductal Carcinoma In Situ Treatment With Likelihood of Breast Preservation.

IMPORTANCE: Large regional variation exists in the use of radiotherapy after breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS). Although patients who do not receive initial radiotherapy for DCIS are candidates for subsequent BCS if they experience a second breast event, many undergo mastectomy instead. OBJECTIVE: To examine whether regional practice patterns of radiotherapy for DCIS affect the use of mastectomy in these patients. DESIGN, SETTING, AND PARTICIPANTS: A retrospective analysis of population-based databases (Surveillance, Epidemiology, and End Results [SEER] and SEER-Medicare). Data were obtained for 2679 women in SEER with a diagnosis of DCIS between 1990 and 2011 and for 757 women in SEER-Medicare with a DCIS diagnosis between 1991 and 2009 who had not undergone radiotherapy for DCIS and experienced a subsequent breast cancer or DCIS diagnosis. EXPOSURES: Treatment intensity for primary DCIS (high, medium, low), as defined by separating health service areas (HSAs) into 3 clusters based on radiotherapy use. MAIN OUTCOMES AND MEASURES: Mastectomy vs BCS at a second breast event defined as DCIS recurrence or new invasive cancer. RESULTS: The median (SD) ages of the participants was 64 (13) years for the 2679 SEER population and 79 (6) years for the SEER-Medicare cohort. Residence in an HSA characterized by greater radiotherapy use for DCIS increased the likelihood of receiving mastectomy vs BCS at a subsequent breast event, even among women who had not previously received radiotherapy for DCIS. Adjusted odds ratios for receiving mastectomy were 1.43 (95% CI, 1.10-1.85) and 1.90 (95% CI, 1.27-2.84) in SEER and SEER-Medicare databases, respectively, among women residing in an HSA with the greatest radiotherapy use vs the least, corresponding to an adjusted increase from 40.8% to 49.6%, and from 38.6% to 54.5%. CONCLUSIONS AND RELEVANCE: Areas with more radiotherapy use for DCIS had increased use of mastectomy at the time of a second breast event even among patients eligible for breast conservation. This association suggests that physician-related factors are affecting the likelihood of breast preservation.

Influence of patient and provider characteristics on quality of care for the myelodysplastic syndromes.

Little is known about quality of care for patients with myelodysplastic syndromes (MDS), or patient and provider factors that influence quality. We identified Medicare enrollees diagnosed with MDS between 2006 and 2011, and analysed linked claims for performance on two widely-accepted quality measures: diagnostic bone marrow cytogenetic testing (diagnostic quality) and pre-treatment iron assessment for patients receiving an erythropoiesis-stimulating agent (ESA; treatment quality). A total of 4575 patients met the criteria for diagnostic quality measurement, and 3379 for treatment quality measurement. In the diagnostic cohort, 74% had a claim for marrow cytogenetic testing 3 months before to 3 months after diagnosis. In multivariate models, younger age (P < 0·001), treatment at a higher-volume MDS centre (P < 0·001), and claims for pancytopenia (P < 0·001) were all associated with higher levels of testing. A borderline result was observed for diagnostic year, with improvement over time (P = 0·06). In the treatment cohort, 56% had evidence of pre-ESA iron assessment, with higher rates for later years of diagnosis (P < 0·001), higher household income (P = 0·03), and those treated at higher-volume centres (P = 0·01). In this large cohort of patients with MDS, quality of care was suboptimal overall, but worse in several specific subgroups. These data suggest that targeted educational and/or process-focused interventions are warranted.

A versatile test for equality of two survival functions based on weighted differences of Kaplan-Meier curves.

With censored event time observations, the logrank test is the most popular tool for testing the equality of two underlying survival distributions. Although this test is asymptotically distribution free, it may not be powerful when the proportional hazards assumption is violated. Various other novel testing procedures have been proposed, which generally are derived by assuming a class of specific alternative hypotheses with respect to the hazard functions. The test considered by Pepe and Fleming (1989) is based on a linear combination of weighted differences of the two Kaplan-Meier curves over time and is a natural tool to assess the difference of two survival functions directly. In this article, we take a similar approach but choose weights that are proportional to the observed standardized difference of the estimated survival curves at each time point. The new proposal automatically makes weighting adjustments empirically. The new test statistic is aimed at a one-sided general alternative hypothesis and is distributed with a short right tail under the null hypothesis but with a heavy tail under the alternative. The results from extensive numerical studies demonstrate that the new procedure performs well under various general alternatives with a caution of a minor inflation of the type I error rate when the sample size is small or the number of observed events is small. The survival data from a recent cancer comparative study are utilized for illustrating the implementation of the process.

Financial hardship and the intensity of medical care received near death.

