The analysis of frailty originated in studies of aging and demography in which the objective was to demonstrate that the hazard rates (mortality risks) of individuals in a population could significantly differ from the population hazard rate as a whole. The differences between these two hazard rates can arise from frailty - differences among individuals that are not observed in a study. We posit that frailty modeling is a useful approach for risk analysis in personalized medicine because it provides a way to address the important and perplexing question of how to translate findings from population studies to the diagnosis and treatment of disease in specific individuals. Our suggestion is based on three unique advantages of frailty modeling: frailty modeling offers an effective approach to analyze the risks at both the individual and population levels and can be used to infer relationships between the two; frailty modeling can be used to analyze the dependence between survival events - one of the most difficult issues in any field that involves common risks; and frailty modeling can be used to describe unobserved or unobservable risks. Finally, we suggest that frailty modeling should be particularly useful in the study and treatment of diseases that are caused or influenced by the human microbiome. By doing so, truly 'personalized' medicine can advance based on a better understanding of the risks to both 'trees' (individuals) and 'forests' (populations).