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A reliable determination of progression is of key importance in determining progression-free survival in oncology trials. An independent review of tumour assessments made by investigators is often implemented with the aim of reducing a possible bias. Often, the independent review is performed in a prespecified but retrospective fashion by reviewing a patient after all assessments have been performed. It has been discussed that this mechanism can lead to informative censoring with respect to independent review. This is caused by the fact that often no further assessments are available after the investigator has declared the patient to be progressive, possibly leading to a considerable amount of patients being judged progressive by the investigator and being censored by independent review. We introduce and investigate a model for the error in assessment with the aim of quantifying the bias in independent review. The model is based on single error probabilities at each assessment time-point that are independent from each other but dependent on the time to the true progression time-point. The bias introduced for the independent review is described and quantified. We show that the investigator assessments of progression can lead to less bias for progression-free survival than the results for independent review. Results show that a within-arm discordance rate is not necessarily correlated with the bias in independent review. Finally, we propose an approach for a sensitivity analysis that is a useful tool to sandwich the true underlying distribution by the results for independent review itself and the described sensitivity analysis.

Among the surrogate endpoints for overall survival (OS) in oncology trials, progression-free survival (PFS) is more and more taking the leading role. Although there have been some empirical investigations on the dependence structure between OS and PFS (in particular between the median OS and the median PFS), statistical models are almost non-existing. This paper aims at filling this gap by introducing an easy-to-handle model based on exponential time-to-event distributions that describe the dependence structure between OS and PFS. Based on this model, explicit formulae for individual correlations are derived together with a lower bound for the correlation of OS and PFS, which is given by the fraction of the two medians for OS and PFS. Two methods on how to estimate the parameter of the model from real data are discussed. One method is based on a maximum-likelihood estimator whereas the other method uses a plug-in approach. Three examples from non-small cell lung cancer are considered. In the first example, the parameters of the model are determined and the estimated survival curce is compared with the observed one. The second example explains how to obtain sample size estimates for OS based on assumptions on median PFS and OS. Finally, the third example provides a way of modelling and quantifying confounding effects that might explain a levelling of differences in OS although a difference in PFS is observed.

Among the surrogate endpoints for overall survival (OS) in oncology trials, progression-free survival (PFS) is more and more taking the leading role. Although there have been some empirical investigations on the dependence structure between OS and PFS (in particular between the median OS and the median PFS), statistical models are almost non-existing. This paper aims at filling this gap by introducing an easy-to-handle model based on exponential time-to-event distributions that describe the dependence structure between OS and PFS. Based on this model, explicit formulae for individual correlations are derived together with a lower bound for the correlation of OS and PFS, which is given by the fraction of the two medians for OS and PFS. Two methods on how to estimate the parameter of the model from real data are discussed. One method is based on a maximum-likelihood estimator whereas the other method uses a plug-in approach. Three examples from non-small cell lung cancer are considered. In the first example, the parameters of the model are determined and the estimated survival curce is compared with the observed one. The second example explains how to obtain sample size estimates for OS based on assumptions on median PFS and OS. Finally, the third example provides a way of modelling and quantifying confounding effects that might explain a levelling of differences in OS although a difference in PFS is observed.