oncologists. Logistic regression models were used to identify patient factors associated with choosing ET regimens only.Results: For patients (n¼300) and oncologists (n¼200) across both DCEs, improving iDFS was most important (1.5-4 times more important than the next most important attribute), followed by neutropenia and diarrhea risk for patients and oncologists, respectively. Dosing schedule, alopecia risk, and ECG monitoring were typically least important for both groups. More patients (24%) than oncologists (9%) selected ET exclusively over CDK4/6i + ET. Patient factors associated with selecting ET monotherapy included natural menopause (vs. induced/premenopausal), stage II (vs. stage III) BC, and not college educated.Conclusions: Patients and oncologists were generally willing to accept increased risks of adverse events with combination CDK4/6i regimens in exchange for improved iDFS. However, patients placed relatively higher importance on safety-related attributes, emphasizing the need for clinicians to communicate and manage potential adverse events with patients to support them in achieving their treatment goals in early BC.

Objectives: There are currently no clear guidelines from healthcare authorities on how to conduct network meta-analysis (NMA) on survival endpoints. This research aims to compare two different methods to conduct network meta-analyses of survival outcomes and explore a number of approaches for extrapolation beyond the follow-up time and give recommendations on when each method should be implemented. MethOds: NMAs based on (1) hazard ratios and (2) reconstructed patient-level data using fractional polynomials were fitted to two different networks of evidence for both overall survival (OS) and progression-free survival (PFS). Assessments were made of the number of statistically significant non-proportional hazard ratios and also the loss of information when only those studies reporting Kaplan-Meier curves were analyzed. Anchoring methods were also conducted using external data to ensure plausible long-term predictions. Results: In the networks studied, 11% of trials reporting OS contained significant non-proportional hazard ratios compared to 29% for PFS. There was a 10% loss of trials from OS and 26% from PFS when only trials that reported Kaplan-Meier charts were included in the networks of evidence. The NMA based on fractional polynomials with anchoring produced survival estimates that fitted most of the study arms well and produced plausible long-term predictions. The NMA based on hazard ratios fitted the data relatively less well and did not produce plausible long-term survival estimates; additional techniques could be used to improve long-term predictions. cOnclusiOns: NMAs based on reconstructed survival data have some clear advantages over simpler methods based on hazard ratios. However, due to the loss of information and potential selection bias when only studies reporting Kaplan-Meier charts are included in a network of evidence, the fractional polynomial method may not always be sufficient if used alone. In some cases the hazard ratio approach may still be the preferred option.

A91transition probabilities until predicted prevalence of each health state and agespecific incidence of esophageal cancer were similar to findings from esophageal cancer screening in high-risk areas of China. Results: Annual transition probabilities were 0.024,0.05, and 0.12 for normal to mild dysplasia,mild dysplasia to moderate dysplasia, and moderate dysplasia to severe dysplasia/carcinoma in situ (CIS), respectively.Age-specific progression probabilities were 0.08-0.18 for severe dysplasia to intramucosal carcinoma,0.40-0.87 for intramucosal carcinoma to submucosal carcinoma (T1N0M0), and 0.2-0.85 for submucosal carcinoma to invasive carcinoma.As for regression,transition probabilities were 0.05 for mild dysplasia to normal,and 0.08 for moderate dysplasia to mild dysplasia,and 0.09-0.17 for severe dysplasia/CIS to moderate dysplasia.Predicted incidence of esophageal cancer increased with age, and model generated estimates for prevalence and incidence were consistent with empirical data. ConClusions: We obtain reliable transition probabilities from a Markov model based on empirical data.Our model can be potentially useful for understanding the natural history of esophageal cancer.