Given the growing number of older adults with multimorbidity who are prescribed multiple medications, clinicians need to prioritize which medications are most likely to benefit and least likely to harm an individual patient. The concept of time to benefit (TTB) is increasingly discussed in addition to other measures of drug effectiveness in order to understand and contextualize the benefits and harms of a therapy to an individual patient. However, how to glean this information from available evidence is not well established. The lack of such information for clinicians highlights a critical need in the design and reporting of clinical trials to provide information most relevant to decision making for older adults with multimorbidity. We define TTB as the time until a statistically significant benefit is observed in trials of people taking a therapy compared to a control group not taking the therapy. Similarly, time to harm (TTH) is the time until a statistically significant adverse effect is seen in a trial for the treatment group compared to the control group. To determine both TTB and TTH, it is critical that we also clearly define the benefit or harm under consideration. Well-defined benefits or harms are clinically meaningful, measurable outcomes that are desired (or shunned) by patients. In this conceptual review, we illustrate concepts of TTB in randomized controlled trials (RCTs) of statins for the primary prevention of cardiovascular disease. Using published results, we estimate probable TTB for statins with the future goal of using such information to improve prescribing decisions for individual patients. Knowing the relative TTBs and TTHs associated with a patient's medications could be immensely useful to a clinician in decision making for their older patients with multimorbidity. We describe the challenges in defining and determining TTB and TTH, and discuss possible ways of analyzing and reporting trial results that would add more information about this aspect of drug effectiveness to the clinician's evidence base.