A key hypothesis in epidemiological studies is that time to disease exposure provides relevant information to be considered in statistical models. However, the initiation time of a particular condition is usually unknown. Therefore, we developed a multiple imputation methodology for the age at onset of a particular condition, which is supported by incidence data from different sources of information. We introduced and illustrated such a methodology using simulated data in order to examine the performance of our proposal. Then, we analyzed the association of gallstones and fatty liver disease in the Maule Cohort, a Chilean study of chronic diseases, using participants' risk factors and six sources of information for the imputation of the age-occurrence of gallstones. Simulated studies showed that an increase in the proportion of imputed data does not affect the quality of the estimated coefficients associated with fully observed variables, while the imputed variable slowly reduces its effect. For the Chilean study, the categorized exposure time to gallstones is a significant variable, in which participants who had short and long exposure have, respectively, 26.2% and 29.1% higher chance of getting a fatty liver disease than non-exposed ones. In conclusion, our multiple imputation approach proved to be quite robust both in the linear/logistic regression simulation studies and in the real application, showing the great potential of this methodology.