A surreal illustration representing shared decision-making in adolescent cancer care.

Empowering Young Adults in Cancer Care: How a New Decision-Making Tool is Changing the Game

Elliot Brynn in Health & Wellness June 2025 • 4 min read.

"A groundbreaking approach to quantifying patient preferences promises to reshape treatment decisions and improve end-of-life care for young adults facing cancer."

Cancer remains a formidable adversary, particularly for adolescents and young adults (AYAs). Despite advancements in treatment, it remains a leading cause of non-accidental death in this age group. What's more, AYA oncology patients often receive more intensive medical services at the end of life (EOL) compared to younger children and older adults. This highlights a critical need: ensuring that the care provided aligns with their values and preferences.

EOL decision-making is a research priority, largely due to a lack of evidence-based tools that can guide healthcare providers (HCPs) and support patients and families through these challenging times. The complexities of oncology decision-making, compounded by uncertainties about prognosis and the rapid pace of treatment innovations, make it difficult for AYA patients and their parents to navigate the available options.

A groundbreaking study is working to address these challenges by developing a novel tool that quantifies the relative importance of various factors considered by AYA patients, their parents, and healthcare providers when making treatment choices. This approach aims to empower patients, improve communication, and ultimately lead to more goal-concordant care and a reduction in overly intensive EOL interventions.

The Conjoint Analysis Revolution: Putting Patient Preferences First

A surreal illustration representing shared decision-making in adolescent cancer care.

The core of this innovative approach lies in conjoint analysis, a powerful method for understanding how individuals value different attributes of a product or service. In this context, the 'product' is cancer treatment, and the 'attributes' are factors like quality of life, potential side effects, the possibility of a cure, and survival length. Unlike traditional methods that focus on one attribute at a time, conjoint analysis allows for the simultaneous consideration of multiple factors, mirroring the complexity of real-world decisions.

Researchers are employing discrete choice experiments (DCEs) to explore these complex decisions. In a DCE, participants are presented with hypothetical scenarios involving different treatment alternatives. Each alternative is defined by a unique combination of attributes, and participants are asked to choose their preferred option. By analyzing these choices, researchers can quantify the relative importance of each attribute in the decision-making process.

Comprehensive Assessment: Conjoint analysis considers multiple treatment attributes simultaneously, reflecting the complexity of real-world decisions.
Preference Quantification: The methodology quantifies the relative importance of each attribute, providing valuable insights into patient priorities.
Enhanced Communication: The tool facilitates communication between patients, families, and healthcare providers.
Goal-Concordant Care: The aim is to align treatment decisions with patient preferences, leading to more personalized and effective care.

Recognizing the limitations of existing approaches, the researchers have adapted adaptive conjoint analysis (ACA) for use with AYA patients. ACA is particularly well-suited for situations with a large number of attributes. ACA surveys have been used to characterize and quantify patient preferences in medical treatments and as decision-making tools in adults. ACA survey interventions have resulted in improvement in patients' knowledge of the risks and benefits of different treatment options, leading to improved shared decision-making.

Looking Ahead: Empowering a Vulnerable Population

The MyPref tool represents a significant step forward in empowering AYA patients, their families, and their healthcare providers to make more informed and preference-aligned treatment decisions. By providing a structured way to clarify and communicate values, this approach has the potential to transform the landscape of AYA oncology care, ensuring that these young adults receive the support and respect they deserve during their most challenging times.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1089/jayao.2018.0116, Alternate LINK

Title: Identifying And Quantifying Adolescent And Young Adult Patient Preferences In Cancer Care: Development Of A Conjoint Analysis-Based Decision-Making Tool

Subject: Oncology

Journal: Journal of Adolescent and Young Adult Oncology

Publisher: Mary Ann Liebert Inc

Authors: Jennifer M. Snaman, Lindsay Blazin, Rachel L. Holder, Joanne Wolfe, Justin N. Baker

Published: 2019-04-01

Everything You Need To Know

Why is there a need to quantify patient preferences in cancer treatment for adolescents and young adults (AYAs)?

The primary goal is to ensure that treatment decisions for Adolescent and Young Adult (AYA) oncology patients align with their individual values and preferences. Given that AYA patients often receive intensive medical services at the end of life, this tool aims to reduce overly intensive end-of-life interventions, instead focusing on goal-concordant care.

How does conjoint analysis help in understanding patient preferences in cancer treatment decisions?

Conjoint analysis is used to understand how individuals value different attributes of a product or service. In this context, cancer treatment is the 'product,' and attributes include factors like quality of life, potential side effects, the possibility of a cure, and survival length. Conjoint analysis considers multiple treatment attributes simultaneously, reflecting the complexity of real-world decisions.

What are discrete choice experiments (DCEs), and how are they used to determine treatment preferences?

Discrete choice experiments (DCEs) are used within conjoint analysis to explore complex decisions. Participants are presented with hypothetical treatment scenarios, each with unique combinations of attributes. By analyzing participants' choices among these scenarios, researchers can quantify the relative importance of each attribute in the decision-making process. This helps in understanding which factors AYA patients prioritize when making treatment decisions.

What is adaptive conjoint analysis (ACA), and what benefits does it offer in determining patient preferences?

Adaptive conjoint analysis (ACA) is particularly useful for situations with a large number of attributes. ACA surveys help characterize and quantify patient preferences in medical treatments and serve as decision-making tools. Interventions using ACA surveys have demonstrated improvements in patients' knowledge of treatment risks and benefits, ultimately leading to improved shared decision-making.

How will the 'MyPref' tool improve treatment decisions and care for young adults with cancer, and what broader implications might it have?

The 'MyPref' tool offers a structured method to clarify and communicate values in AYA oncology care. By quantifying patient preferences through conjoint analysis and discrete choice experiments, the tool empowers AYA patients, their families, and healthcare providers to make informed, preference-aligned treatment decisions. The expected outcome is a transformation in AYA oncology care, ensuring young adults receive the support and respect they deserve during difficult times. However, without additional tools to deal with the emotional trauma, this tool only addresses preferences, not complete care.