摘要Progressive pulmonary fibrosis(PPF)is a progressive and lethal condition with few effective treatment options.Improvements in quality of life for patients with PPF remain limited even while receiving treat-ment with approved antifibrotic drugs.Traditional Chinese medicine(TCM)has the potential to improve cough,dyspnea and fatigue symptoms of patients with PPF.TCM treatments are typically diverse and individualized,requiring urgent development of efficient and precise design strategies to identify effec-tive treatment options.We designed an innovative Bayesian adaptive two-stage trial,hoping to provide new ideas for the rapid evaluation of the effectiveness of TCM in PPF.An open-label,two-stage,adaptive Bayesian randomized controlled trial will be conducted in China.Based on Bayesian methods,the trial will employ response-adaptive randomization to allocate patients to study groups based on data col-lected over the course of the trial.The adaptive Bayesian trial design will employ a Bayesian hierarchical model with"stopping"and"continuation"criteria once a predetermined posterior probability of superi-ority or futility and a decision threshold are reached.The trial can be implemented more efficiently by sharing the master protocol and organizational management mechanisms of the sub-trial we have imple-mented.The primary patient-reported outcome is a change in the Leicester Cough Questionnaire score,reflecting an improvement in cough-specific quality of life.The adaptive Bayesian trial design may be a promising method to facilitate the rapid clinical evaluation of TCM effectiveness for PPF,and will pro-vide an example for how to evaluate TCM effectiveness in rare and refractory diseases.However,due to the complexity of the trial implementation,sufficient simulation analysis by professional statistical ana-lysts is required to construct a Bayesian response-adaptive randomization procedure for timely response.Moreover,detailed standard operating procedures need to be developed to ensure the feasibility of the trial implementation.