Weighing risks and benefits in the presence of competing risks

When competing events occur, there are two main options for handling them analytically that invoke different assumptions: 1) censor person-time after a competing event (which is akin to assuming they could be prevented) to calculate a conditional risk; or 2) do not censor them (allow them to occur) to calculate an unconditional risk. The choice of estimand has implications when weighing the relative frequency of a beneficial outcome and an adverse outcome in a risk-benefit analysis.

We review the assumptions and interpretations underlying the two main approaches to analyzing competing risks. Using a popular metric in risk-benefit analyses, the Benefit-Risk Ratio, and a toy dataset, we demonstrated that conclusions about whether a treatment was more beneficial or more harmful can depend on whether one uses conditional or unconditional risks.

We argue that unconditional risks are more relevant to decision-making about exposures with competing outcomes than conditional risks.