Estimating and Rewarding the Value of Healthcare Interventions Beyond the Healthcare Sector: A Conceptual Framework.

BACKGROUND: Evaluating healthcare interventions for their impacts beyond health outcomes may result in recognition of changes in human capital, income level, tax revenue, and government spending, which could affect economic growth and population health. In this paper, we document instances where current health technology assessment (HTA) practices fail to account for the impacts of healthcare interventions on broader society beyond the healthcare sector. METHODS: We propose a novel conceptual framework, highlighting its three components (distributional cost-effectiveness analysis [DCEA], input-output model, and voting scheme) and their contributions to capturing the economic and societal ripple effects of healthcare interventions. This manuscript also outlines a case study in which the framework is applied to the reassessment of a previously evaluated digital health therapeutic for the treatment of opioid use disorder (OUD) compared with standard of care, demonstrating its practical application. RESULTS: The DCEA health value metric indicates that digital therapeutic is more equitable, favoring socioeconomically disadvantaged groups, while standard of care exacerbates health inequality by benefiting the already advantaged. Additionally, digital therapeutic shows potential for boosting productivity, raising income, and creating jobs, supporting its consideration by employer-sponsored health plans to optimize resource allocation for treating OUD. CONCLUSION: The conceptual framework provides insights for enhancing HTAs to incorporate the broader economic and societal impacts of healthcare interventions. By integrating DCEA, extended HTA analysis with input-output modeling, and a voting scheme, decision makers can make informed choices aligned with societal priorities, although further research and validation are necessary for practical implementation across diverse healthcare contexts.

The State of Pharmacoeconomics Education in the Doctor of Pharmacy Curriculum amid the Changing Face of Pharmacy Practice.

(1) Background: Continuous growth in pharmaceutical expenditure indicates the need for more advanced pharmacoeconomics evaluations to optimize healthcare outcomes and resource allocation. This study assesses the extent to which accredited pharmacy colleges in the United States cover pharmacoeconomics content within the didactic curriculum of their Doctor of Pharmacy (PharmD) programs. (2) Methods: We conducted a systematic search of the websites of accredited professional-degree programs in pharmacy schools located in the United States to identify pertinent content related to pharmacoeconomics. (3) Results: Out of 141 pharmacy programs, a total of 111 programs of varying sizes were found to have publicly accessible information regarding the content of their pharmacoeconomic curricula on their websites. All these programs required the inclusion of pharmacoeconomics content in their curricula. An examination of course syllabi revealed that the goals and descriptions were broad, aiming to provide students with an introductory understanding of the principles of pharmacoeconomics. The number of credit hours allocated to pharmacoeconomics education ranged from one to seven across the programs. The approach to delivering pharmacoeconomics content varied among the programs. (4) Conclusions: Advanced knowledge of the principles of pharmacoeconomics must be an integral component of all PharmD curricula to prepare pharmacists to assess the rational use of pharmacy products and services, improve clinical outcomes, and mitigate healthcare expenditures.

Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes.

BACKGROUND: Major Adverse Cardiovascular Events (MACE) are common complications of type 2 diabetes mellitus (T2DM) that include myocardial infarction (MI), stroke, and heart failure (HF). The objective of the current study was to predict MACE among T2DM patients. METHODS: Type 2 diabetes mellitus patients above 18 years old were recruited for the study from the All of Us Research Program. Eligible participants were those who took sodium-glucose cotransporter 2 inhibitors. Different Machine learning algorithms: including RandomForest (RF), XGBoost, logistic regression (LR), and weighted ensemble model (WEM) were employed. Clinical attributes, electrolytes and biomarkers were explored in predicting MACE. The feature importance was determined using mean decrease accuracy. RESULTS: Overall, 9, 059 subjects were included in the analyses, of which 5197 (57.4%) were females. The XGBoost Model demonstrated a prediction accuracy of 0.80 [0.78-0.82], which is higher as compared to the RF 0.78[0.76-0.80], the LR model 0.65 [0.62-0.67], and the WEM 0.75 [0.73-0.76], respectively. The classification accuracy of the models for stroke was more than 95%, which was higher than prediction accuracy for MI (∼85%), and HF (∼80%). Phosphate, blood urea nitrogen and troponin levels were the major predictors of MACE. CONCLUSION: The ML models had shown acceptable performance in predicting MACE in T2DM patients, except the LR model. Phosphate, blood urea nitrogen, and other electrolytes were important predictors of MACE, which is consistent between the individual components of MACE, such as stroke, MI, and HF. These parameters can be calibrated as prognostic parameters of MACE events in T2DM patients.

Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data.

There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79-0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73-0.75), the logistic regression 0.64 (95% CI: 0.63-0.65) and the weighted ensemble model 0.77 (95% CI: 0.76-0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics.

Impact of Sertraline, Fluoxetine, and Escitalopram on Psychological Distress among United States Adult Outpatients with a Major Depressive Disorder.

How impactful is the use of Sertraline, Fluoxetine, and Escitalopram monotherapy on psychological distress among adults with depression in the real world? Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed antidepressants. Medical Expenditure Panel Survey (MEPS) longitudinal data files from 1 January 2012 to 31 December 2019 (panel 17-23) were used to assess the effects of Sertraline, Fluoxetine and Escitalopram on psychological distress among adult outpatients diagnosed with a major depressive disorder. Participants aged 20-80 years without comorbidities, who initiated antidepressants only at rounds 2 and 3 of each panel, were included. The impact of the medicines on psychological distress was assessed using changes in Kessler Index (K6) scores, which were measured only in rounds 2 and 4 of each panel. Multinomial logistic regression was conducted using the changes in the K6 scores as a dependent variable. A total of 589 participants were included in the study. Overall, 90.79% of the study participants on monotherapy antidepressants reported improved levels of psychological distress. Fluoxetine had the highest improvement rate of 91.87%, followed by Escitalopram (90.38%) and Sertraline (90.27%). The findings on the comparative effectiveness of the three medications were statistically insignificant. Sertraline, Fluoxetine, and Escitalopram were shown to be effective among adult patients suffering from major depressive disorders without comorbid conditions.

Health-Related Quality of Life and Healthcare Events in Patients with Monotherapy of Anti-Diabetes Medications.

This study aimed to examine the difference in health-related quality of life (HRQOL) and diabetes-related healthcare events (HCEs) among adults with diabetes who were on metformin, sulfonylurea, insulin, or thiazolidinedione (TZD) monotherapy. The data were sourced from the Medical Expenditure Panel Survey (MEPS). Diabetes patients ≥18 years old who had a complete record of physical component score and mental component scores in round 2 and round 4 of the survey were included. The primary outcome was HRQOL of diabetes patients as measured by the Medical Outcome Study short-form (SF-12v2TM). Multinomial logistic regression and negative binomial regression were conducted to determine associated factors of HRQOL and HCE, respectively. Overall, 5387 patients were included for analysis. Nearly 60% of patients had unchanged HRQOL after the follow-up, whereas almost 15% to 20% of patients showed improvement in HRQOL. The relative risk of declined mental HRQOL was 1.5 times higher relative to unchanged mental HRQOL in patients who were on sulfonylurea 1.55 [1.1-2.17, p = 0.01] than metformin users. The rate of HCE decreased by a factor of 0.79, [95% CI: 0.63-0.99] in patients with no history of hypertension. Patients on sulfonylurea 1.53 [1.20-1.95, <0.01], insulin 2.00 [1.55-2.70, <0.01], and TZD 1.78 [1.23-2.58, <0.01] had increased risk of HCE compared to patients who were on metformin. In general, antidiabetic medications modestly improved HRQOL in patients with diabetes during the follow-up period. Metformin had a lower rate of HCE as compared to other medications. The selection of anti-diabetes medications should focus on HRQOL in addition to controlling glucose level.

Cost Effectiveness of Dapagliflozin Added to Standard of Care for the Management of Diabetic Nephropathy in the USA.