BACKGROUND: Although end-of-life (EOL) care can present a substantial financial burden for the household, the influence of this burden on the intensity of care received at the EOL remains unknown. The goal of this study was to determine the association between financial hardship and intensive care in the last week of life. METHODS: The Coping with Cancer (CwC) Study is a longitudinal, multisite cohort study of terminally ill cancer patients and their informal caregivers, September 2002-February 2008. Patients (N = 281) were followed from baseline to death, a median of 4.4 months after baseline assessment. Intensive care was defined as the use of resuscitation and/or ventilation in the patient's last week of life. Financial hardship was measured at study baseline as a positive response to whether the household had to use all or most of their savings because of the family member's illness. RESULTS: Twenty-nine percent reported financial hardship, and 9% received intensive EOL care. Patients reporting financial hardship had a 3.22 (95% CI: 1.38, 7.53) higher likelihood of receiving intensive EOL care compared with patients not reporting financial hardship. After adjusting for sociodemographic characteristics and patient preferences, patients reporting financial hardship had a 3.05 (95% CI: 1.22, 7.62) higher likelihood of receiving intensive EOL care. CONCLUSION: The depletion of a family's financial resources is a significant predictor of intensive EOL care, over and above the influence of sociodemographic characteristics and patient preferences.

Derivation and validation of the SEER-Medicare myelodysplastic syndromes risk score (SMMRS).

Accruing data since 2001, the SEER-Medicare dataset is an outstanding resource for MDS-related comparative effectiveness research; however, although several MDS prognostic models exist, complete data required to calculate them are not available therein. Using SEER-Medicare as a training and internal validation set (n=9820) and clinical data at our institution as an external validation set (n=307), we created a new MDS risk score (the SMMRS) for use with SEER-Medicare. Risk stratification by the SMMRS was comparable to the IPSS (net reclassification improvement index was 0.0), demonstrating that it is a useful research tool to stratify MDS patients in studies using SEER-Medicare.

Acute hospital care is the chief driver of regional spending variation in Medicare patients with advanced cancer.

The root causes of regional variation in medical spending are poorly understood and vary by clinical condition. To identify drivers of regional spending variation for Medicare patients with advanced cancer, we used linked Surveillance, Epidemiology, and End Results program (SEER)-Medicare data from the period 2004-10. We broke down Medicare spending into thirteen cancer-relevant service categories. We then calculated the contribution of each category to spending and regional spending variation. Acute hospital care was the largest component of spending and the chief driver of regional spending variation, accounting for 48 percent of spending and 67 percent of variation. In contrast, chemotherapy accounted for 16 percent of spending and 10 percent of variation. Hospice care constituted 5 percent of spending. However, variation in hospice spending was fully offset by opposing variation in other categories. Our analysis suggests that the strategy with the greatest potential to improve the value of care for patients with advanced cancer is to reduce reliance on acute hospital care for this patient population.

Prognostic assessment of estimated glomerular filtration rate by the new Chronic Kidney Disease Epidemiology Collaboration equation in comparison with the Modification of Diet in Renal Disease Study equation.

BACKGROUND: Systematic reporting of estimated glomerular filtration rate (eGFR) using the Modification of Diet in Renal Disease (MDRD) Study equation is recommended for detection of chronic kidney disease and prediction of cardiovascular (CV) risk. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation is a newly developed and validated formula for eGFR that is more accurate at normal or near-normal eGFR. We aimed to assess the incremental prognostic accuracy of eGFR(CKD-EPI) versus eGFR(MDRD) in subjects at increased risk for CV disease. METHODS: We performed a post hoc analysis of the VALIANT trial that enrolled 14,527 patients with acute myocardial infarction with signs and symptoms of heart failure and/or left ventricular systolic dysfunction. The eGFR(MDRD) and eGFR(CKD-EPI) were computed using age, gender, race, and baseline creatinine level. Patients were categorized according to their eGFR using each equation. To assess the incremental prognostic value of eGFR(CKD-EPI), the net reclassification improvement was calculated for the composite end point of CV death, recurrent myocardial infarction, heart failure, or stroke. RESULTS: Twenty-four percent of the subjects were reclassified into a different eGFR category using eGFR(CKD-EPI). The composite end point occurred in 33% of the subjects in this cohort. Based on eGFR(CKD-EPI), subjects reclassified into a higher eGFR experienced fewer events than those reclassified into a lower eGFR (21% vs 43%). In unadjusted analyses, the composite end point risk in subjects with eGFR between 75 and 90 mL/min per 1.73 m(2) was comparable with the referent group (eGFR between 90 and 105) using eGFR(MDRD) (hazard ratio 1.1, 95% CI 0.9-1.2) but was significantly higher using eGFR(CKD-EPI) (hazard ratio 1.2, 95% CI 1.1-1.4). The net reclassification improvement for eGFR(CKD-EPI) over eGFR(MDRD) was 8.7%. CONCLUSION: The CKD-EPI equation provides more accurate risk stratification than the MDRD Study equation in patients at high risk for CV disease, including identification of increased risk at mildly decreased eGFR.