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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.
Programming issues in oncology trials
Oncology trials are complex and require a different approach to trials compared with many other therapeutic areas and can generate significant programming challenges.
Oncology can be associated with fast developing disease and short survival times. Due to the fact that many oncology trials are event-driven, the timelines and resources are regularly reviewed and updated. In earlier phase oncology trials, there are frequent data reviews to assess for safety and/or efficacy, which can present challenges in the planning and delivery of programming tasks. Standards programs are often developed so that they can be used across different studies, and re-used across multiple deliveries. This helps reducing the programming time on each study and therefore meeting the tight timelines specific to the oncology therapeutic area. Close collaboration between programmers, data managers and clinicians is required to ensure data issues are promptly resolved.
Clinical oncology trials are also more complex than those in other therapeutic area. The design is often more complicated, with additional data being collected such as quality of life questionnaires, genetic and biomarkers data. Analyses performed are often more specific to this therapeutic area. Therefore, the datasets can be quite complex and below are some examples of the biggest programming challenges.
Use of the Continual Reassessment Method (CRM) in phase 1 oncology trials
By Gareth James, Senior Statistician, PHASTAR
The purpose of a phase I clinical trial is to determine the recommended dose for further testing whilst minimising the number of patients used and preserving safety. Oncology trials are different to those for other indications as patients have metastatic disease and have exhausted other treatment options. It is therefore important to minimise allocation to toxic and sub-optimal doses.
The 3+3 method is the most common dose-escalation design, with over 96% of phase I studies using this method, but it is not statistically efficient as it does not use all available data to recommend the next dose level to allocate. Approximately 100 publications have demonstrated the advantages of using model based methods such as the CRM compared to the 3+3 method. This included reducing the number of patients allocated to toxic and sub-optimal doses, and identifying the true maximum tolerated dose (MTD) more frequently, which would reduce the likelihood of making a costly and potentially unsafe decision.