Background: Families and friends provide a considerable proportion of care for patients and elderly people. Caregiving can have substantial effects on caregivers' lives, health, and well-being. However, because clinical trials rarely assess these effects, no information on caregiver burden is available when evaluating the cost effectiveness of treatments. Objective: This study develops an algorithm for estimating caregiver time using information that is typically available in clinical trials: the EQ-5D scores of patients and their gender. Methods: Four datasets with a total of 8012 observations of dyads of caregivers and a gamma model with a log-link estimated with the Bayesian approach were used to estimate the statistical association between patient scores on the EQ-5D-3L dimensions and the numbers of hours of care provided by caregivers during the previous week. The model predicts hours of care as mean point estimates with 95% credible intervals or entire distributions. Results: Model predictions of hours of care based on the five EQ-5D dimensions ranged from 13.06 (12.7-14.5) h/week for female patients reporting no health problems but receiving informal care to 52.82 (39.38-66.26) for male patients with the highest level of problems on all EQ-5D dimensions. Conclusions: The iCARE algorithm developed in this study allows researchers who only have patient-level EQ-5D data to estimate the mean hours of informal care received per week, including a 95% Bayesian credible interval. Caregiver time can be multiplied with a monetary value for caregiving, enabling the inclusion of informal care costs in economic evaluations. We recommend using the tool for samples that fall within the confidence intervals of the characteristics of our samples: men (age range 47.0-104.2 years), women (age range 55-103 years).