BACKGROUND: Angiotensin-converting enzyme inhibitors have been used as the standard of care for the treatment of diabetic nephropathy. Recently, dapagliflozin has been shown to reduce diabetic nephropathy when added to the standard of care. OBJECTIVE: The objective of this study was to determine the cost effectiveness of dapagliflozin added to the standard of care in diabetic nephropathy in the United States of America (USA). METHODS: A Markov model was developed to determine the cost-effectiveness outcomes from the Medicare/Medicaid health coverage perspective. Model inputs were derived from the literature. The primary outcomes were total costs, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio. Deterministic and probabilistic sensitivity analyses were performed to determine the robustness of our results. A willingness-to-pay threshold of $100,000 per QALY was applied, which is based on previous studies. RESULTS: Dapagliflozin yielded a lifetime QALY of 2.8. The discounted QALY associated with the standard of care was 2.6. The standard of care was the less costly treatment with a lifetime cost of $106,150.25 as compared with dapagliflozin, which costs $110,689.25. Dapagliflozin demonstrated an incremental cost-effectiveness ratio of $21,141.51 per additional QALY. The most influential parameters of the incremental cost-effectiveness ratio were the adverse drug reaction-related cost of the standard of care and dapagliflozin, the acquisition cost, and the adverse drug reaction-related cost of dapagliflozin. The effects and costs of the interventions were consistent between base-case analyses and the probabilistic model (incremental cost-effectiveness ratio: $19,023.35 [$13,637.8-$27,483.1]). CONCLUSIONS: Dapagliflozin added to the standard of care was cost effective relative to the standard of care alone in the USA for patients with diabetic nephropathy.

Comparative cost-effectiveness of radiotherapy among older women with hormone receptor positive early-stage breast cancer.

OBJECTIVE: The aim was to examine the real-world cost-effectiveness of breast-conserving surgery (BCS) plus hormonal therapy with radiotherapy, compared to hormonal therapy alone among women 66 and older with hormone receptor positive early-stage breast cancer in the United States (US). METHODS: This study was conducted from a U.S. Centers for Medicare and Medicaid Services perspective and an eight-year time horizon. Both costs (2020 US$) and health utilities (quality-adjusted life years, QALYs) were obtained from retrospective studies using the SEER linked with Medicare and Medicare Health Outcomes Survey, respectively. The incremental cost-effectiveness ratio (ICER) of the addition of radiotherapy to hormonal therapy versus hormonal therapy alone after BCS was estimated by an unbiased doubly robust estimator. Sensitivity analyses were conducted through bootstrapping to estimate credible intervals. RESULTS: The addition of radiotherapy to hormonal therapy after BCS yielded the highest clinical benefits (2.66 QALYs) and costs ($19,424.27) compared to its hormonal therapy alone after BCS (0.77 QALYS; $2,028.58). The ICER was estimated to be $9,174.94/QALY. Sensitivity analyses did not change the direction of the findings. CONCLUSIONS: The results implicated that the combination of radiotherapy and hormonal therapy is cost-effective in the US.

Real-World Clinical and Economic Outcomes Associated with Palbociclib for HR-Positive/HER2 Negative Metastatic Breast Cancer: A Commentary.

Despite the achieved advancement in pharmacological cancer treatments, the majority of postmenopausal women with hormone receptor-positive metastatic breast cancer (mBC) will experience disease progression. Research into alternative therapies with improved efficacy and reduced side effects has led to the development of a new class of oral anticancer medications, the cyclin-dependent kinase (CDK) 4/6 inhibitors, which include palbociclib, ribociclib, and abemaciclib. Nonetheless, there is growing evidence that the effectiveness of oral anticancer medications is sub-optimal, being influenced by low adherence, sociodemographic factors, and adverse effect profiles. In addition, there is a disconnect between the high price tags of CDK 4/6 inhibitors and their observed effectiveness, raising questions about their value. Currently, the existing knowledge base on the effectiveness and cost-effectiveness of newer oral anticancer medications in understudied populations with possible health disparities is scant. This commentary discusses what is known about palbociclib's clinical effectiveness, safety, and adherence and suggests the need for further studies of real-world effectiveness and cost-effectiveness to help establish the value of newer oncologic drugs, such as palbociclib. DISCLOSURES: No funding supported the writing of this article. The authors have nothing to disclose.

Dataset used in the economic evaluation trastuzumab-based regimens for HER-2 positive metastatic breast cancer patients in the Taiwanese healthcare setting.

The present data article aims to describe the input parameters for a Markov model assessing the cost-effectiveness of four treatment sequences for patients with HER-2 positive metastatic breast cancer. The model input parameters include costs for physician visits, drugs, adverse event management, computed tomography (CT) scan, laboratory tests, echocardiogram, utilities, disutilities as well as the shape and scale parameters of a log-logistic distribution used for the transition probabilities.

