Transportability Analysis—A Tool for Extending Trial Results to a Representative Target Population
Year: 2024
Type: article
Abstract: Randomized controlled trials estimate the causal effect between an intervention and the outcome on a target population through careful design to control for potential biases.However, this effect varies based on how the target population is defined, and in practice, physicians are tasked with determining the relevance of RCT results for patient populations with different characteristics than the study population.1,2 Approaches that extend inferences from a study population to a representative target, such as transportability analysis, provide powerful tools to address questions like who would most benefit from a clinical intervention.3,4 Lung cancer remains the leading cause of cancer-related death worldwide, driven by the advanced disease at the time of diagnosis.While screening the general population using chest radiographs showed no benefit, evidence from two large trials showed lung cancer mortality benefit when screening high-risk populations based on age and smoking history using low-dose computed tomography (LDCT).The National Lung Screening Trial enrolled 53,454 participants randomly assigned to undergo three annual screens with either low-dose CT (n=26,722) or chest radiography (n=26,732).5 The trial reported a 20% (95% CI, 6.8-26.7%)relative risk reduction of lung cancer-related mortality in men and women compared to chest radiography at 6.5-year follow-up.The Dutch/Belgian randomized lung cancer screening trial (NELSON) reported its survival results involving 15,792 participants who were screened at 1, 3, and 6 years, showing 24% (95% CI, 7-39%) relative risk reduction of lung cancer-related mortality in men undergoing LDCT screening as compared to no screening at 10-year follow-up.6 These trials show compelling evidence for the studied
Source: JAMA Network Open
Authors: Kosuke Inoue, William Hsu
Institutions: Kyoto University, University of California, Los Angeles
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W4391346547