Hybrid design evaluating new biomarkers when there is an existing screening test.
Yeonhee ParkYing YuanJing NingSuyu LiuZiding FengPublished in: Statistics in medicine (2021)
Development of cancer screening biomarkers usually follows the Early Detection Research Network 5-Phase guideline in Pepe et al. A key feature of this guide is that the phased development follows a sequential order, moving to the next phase only when the current phase study is complete and has met its target performance. Motivated by a newly funded Newly onset Diabetes cohort study, we propose a design evaluating new biomarkers to discriminate between cases and controls in the presence of an existing screening test. The proposed design achieves two goals: (1) avoiding bias in estimating sensitivity or specificity in predicting cancer at a given time period prior to clinical diagnosis, using data from both screening detected cancers in Phase IV study and clinically diagnosed cancers in Phase III study; and (2) building a panel with biomarkers for Phase III and IV studies based on all data. A simulation study shows that the proposed design outperforms both a conventional method using data in Phase III arm only and a naive method using data in Phase III and IV arms ignoring the difference between the time of screening the detected cancer and the time of clinical diagnosis. The proposed design yields a smaller standard error of the estimation and increases the statistical power to confirm biomarker performance. This proposed method has the potential to shorten the cancer screening biomarker development process, use resources more effectively, and bring benefits to patients quickly.
Keyphrases
- phase iii
- open label
- clinical trial
- papillary thyroid
- double blind
- placebo controlled
- phase ii
- squamous cell
- electronic health record
- big data
- randomized controlled trial
- machine learning
- cardiovascular disease
- squamous cell carcinoma
- study protocol
- childhood cancer
- metabolic syndrome
- lymph node metastasis
- young adults
- data analysis
- tyrosine kinase
- public health
- deep learning
- patient reported outcomes
- newly diagnosed
- climate change
- case control
- global health