A cost-effectiveness analysis of trastuzumab-containing treatment sequences for HER-2 positive metastatic breast cancer patients in Taiwan.

OBJECTIVE: Treatment options for HER-2-positive metastatic breast cancer (mBC) patients have expanded markedly since trastuzumab approval in 1998. Several other regimens are now available, including pertuzumab plus trastuzumab plus docetaxel, T-DM1, capecitabine plus lapatinib, and trastuzumab plus lapatinib. This study assesses the cost-effectiveness of four treatment sequences for HER-2-positive mBC according to the Taiwanese National Health Insurance Administration (TNHIA). METHODS: Costs (U.S. Dollars) and effectiveness (quality-adjusted life years) of four treatment sequences for HER-2-positive mBC patients were examined using a Markov model over a lifetime horizon. Transition probabilities, disease progression, and probability of adverse events and survival were derived from clinical trial data. Costs and health utilities were estimated from TNHIA, Taipei Medical University Hospital, and the literature. Deterministic, probabilistic sensitivity analyses and a scenario analysis examined parameter uncertainty and accounted for drug wastage in dosage and cost calculations. RESULTS: Sequence 3 (1st line: trastuzumab plus docetaxel; 2nd line: T-DM1; 3rd line: trastuzumab plus lapatinib) was the most cost-effective sequence followed by sequence 1 (1st line: pertuzumab plus trastuzumab plus docetaxel; 2nd line: T-DM1; 3rd line: capecitabine plus lapatinib), and sequence 4 (1st line: trastuzumab plus docetaxel; 2nd line: trastuzumab plus lapatinib; 3rd line: trastuzumab plus capecitabine), respectively. The model was sensitive to costs and transition probabilities, but not particularly sensitive to the wastage assumption. CONCLUSIONS: From the perspective of the TNHIA, trastuzumab plus docetaxel as 1st line followed by T-DM1 and trastuzumab plus lapatinib as 2nd and 3rd line represents the most cost-effective strategy among the four sequences considered for treating HER-2-positive mBC patients.

Using time series analysis to forecast the health-related quality of life of post-menopausal women with non-metastatic ER+ breast cancer: A tutorial and case study.

BACKGROUND: Time series models are widely used forecasting techniques in health care for long time series and are typically built in commercial statistical packages. However, for short time series data, such as health-related quality of life (HRQoL), guidance on how to select and use appropriate time series models is lacking. This tutorial provides a step-by-step guide adopting a time series analysis framework for HRQoL forecasting. OBJECTIVE: We walk through a case study examining the forecasting of the effects of adjuvant endocrine therapy on the HRQoL of post-menopausal women with non-metastatic ER + breast cancer using data from the HRQoL sub-protocol of the Tamoxifen arm of the Arimidex, tamoxifen, alone or in combination (ATAC) trial. METHODS: The forecasting of HRQoL consists of four steps: 1) data extraction and accuracy check, 2) forecasting horizon definition and identification of data pattern, 3) forecasting model identification and fitting using five forecasting approaches appropriate for short time series ((i) double exponential smoothing, (ii) double moving average, (iii) fuzzy forecasting, (iv) grey forecasting, and (v) Volterra series), 4) forecasting model selection. A user-friendly visual basic for applications (VBA) Excel add-in is made available to interested users to facilitate the application of the tutorial. RESULTS: The Grey method and Volterra series appeared to be good candidates to forecast the effects of adjuvant endocrine therapy on the HRQoL of post-menopausal women with non-metastatic ER + breast cancer enrolled in the ATAC trial. CONCLUSION: It is feasible to forecast the effects of treatments on HRQOL even when the time series is short.

Economic evaluation of sequencing strategies in HER2-positive metastatic breast cancer in Mexico: a contrast between public and private payer perspectives.

