Statistical issues in oncology trials
A key issue in assessing the efficacy of new drugs in oncology is getting the balance right between choosing a hard endpoint survival - and an endpoint that allows evidence to be assessed more quickly progression. Often discussions such as these are required after interactions with regulatory agencies. Progression free survival is used in many studies to assess whether drugs are providing benefit, but it comes with difficulties in analysis and interpretation. In any survival analysis (what we call any "time to-event" analysis), the statistician needs to decide on censoring â€“ patients who "run out" of data. One of the fundamental assumptions in survival analysis is that the censoring is unrelated to the survival times. If a treatment works then you'd hope for longer survival times in one treatment group. If more patients, or more of a certain type of patients, on one of the treatment groups are censored, then this assumption will not hold.
One of the main points of contention relates to handling patients who stop randomised therapy and start a new therapy while on the study. Should these patients be censored or not? There is a good chance that patients who are changing therapies are not doing well on their current therapy, either for safety or efficacy reasons. If the patient is censored, then the negative information about the treatment is ignored, and it's very likely that a bias will be introduced in the analysis, with resultant incorrect estimates of treatment effect. Recently, in a PhRMA sponsored study, it was found that anything other than a strict intent to treat analysis introduces bias. To avoid bias, patients should be followed up until progression, irrespective of whether they remain on the treatment being studied.