Warren Stevens

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

To address the need for more accurate risk stratification models for cancer immuno-oncology, this study aimed to develop a machine-learned Bayesian network model (BNM) for predicting outcomes in patients with metastatic renal cell carcinoma (mRCC) being treated with immunotherapy. Patient-level data from the randomized, phase III CheckMate 025 clinical trial comparing nivolumab with everolimus for second-line treatment in patients with mRCC were used to develop the BNM. Outcomes of interest were overall survival (OS), all-cause adverse events, and treatment-related adverse events (TRAE) over 36 months after treatment initiation. External validation of the model's predictions for OS was conducted using data from select centers from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC). Areas under the receiver operating characteristic curve (AUCs) for BNM-based classification of OS using baseline data were 0.74, 0.71, and 0.68 over months 12, 24, and 36, respectively. AUC for OS at 12 months increased to 0.86 when treatment response and progression status in year 1 were included as predictors; progression and response at 12 months were highly prognostic of all outcomes over the 36-month period. AUCs for adverse events and treatment-related adverse events were approximately 0.6 at 12 months but increased to approximately 0.7 by 36 months. Sensitivity analysis comparing the BNM with machine learning classifiers showed comparable performance. Test AUC on IMDC data for 12-month OS was 0.71 despite several variable imbalances. Notably, the BNM outperformed the IMDC risk score alone. The validated BNM performed well at prediction using baseline data, particularly with the inclusion of response and progression at 12 months. Additionally, the results suggest that 12 months of follow-up data alone may be sufficient to inform long-term survival projections in patients with mRCC.

Cost‐effectiveness is traditionally treated as a static estimate driven by clinical trial efficacy and drug price at launch. Prior studies suggest that cost‐effectiveness varies over the drug's lifetime. We examined the impact of “learning by doing,” one of the least studied drivers of changes in cost‐effectiveness across the product life cycle. We combined time‐series trends in effectiveness over time by cancer regimen using the Surveillance, Epidemiology, and End Results‐Medicare database. We estimated the time‐varying effects of treatments in colorectal and pancreatic cancer over their life cycle, including FOLFOX (leucovorin, 5‐fluorouracil, and oxaliplatin) and gemcitabine, on survival of patients. Mean prices over time by strength and dosage form were calculated using historical wholesale acquisition costs. We found consistent downward trends in the mortality hazard ratios, which suggest that effectiveness improves over time. In the case of first‐line FOLFOX for colorectal cancer, the implied incremental cost‐effectiveness ratio based on the observational data fell from $610,000 per life year gained in 2004 to $27,000 per life year gained in 2011. Cost‐effectiveness estimated at launch is unlikely to be representative of cost‐effectiveness over the drug's lifetime. In the drugs studied, the impact of time‐varying clinical effectiveness dominated the impact of changing prices overtime.

Rates of chronic illness within the New South Wales, Australia, prison population are alarmingly high. Population approaches to health care and community development pose some unique challenges in the context of prisons. These challenges are discussed in the context of the report on the results of a small pilot randomised control trial conducted within one New South Wales prison that investigated the effect of a 12-week exercise and health education program on male inmates? psychological distress. The exercise program targeted individuals with a chronic illness, those who had two or more risk factors for developing a chronic illness and/or inmates over 40 years of age. The psychological distress of the inmate participants was measured weekly using a modified Kessler 10 tool. No statistically significant differences were found between the groups pre- or post- the exercise program. There was also no significant correlation between total psychological distress and participating or not participating in the exercise. A further study, with a larger sample size and different tool, is recommended.