PURPOSE: Breast cancer is the most common malignancy among women in Mexico. A large proportion of Mexican patients present with advanced disease, and 25% have HER2-positive tumors. We performed a cost-effectiveness analysis of different sequencing strategies of HER2-targeted agents in Mexico according to various payer perspectives. METHODS: A Markov model was constructed to evaluate the cost-effectiveness of four different HER2-targeted treatment sequences among patients with HER2-positive metastatic breast cancer treated in Mexico according to three public and one private payer perspectives. Patients were followed weekly over their remaining life expectancies within the model. Health states considered were progression-free survival (PFS) 1st-3rd lines, and death. Transition probabilities between states were based on published trials. Cost data were obtained from official publications from Mexican healthcare institutions. The evaluated outcomes were PFS, OS, costs, QALYs, and incremental cost effectiveness ratio (ICER). RESULTS: In the public payer perspective, sequences containing pertuzumab or T-DM1 were not cost-effective when compared with a sequence including the combination of trastuzumab/docetaxel as first line without subsequent T-DM1 or pertuzumab, even when utilizing alternate definitions for willingness to pay thresholds. In the private payer perspective, a sequence containing T-DM1 but not pertuzumab proved cost-effective at a lower clinical effectiveness. CONCLUSIONS: In Mexico, the use of at least three lines of trastuzumab in combination with other therapies, but not with pertuzumab or TDM-1, represents the most cost-effective option for patients covered by the public healthcare system, and this sequence should be made available for all patients.

Comparison of health utility weights among elderly patients receiving breast-conserving surgery plus hormonal therapy with or without radiotherapy.

BACKGROUND: The selection of the most appropriate treatment combinations requires the balancing of benefits and harms of these treatment options as well as the patients' preferences for the resulting outcomes. OBJECTIVE: This research aimed at estimating and comparing the utility weights between elderly women with early stage hormone receptor positive (HR+) breast cancer receiving a combination of radiotherapy and hormonal therapy after breast conserving surgery (BCS) and those receiving a combination of BCS and hormonal therapy. METHODS: The Surveillance, Epidemiology, and End Results (SEER) linked with Medicare Health Outcomes Survey (MHOS) was used as the data source. Health utility weights were derived from the VR-12 health-related quality of life instrument using a mapping algorithm. Descriptive statistics of the sample were provided. Two sample t-tests were performed to determine potential differences in mean health utility weights between the two groups after propensity score matching. RESULTS: The average age at diagnosis was 72 vs. 76 years for the treated and the untreated groups, respectively. The results showed an inverse relationship between the receipt of radiotherapy and age. Patients who received radiotherapy had, on average, a higher health utility weight (0.70; SD = 0.123) compared with those who did not receive radiotherapy (0.676; SD = 0.130). Only treated patients who had more than two comorbid conditions had significantly higher health utility weights compared with patients who were not treated. CONCLUSIONS: The mean health utility weights estimated for the radiotherapy and no radiotherapy groups can be used to inform a comparative cost-effectiveness analysis of the treatment options. However, the results of this study may not be generalizable to those who are outside a managed care plan because MHOS data is collected on managed care beneficiaries.

Impact of Comorbidities on Prostate Cancer Stage at Diagnosis in Florida.

To examine the association of major types of comorbidity with late-stage prostate cancer, a random sample of 11,083 men diagnosed with prostate cancer during 2002-2007 was taken from the Florida Cancer Data System. Individual-level covariates included demographics, primary insurance payer, and comorbidity following the Elixhauser Index. Socioeconomic variables were extracted from Census 2000 data and merged to the individual level data. Provider-to-case ratio at county level was alsocomputed. Multilevel logistic regression was used to assess associations between these factors and late-stage diagnosis of prostate cancer. Higher odds of late-stage diagnosis was significantly related to presence of comorbidities, being unmarried, current smoker, uninsured, and diagnosed in not-for-profit hospitals. The study reported that the presence of certain comorbidities, specifically 10 out of the 45, was associated with late-stage prostate cancer diagnosis. Eight out of 10 significant comorbid conditions were associated with greater risk of being diagnosed at late-stage prostate cancer. On the other hand, men who had chronic pulmonary disease, and solid tumor without metastasis, were less likely to be diagnosed with late-stage prostate cancer. Late-stage diagnosis was associated with comorbidity, which is often associated with increased health care utilization. The association of comorbidity with late-stage prostate cancer diagnosis suggests that individuals with significant comorbidity should be offered routine screening for prostate cancer rather than focusing only on managing symptomatic health problems.

Factors associated with overall survival prostate cancer in Florida: a multilevel analysis.

