Missing Data Mechanisms
deaths, death, tumor
TUMOR GROWTH, TUMOR
In MCT, the tumor growth process is stopped by death (regardless of the cause of death) which entails a dropout and missing tumor volumes. Before applying any statistical method, it is crucial to understand the nature of the missing data as statistical models are not all robust to the same type of missing data mechanism. Two different mechanisms are to be distinguished (Missing at random (MAR): occurrence of missing data is only related to observed data (in the MCT context, some sacrifices of mice are linked to an ethical threshold of tumor volume not to be exceeded (e.g., 1,500 mmMissing not at random (MNAR): occurrence of missing data is related to unobserved data (in MCT context, death due to natural causes). It is an informative study dropout.In our use case, after identifying the missing data process related to deaths, an additional analysis was performed by perturbing the initial dataset to modify the missing data process. Taking into account the observed process, the data were disrupted to have a scenario with only MNAR data by making the last TV measurements unobserved before death. This was done by considering only tumor volume measurements below a threshold that is itself below the ethical threshold. The number of deaths and date of deaths were unchanged in this dataset. The objective of these analyses was to assess the robustness of the estimates provided by the mixed and joint models in the MCT framework.