Distribution of the probability of survival is a strategic issue for randomized trials in critically ill patients

• Published in: Anesthesiology (Philadelphia) 2001-07, Vol.95 (1), p.56-63
• Main Authors: RIOU, Bruno, LANDAIS, Paul, VIVIEN, Benoit, STELL, Philippe, LABBENE, Iheb, CARLI, Pierre
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; Biological and medical sciences; Critical Illness - therapy; Female; Glasgow Coma Scale; Humans; Intensive care medicine; Male; Medical sciences; Middle Aged; Miscellaneous; Probability; Randomized Controlled Trials as Topic - statistics & numerical data; Research Design; Survival Analysis; Wounds and Injuries - therapy
• ISSN: 0003-3022; 1528-1175
• DOI: 10.1097/00000542-200107000-00014

Many randomized clinical trials in trauma have failed to demonstrate a significant improvement in survival rate. Using a trauma patient database, we simulated what could happen in a trial designed to improve survival rate in this setting. The predicted probability of survival was assessed using the TRISS methodology in 350 severely injured trauma patients. Using this probability of survival, the authors simulated the effects of a drug that may increase the probability of survival by 10-50% and calculated the number of patients to be included in a triad, assuming alpha = 0.05 and beta = 0.10 by using the percentage of survivors or the individual probability of survival. Other distributions (Gaussian, J shape, uniform) of the probability of survival were also simulated and tested. The distribution of the probability of survival was bimodal with two peaks (< 0.10 and > 0.90). There were major discrepancies between the number of patients to be included when considering the percentage of survivors or the individual value of the probability of survival: 63,202 versus 2,848 if the drug increases the probability of survival by 20%. This discrepancy also occurred in other types of distribution (uniform, J shape) but to a lesser degree, whereas it was very limited in a Gaussian distribution. The bimodal distribution of the probability of survival in trauma patients has major consequences on hypothesis testing, leading to overestimation of the power. This statistical pitfall may also occur in other critically ill patients.