1. Acknowledgements This report differs from the submitted abstract due to further subdivision of patients into analytic and non- analytic, and focus on the non-analytic patients. Thanks to Carol Wages for data registry access, and Michele Sumner for chart reviews. Statistics with Statistica 5.5. Conclusions Stage 4 cancer patients did not come to CTCA at a random moment in their illness. Analytic patients came at the beginning, at an average 16% point in their disease course. Non-analytic patients had already lived through 2/3 of their cancer course, on average, when they first came to CTCA. Length of time living with cancer before coming to CTCA was a very significant factor influencing how long they would subsequently live. Patients diagnosed more than 2 years before presenting went on to have an additional 10.3 month survival advantage over those diagnosed less than 2 years before. Long survivors continued to survive longer. This 10.3 month advantage in survival greatly outstrips the benefit seen in many chemotherapy studies for stage 4 cancers – raising the issue that this could be an important form of bias in non-randomized studies for 2 nd and 3 rd line cancer therapies. Was there anything special about long term survivors? Yes! They were younger at diagnosis, more often female, tended to have an earlier stage at diagnosis, and had more breast cancer and less lung cancer. Long term survivors are different in many ways, both obvious and unobvious. Their cancer may have a better biology, they may be healthier and stronger, and they may be more motivated to fight the cancer. This “long survivor bias” is like a meta-bias, incorporating multiple factors both known and unknown into a simple metric: length of past survival with the condition before the point of consideration. Factors like motivation are hard to quantify when trying to balance study groups, but length of past survival with cancer is very easy to calculate and balance. This bias would be applicable to many other illnesses, such as HIV/AIDS, heart disease, and to other cancer situations. Cancer has become more of a chronic disease for some patients. Recurrences are often managed aggressively, and patients with relapses will often travel to new cancer centers or enroll in clinical trials when a previous treatment has failed. In these cases, length of past survival becomes an important source of bias. Cancer survivors should not be disheartened if they are early in their fight against cancer – even long term survivors were once short term survivors. Although this principle appears to have value in comparing groups, it is not intended to predict the survival of an individual with cancer. Self-Referral The date of self-referral to a different cancer center is probably not a random moment for most cancer patients. It is frequently at a point where the cancer has recurred or progressed or no further treatment options are made available. The reason for transferring to a new hospital would be expected to have an influence on the subsequent survival: Positive influence would be newly diagnosed (analytic) No effect or truly random would be if a patient is moving, or the cancer is in control Negative influence would be if the cancer has progressed, or health is deteriorating, and/or the patient has run out of options at his current center. Introduction When J.R. Gott first came upon the Berlin Wall in 1969 during some travels, it had been standing for 8 years. He estimated there was a 50% chance it would stand for another 2.6 to 24 years if he was seeing it at a random moment in its life. It came down in 1989, 20 years later. Gott’s Principle implies that the longer something has been in existence, the longer it should continue to exist on average, if it is being examined at a random time. Can this principle be applied to stage 4 cancer patients? Stage 4 cancer has a beginning (the date of diagnosis), and unfortunately usually an end (the date of death). When patients transfer to another hospital, the date of first contact at the new hospital becomes an intermediate point in their disease course. Can the length of past survival be predictive of average future survival? Do patients transfer to a new hospital at a random point in their cancer course? This is not intended as a prognosticator for individual patients. An individualized prognosis usually incorporates cancer biology, extent of spread, prior treatments, performance status, and other factors. Gott’s principle ignores all these prognostic factors and simply looks at past longevity. Results Baseline characteristics of the patients Non – Analytic Analytic =24 mosP N7411998 Age at Dx59.754.252.6.00004 Male Sex60%54%34%.01 Stage 3/4100% 74%36%.0000 at Diagnosis Primary.00000 Lung50%41%9% Breast8%12%38% Colorectal15%23%19% Prostate7%2%13% Other21%22%20% Median Survival Durations Non – Analytic Analytic<24 mos≥24 mosP Past1.4 mos11.2 mos48.9 mos Survival Future9.8 mos6.7 mos17 mos.00002 Survival between <24, ≥24 groups Point in16%57%77%.003 Disease between <24, ≥24 groups Long Term Cancer Survivors will Continue to Survive Longer: Gott’s Principle in Stage 4 Non-Analytic Cancer Patients Douglas A. Kelly, MD Cancer Treatment Centers of America, Tulsa, OK ZeroPSA@gmail.com Patients who were diagnosed more than 24 months before coming to CTCA had a 10.3 month median survival advantage over those who were diagnosed less than 24 months before coming to CTCA. Methods The cancer registry was searched for all stage 4 cancer patients who were first registered in 2003 in a single cancer hospital (CTCA – Tulsa). 292 patients were identified with current cancer stage 4. They were subdivided into 74 analytic and 218 non- analytic patients. Past survival was calculated from the date of diagnosis until the date of first contact at CTCA. Future survival was from the date of first contact until the date of death or last follow-up. Non-Analytic patients were further subdivided into two groups based on past survival of less than 24 months or greater than 24 months. Analysis was primarily limited to non-analytic patients in this study. All analytic cases were stage 4 at diagnosis. Non-analytic patients at diagnosis were stage 1 (10%), stage 2 (31%), stage 3 (19%), stage 4 (34%), and unrecorded (6%). All of these patients had a current stage of 4 when they first came to CTCA. Median future survival times were calculated with life table (Kaplan Meier) methods to account for patients who are still alive, or who were lost to follow-up. Point in disease was calculated as the point in their cancer course the patient first came to CTCA. The formula is (past survival) / (past survival + future survival). This was treated with life table methods. Analytic: Recently diagnosed and received all, or part of, the first course of therapy at this hospital. (74 pts) Non-Analytic: Received first course of treatment elsewhere and now transferring to this hospital. (218 pts)