Assessment of patients’ preferences for new anticancer drugs in China: a best–worst discrete choice experiment on three common cancer types

Abstract

Objectives

Despite the advancement in anticancer drug therapies, cancer treatment decisions are often complex and preference-sensitive, making them well suited for studying shared decision-making (SDM). Our study aimed to assess preferences for new anticancer drugs among three common types of patients with cancer to inform SDM.

Design

We identified five attributes of new anticancer drugs and used a Bayesian-efficient design to generate choice sets for a best–worst discrete choice experiment (BWDCE). The mixed logit regression model was applied to estimate patient-reported preferences for each attribute. The interaction model was used to investigate preference heterogeneity.

Setting

The BWDCE was conducted in Jiangsu province and Hebei province in China.

Participants

Patients aged 18 years or older, who had a definite diagnosis of lung cancer, breast cancer or colorectal cancer were recruited.

Results

Data from 468 patients were available for analysis. On average, the most valued attribute was the improvement in health-related quality of life (HRQoL) (p<0.001). The low incidence of severe to life-threatening side effects, prolonged progression-free survival and the low incidence of mild to moderate side effects were also positive predictors of patients’ preferences (p<0.001). Out-of-pocket cost was a negative predictor of their preferences (p<0.001). According to subgroup analysis by type of cancer, the improvement in HRQoL remained the most valuable attribute. However, the relative importance of other attributes varied by type of cancer. Whether patients were newly diagnosed or previously diagnosed cancer cases played a dominant role in the preference heterogeneity within each subgroup.

Conclusions

Our study can assist in the implementation of SDM by providing evidence on patients’ preferences for new anticancer drugs. Patients should be informed of the multiattribute values of new drugs and encouraged to make decisions reflecting their values.