Bivariate survival analysis with complete or censored data

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Measuring the level of dependence between two variables is of great importance in practice. For example, suppose data are collected on patients receiving heart transplantation. It may be of interest to determine if there is a relationship between age at transplantation and the survival time following the operation. Such measures of association are not easy to estimate in presence of incomplete data. The two most common phenomenons causing incomplete data are censoring and truncation. For instance, an observation is said to be "right-censored" if the only information available to us is that its true value is greater than some number c. A variable X is said to be "left-truncated" when only individuals whose value of X is greater than some number c are included in the sample (other individuals are completely missed by the data collection process). There exist several other schemes of incomplete data. I intend to develop methods of estimating the dependence between two variables under such settings.