Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps

A scoping review on patient heterogeneity in economic evaluations of precision medicine based on basket trials

INTRODUCTION

Considerable challenges in the economic evaluation of precision medicines have been mentioned in previous studies. However, they have not addressed how an economic assessment would be conducted based on basket trials (novel studies for evaluation of precision medicine effects) in which the included populations have specific biomarkers and various cancers. Since basket trial populations have remarkable heterogeneity, this study aims to investigate the concept of heterogeneity and specific method(s) for considering it in economic evaluations through guidelines and studies that could be applicable in economic evaluation based on basket trials.

AREA COVERED

We searched PubMed, Web of Science, Scopus, Google Scholar, and Google to find studies and pharmacoeconomics guidelines. The inclusion criteria included subjects of patient heterogeneity and suggested explicit method(s). Thirty-nine guidelines and 43 studies were included and evaluated. None of these materials mentioned disease types in a target population as a factor causing heterogeneity. Moreover, in economic evaluations, patient heterogeneity has been considered with four general approaches subgroup analysis, individual-based models, sensitivity analysis, and regression models.

EXPERT OPINION

Type of disease is not considered a contributing factor in population heterogeneity, and the probable appropriate method for this issue could be individual-based models.

The effect of smoking on survival in lung carcinoma patients with brain metastasis: a systematic review and meta-analysis

The effects of smoking on survival in BM patients have yet to be reviewed and meta-analysed. However, previous studies have shown that smokers had a greater risk of dying from lung cancer compared to non-smokers. This meta-analysis, therefore, aimed to analyse the effects of cigarette smoking on overall survival (OS) and progression-free survival (PFS) in lung cancer BM patients. PubMed, Embase, Web of Science, Cochrane and Google Scholar were searched for comparative studies regarding the effects of smoking on incidence and survival in brain metastases patients up to December 2020. Three independent reviewers extracted overall survival (OS) and progression-free survival data (PFS). Random-effects models were used to pool multivariate-adjusted hazard ratios (HR). Out of 1890 studies, fifteen studies with a total of 2915 patients met our inclusion criteria. Amongst lung carcinoma BM patients, those who were smokers (ever or yes) had a worse overall survival (HR: 1.34, 95% CI 1.13, 1.60, I2: 72.1%, p-heterogeneity < 0.001) than those who were non-smokers (never or no). A subgroup analysis showed the association to remain significant in the ever/never subgroup (HR: 1.34, 95% CI 1.11, 1.63) but not in the yes/no smoking subgroup (HR: 1.30, 95% CI 0.44, 3.88). This difference between the two subgroups was not statistically significant (p = 0.91). Amongst lung carcinoma BM patients, smoking was associated with a worse OS and PFS. Future studies examining BMs should report survival data stratified by uniform smoking status definitions.

Big Data and Real-World Data based Cost-Effectiveness Studies and Decision-making Models: A Systematic Review and Analysis

Background: Big data and real-world data (RWD) have been increasingly used to measure the effectiveness and costs in cost-effectiveness analysis (CEA). However, the characteristics and methodologies of CEA based on big data and RWD remain unknown. The objectives of this study were to review the characteristics and methodologies of the CEA studies based on big data and RWD and to compare the characteristics and methodologies between the CEA studies with or without decision-analytic models. Methods: The literature search was conducted in Medline (Pubmed), Embase, Web of Science, and Cochrane Library (as of June 2020). Full CEA studies with an incremental analysis that used big data and RWD for both effectiveness and costs written in English were included. There were no restrictions regarding publication date. Results: 70 studies on CEA using RWD (37 with decision-analytic models and 33 without) were included. The majority of the studies were published between 2011 and 2020, and the number of CEA based on RWD has been increasing over the years. Few CEA studies used big data. Pharmacological interventions were the most frequently studied intervention, and they were more frequently evaluated by the studies without decision-analytic models, while those with the model focused on treatment regimen. Compared to CEA studies using decision-analytic models, both effectiveness and costs of those using the model were more likely to be obtained from literature review. All the studies using decision-analytic models included sensitivity analyses, while four studies no using the model neither used sensitivity analysis nor controlled for confounders. Conclusion: The review shows that RWD has been increasingly applied in conducting the cost-effectiveness analysis. However, few CEA studies are based on big data. In future CEA studies using big data and RWD, it is encouraged to control confounders and to discount in long-term research when decision-analytic models are not used.

Primary Prophylaxis With Biosimilar Filgrastim for Patients at Intermediate Risk for Febrile Neutropenia: A Cost-Effectiveness Analysis

PURPOSE

Temporary COVID-19 guideline recommendations have recently been issued to expand the use of colony-stimulating factors in patients with cancer with intermediate to high risk for febrile neutropenia (FN). We evaluated the cost-effectiveness of primary prophylaxis (PP) with biosimilar filgrastim-sndz in patients with intermediate risk of FN compared with secondary prophylaxis (SP) over three different cancer types.

METHODS

A Markov decision analytic model was constructed from the US payer perspective over a lifetime horizon to evaluate PP versus SP in patients with breast cancer, non-small-cell lung cancer (NSCLC), and non-Hodgkin lymphoma (NHL). Cost-effectiveness was evaluated over a range of willingness-to-pay thresholds for incremental cost per FN avoided, life year gained, and quality-adjusted life year (QALY) gained. Sensitivity analyses evaluated uncertainty.

RESULTS

Compared with SP, PP provided an additional 0.102-0.144 LYs and 0.065-0.130 QALYs. The incremental cost-effectiveness ranged from $5,660 in US dollars (USD) to $20,806 USD per FN event avoided, $5,123 to $31,077 USD per life year gained, and $7,213 to $35,563 USD per QALY gained. Over 1,000 iterations, there were 73.6%, 99.4%, and 91.8% probabilities that PP was cost-effective at a willingness to pay of $50,000 USD per QALY gained for breast cancer, NSCLC, and NHL, respectively.

CONCLUSION

PP with a biosimilar filgrastim (specifically filgrastim-sndz) is cost-effective in patients with intermediate risk for FN receiving curative chemotherapy regimens for breast cancer, NSCLC, and NHL. Expanding the use of colony-stimulating factors for patients may be valuable in reducing unnecessary health care visits for patients with cancer at risk of complications because of COVID-19 and should be considered for the indefinite future.