OBJECTIVE: To investigate individual and contextual factors contributing to overall prostate cancer (PCa) survival in Florida. METHODS: A random sample of 6,457 PCa cases diagnosed between 10/1/2001 and 12/31/2007 was extracted from Florida Cancer Data System. Comorbidity was computed following Elixhauser Index. Survival probability curve was generated using Kaplan-Meier method. The Wei, Lin, and Weissfel model was used for the multivariate analysis. RESULTS: Older age at diagnosis was associated with shorter time to death. Current smokers had a higher hazard rate than non-current smokers. Higher hazard of overall mortality was associated with being diagnosed with advanced stage compared with localized stage and having poorly-differentiated tumor compared with well-moderately differentiated tumor. No definitive treatment, radiation alone, and hormone alone were significantly associated with elevated hazard rate compared with surgery. Fifteen comorbidities were significantly associated with shorter time-to-death. CONCLUSIONS: Effective control of comorbidity in PCa patients should help improve life expectancy and lead to prolonged survival.

Health insurance and breast-conserving surgery with radiation treatment.

OBJECTIVES: To examine the impact of health insurance type on treatment of early-stage breast cancer using breast-conserving surgery (BCS) with radiation therapy (RT) among women in Florida and identify factors that contribute to the variations in receiving the treatment in women with the same health insurance type. STUDY DESIGN AND METHODS: Breast cancer cases diagnosed during 1997 to 2002 were obtained from the Florida Cancer Data System. Women 40 years and older diagnosed with localized breast cancer were included. Demographic, insurance, and treatment information were extracted and linked with 2000 census data. χ² and multilevel logistic regression analyses were used. RESULTS: A total of 33,706 women were diagnosed with localized breast cancer in Florida during 1997 to 2002. The average age of the women was 66 years, 58.62% had BCS while 38.61% had mastectomy, and only 2.77% had no surgical treatment. Type of health insurance plays a significant role in receiving BCS with RT. Furthermore, we found significant variations in the use of BCS with RT among women who have the same type health insurance by marital status, age, tumor size, year of diagnosis, level of education, and poverty level. CONCLUSIONS: Although clinical practice guidelines recommend BCS with RT to treat women with localized breast cancer, significant differences in receiving the recommended treatment is found between and within types of health insurance. Identifying cultural barriers and educating the public about available treatment options are the major policy implications of this study. These observed differences require further study.

Using quality-adjusted progression-free survival as an outcome measure to assess the benefits of cancer drugs in randomized-controlled trials: case of the BOLERO-2 trial.

The aim of this study is to estimate the quality-adjusted progression-free survival (QAPFS) as an effectiveness measure for the treatment arms of the BOLERO-2 trial. For each treatment arm of the trial, QAPFS was estimated by multiplying the overall health utility weights associated with progression-free survival (PFS) (accounting for utility decrements associated with the adverse events of treatments) by the corresponding mean PFS time. Health utility data were obtained from the literature, while mean PFS times were estimated through a survival analysis of the reconstructed individual patient data of the BOLERO-2 trial. PFS (robust mean, (95 % robust confidence interval)) was 44.73 weeks (41.03; 48.43) for Everolimus + Exemestane and 22.98 weeks (19.88; 26.08) for Placebo + Exemestane. The QAPFS (robust mean, (95 % robust confidence interval)) for the treatment arms of the trial was 30.09 (27.60; 32.58) for Everolimus + Exemestane and 16.27 (14.07; 18.46) for Placebo + Exemestane, respectively. Using QAPFS as an outcome measure provides a complete picture of the benefit induced by the treatment arms of the BOLERO-2 trial. The benefit of Everolimus + Exemestane over Placebo + Exemestane observed in the trial is maintained in this analysis. The approach and estimates obtained as part of our analysis can serve as a basis for cost effectiveness analyses of the treatment arms of the BOLERO-2 trial.

Use of treatment information from a state central cancer registry in prostate cancer research.

A method was developed to categorize prostate cancer treatments for epidemiologic and outcomes studies. A total of 60,497 prostate cancer cases from the Florida Cancer Data System diagnosed between 2001 and 2007 were used. The classification has the following properties. First, the treatments classified in the same group are clinically comparable and capture all prostate cancer treatments or combinations of treatments (exhaustive classification). Second, the grouping was set up in a way that each patient is captured in only 1 treatment category without leaving out any patient due to treatment type (mutually exclusive categories). The prostate cancer cases were initially categorized into 14 combinations of treatment, which were then collapsed into 8 broader groups in order to maintain a large sample size for all groups, with treatments remaining clinically comparable within a group.