Haematology covers a great variety of blood-related complex diseases with different causes, therapies, clinical courses and prognosis and it is one of the main functions of clinical research to provide accurate descriptions of history of these various entities. Multidisciplinary experts design individualized treatment programs based on patients and disease characteristics. Effective options include chemotherapy, radiation therapy, targeted or biological therapy, hematopoietic stem cell transplantation (HCT), and more recently chimeric antigen receptor T-cell (CAR-T) therapy. HCT is a widely accepted treatment modality for many haematological malignancies, with both allogeneic and autologous HCT providing a life-prolonging or potentially curative treatment option for patients. HCT has evolved over the past 60 years from experimental therapy for patients with incurable leukemia to standard treatment for a broad range of patients with both myeloid and lymphoid neoplasms. In recent years, CAR-T therapy has improved outcomes in patients with relapsed/refractory haematological malignancies and CAR-T cell therapy indications are growing.

Posada de la Paz and his colleagues [1] define the natural history of a disease as the “natural course of a disease from the time immediately prior to its inception, progressing, through its presymptomatic phase and different clinical stages to the point where it has ended and the patient is either cured, chronically disabled or dead without external intervention.”: as described in the previous paragraph, disease history of haematological malignancies is rather complex. Haematologists are mostly dealing with studies aiming to determine risk factors of outcomes such as progression, infections, death, or comparing effects of different treatments strategies on these outcomes. These studies usually collect baseline characteristics of patients at the time of study onset, time-to-event data and repeated measurements of longitudinal data for each subject. Multiple variables may change during the patients' follow-up and can all influence outcomes. This article reviews statistical methods for time-dependent variables and their applications showing different examples in haematology and HCT setting.