Assessing causal relationships between treatments and clinical outcomes: always read the fine print

Changes in clinical practice should be driven by relevant and reliable evidence. Hence, adoption of a new therapy requires demonstrating that it provides (causes) benefit. Such evidence is generally obtained from intent-to-treat analyses of randomized clinical trials (RCTs). In this paper, we review other approaches to assessing the causal relationship between treatments and outcomes: (1) inference from non-randomized (observational) studies, (2) analysis of randomized studies where patients received treatments other than those to which they were randomized and (3) analysis of studies where the outcome of interest is sometimes unobservable because of a competing event (competing risks). We conclude that for the practice-changing demonstration of a favorable benefit-to-risk ratio, the gold standard is the intent-to-treat analysis of RCTs. At the same time, we illustrate how careful application of special statistical methods for assessment of treatment–outcome causation can be instrumental in complementing existing randomized evidence and guiding design of future research.