ABSTRACT In biomedical research, understanding the dynamic relationships between two binary variables over time is crucial. Our study enhances this understanding by employing longitudinal analysis to introduce measures such as the bivariate time‐varying odds ratio and relative risk. These metrics adeptly quantify evolving associations and effectively address the complexities involved in estimating variables recorded at disparate times. We have developed a nonparametric approach specifically designed for longitudinal samples that vary in their measurement timelines, demonstrating its applicability to both concurrent and nonconcurrent sampling scenarios. Additionally, in studies where end‐of‐life considerations are prevalent, missing data can significantly skew results. To mitigate this, we implemented a model that accounts for missingness and developed an inverse‐probability weighting method that has been validated through simulation studies to correct biases effectively. By applying our methodology to the Framingham Heart Study, we investigated the temporal changes in the association of hypertension among mothers and daughters over a 45‐year span. This application not only underscores the versatility of our approach but also provides valuable insights into long‐term health trends within families.
Liu et al. (Sun,) studied this question.