Recovering Loss to Followup Information Using Denoising Autoencoders

Imagine this scenario: In a clinical trial investigating the toxicity of a new chemotherapy drug to treat breast cancer, some patients drop out of the trial before completion for various reasons, hence we do not have the data for final outcome on the dropped out patients. What if the patients who drop out of the trial before completion are the ones who experienced toxicity and are unwilling to continue the treatment, this reason however is not recorded in the database and the patients are marked as "lost to followup". If the investigators were to analyze the data using conventional methods where loss to followup is ignored and not properly accounted for, they will estimate the toxicity to be far less than what it really is. These results can lead to adapting a drug, that is otherwise unsafe. Similarly if patients who are feeling better dropout of the trial before completion, the estimates of toxicity would be far greater than the real value, leading to rejection of a potential lifesaver drug.