Department of Medical Informatics, Nantong University Medical School, Nantong, Jiangsu, China.
Department of Radiotherapy, Tinghu District People's Hospital, Yancheng, Jiangsu, China.
BMJ Open. 2023 Jun 2;13(6):e072469. doi: 10.1136/bmjopen-2023-072469.
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.
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.
The BWDCE was conducted in Jiangsu province and Hebei province in China.
Patients aged 18 years or older, who had a definite diagnosis of lung cancer, breast cancer or colorectal cancer were recruited.
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.
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.
尽管抗癌药物治疗取得了进展,但癌症治疗决策往往较为复杂且具有偏好性,因此非常适合研究共同决策(SDM)。我们的研究旨在评估三种常见类型癌症患者对新型抗癌药物的偏好,以为 SDM 提供信息。
我们确定了新型抗癌药物的五个属性,并使用贝叶斯有效设计为最佳最差离散选择实验(BWDCE)生成选择集。应用混合 logit 回归模型估计患者对每个属性的报告偏好。应用交互模型来研究偏好异质性。
BWDCE 在江苏省和河北省进行。
招募年龄在 18 岁或以上、明确诊断为肺癌、乳腺癌或结直肠癌的患者。
可分析 468 名患者的数据。平均而言,最有价值的属性是改善健康相关生活质量(HRQoL)(p<0.001)。严重到危及生命的副作用发生率低、无进展生存期延长和轻度到中度副作用发生率低也是患者偏好的积极预测因素(p<0.001)。自付费用是其偏好的负面预测因素(p<0.001)。根据癌症类型的亚组分析,改善 HRQoL 仍然是最有价值的属性。然而,其他属性的相对重要性因癌症类型而异。患者是初诊还是复发性癌症病例,在每个亚组内的偏好异质性中起着主导作用。
我们的研究通过提供患者对新型抗癌药物偏好的证据,有助于实施 SDM。应向患者告知新药的多属性价值,并鼓励他们做出反映自身价值观的决策。