In the global battle against lung cancer, the quest to align treatment strategies with patient preferences has gained unprecedented momentum. Lung cancer, notorious for its high mortality and complex treatment landscape, demands innovative approaches that transcend traditional clinical outcomes. A groundbreaking scoping review, recently published in BMC Cancer, delves deeply into the intricacies of discrete choice experiments (DCEs) designed to capture patient preferences in lung cancer therapies, critically analyzing them through the rigorous lens of the ISPOR ESTIMATE checklist.
The rise of discrete choice experiments as a methodological tool reflects a paradigm shift in healthcare research, where patient voices increasingly guide therapeutic development and health policy. DCEs provide a sophisticated approach to quantifying patient preferences by presenting them with hypothetical treatment scenarios comprising varied attributes and levels. These experiments simulate real-world decision-making processes, allowing researchers to discern which factors most influence patient choices. However, the robustness of these insights hinges on the meticulous design and analysis of DCE studies.
This review, focusing on studies published after the 2016 release of the ISPOR ESTIMATE checklist, scrutinizes the current landscape of DCE research in lung cancer. The ESTIMATE checklist, an acronym representing Evaluation, Stochastic, Interpretation, Method, Assumptions, Trade-offs, and Errors, serves as a gold standard framework to ensure rigor and transparency in DCE reporting and analysis. The review identifies 12 seminal studies spanning January 2017 to June 2022, marking a critical juncture for assessing methodological advances since the checklist’s inception.
Among the key findings is the consistent incorporation of treatment efficacy and side effects as central attributes across all examined studies. These factors resonate universally with patients confronting the harsh realities of lung cancer therapies. Yet, intriguingly, fewer than half the studies incorporated cost considerations—a vital determinant in real-world treatment accessibility and patient compliance, potentially reflecting gaps in economic engagement within patient preference models.
Critical evaluation against the ESTIMATE checklist reveals an uneven adherence to best practices. All studies showcased strength in domains related to Interpretation, Method, and Assumptions, suggesting researchers are thorough in explicating analytical techniques and articulating underlying premises. However, a substantial proportion faltered in the Evaluation, Stochastic, and Trade-offs domains, with 83.3%, 41.7%, and 33.3% of studies respectively lacking completeness. This shortfall uncovers a crucial vulnerability in the evaluative rigor of statistical analyses and the nuanced understanding of risk-benefit balances essential to patient-centric decision frameworks.
The Evaluation domain pertains to comprehensive assessment of model performance and validity, incorporating sensitivity analyses and goodness-of-fit metrics that verify the credibility of preference estimations. The significant omission in this domain across most studies points to potential overconfidence in preliminary results and underexploration of model robustness. Similarly, deficits in addressing Stochastic considerations highlight shortcomings in acknowledging uncertainty and variability inherent in patient choice data, thereby risking oversimplified interpretations.
Attention to Trade-offs, encompassing the quantification of how patients weigh different treatment attributes against each other, remains incomplete in one-third of the studies. Given that treatment decisions are inherently multi-criteria and context-dependent, this gap signals a need for more sophisticated analytic strategies to capture the complex compensations patients make between efficacy, side effects, and other factors.
Beyond these technical critiques, the review underscores the broader imperative to harmonize methodological rigor with clinical relevance. Effective DCE design must not only meet statistical standards but also resonate with real patient experiences and health economics frameworks. Incorporating costs and quality-of-life impacts more comprehensively could enrich these experimental paradigms, making findings more actionable for policy-makers, clinicians, and patients alike.
The study also highlights the evolving role of guidelines like the ISPOR ESTIMATE checklist in shaping research quality. Since its release, the checklist has sharpened focus on comprehensive reporting and analytical transparency. Yet, its partial adoption signals a pressing need for enhanced education and dissemination among researchers in the lung cancer domain, possibly through workshops, collaborative networks, and integration into peer review standards.
Looking forward, the fusion of patient preference data with emerging precision oncology insights offers an exciting frontier. Integrating genomic profiles, treatment response biomarkers, and patient-reported outcomes within DCE frameworks could yield personalized preference models, revolutionizing shared decision-making in lung cancer. However, such advancements will necessitate even more scrupulous adherence to checklist principles, particularly in methodological sophistication and evaluative robustness.
Moreover, digital innovations—including adaptive DCE designs, machine learning-enhanced analyses, and virtual reality-based preference elicitation—hold promise to overcome current limitations by capturing dynamic, context-rich patient preferences more intuitively and accurately. These tools could also facilitate inclusion of diverse patient populations, addressing equity concerns by ensuring that preference data reflect broad demographic and socio-economic spectra.
In conclusion, this comprehensive review casts a spotlight on both achievements and challenges in harnessing discrete choice experiments for patient-centric lung cancer treatment design. While methodological frameworks like the ISPOR ESTIMATE checklist provide essential scaffolding, the journey towards fully realizing patient preference-informed care is ongoing. Continuous refinement of DCE methodologies, robust statistical evaluation, and integrated, patient-centered attributes will be pivotal in shaping future research and clinical practice paradigms.
As the lung cancer community grapples with increasingly complex therapeutic landscapes, embedding patient voices through rigorous DCEs emerges not merely as an option but as a necessity. By bridging the gap between clinical innovation and patient priorities, these experiments can ultimately transform treatment trajectories and improve outcomes in one of the world’s most formidable cancer challenges.
Subject of Research:
Article Title:
Article References: Hirai, T., Horie, Y. & Kitamura, T. Summary of present design of discrete choice experiments for patient preferences in lung cancer based on the ISPOR ESTIMATE checklist. BMC Cancer 25, 1649 (2025). https://doi.org/10.1186/s12885-025-15116-6
Image Credits: Scienmag.com
DOI: https://doi.org/10.1186/s12885-025-15116-6
