Estimates Of Long-term Survival Rates Research Articles

Survivorship of discarded cuckoo ray in bottom trawl fisheries in the northern Bay of Biscay, Southern Celtic and Irish Seas

Estimating the survivorship of discarded fish is often crucial for stock assessment and resultant management of fisheries resources. In order to assess the survivorship of cuckoo ray (Leucoraja naevus) caught in commercial bottom trawl fisheries, experiments involving French and Irish fisheries were conducted. These experiments were particularly challenging considering the fisheries are offshore. Data from these experiments were analysed jointly in this study. Results from monitoring of individuals held in captivity in onshore facilities and observations of vitality status were combined to produce estimates of long-term survival rates across fishing trips in all four seasons. These rates varied greatly between fishing trips and ranged between 3.6% and 26%. Based on indications that the observed mortality may be at least partly attributable to the conditions of captivity, we propose an approach to reduce the underestimation bias when estimating discard survival. This approach produced higher bounds for the estimated discard survival rates. The estimated survival rates are lower than those of other skate species in the North-East Atlantic, suggesting the cuckoo ray is less resilient to trawl-and-release, although previous studies were conducted in shallower water and with shorter haul duration.

Estimating long-term survival of PD-L1-expressing, previously treated, non-small cell lung cancer patients who received pembrolizumab in KEYNOTE-001 and -010.

77 Background: Pembrolizumab showed promising activity in patients with advanced non–small cell lung cancer (NSCLC) in the KEYNOTE-001 study (NCT01295827) and significantly prolonged overall survival (OS) compared with docetaxel in the randomized KEYNOTE-010 study (NCT01905657). Responses to pembrolizumab appear to be remarkably durable, making long-term survival possible in some patients. Typical parametric survival models do not account for the possibility of long-term survival. An alternative, well-established class of statistical models called long-term survival models can be used to directly estimate the percentage of patients achieving long-term survival (>5 years), called “long-term survival rate.” (Berkson J, Gage RP. J Am Stat Assoc. 1952;47:501-515.)(Tsodikov AD et al. J Am Stat Assoc. 2003;98:1063-1078.) Methods: Data from patients with PD-L1–expressing (tumor proportion score ≥1%), previously treated, advanced NSCLC in KEYNOTE-001 and KEYNOTE-010 were used. KEYNOTE-001 data were used to initially estimate the long-term survival rate of pembrolizumab, while KEYNOTE-010 data were used for subsequent independent validation. Point estimates of long-term survival rates with their 95% CIs based on the model described above are reported. Results: Based on the long-term survival model, the estimated long-term survival rate in pembrolizumab-treated population in KEYNOTE-001 (n = 306) is 25.4% (95% CI, 15.2%-33.3%) and the intention-to-treat population who received pembrolizumab in KEYNOTE-010 (n = 690) is 25.3% (95% CI, 8.9%-36.9%). In contrast, the long-term survival rate of docetaxel arm (n = 343) in KEYNOTE-010 is estimated to be 3.2% (95% CI, 0%-17.4%). Conclusions: In 2 independent data sets, it is estimated that 25% of patients with previously treated PD-L1–expressing NSCLC may achieve long-term benefit from pembrolizumab monotherapy. Long-term survival models can provide an early estimate of long-term benefit from pembrolizumab using data with limited follow-up time. Long-term follow-up from these trials will further validate this finding. MDH and JM are co-first authors.

기간분석에 따른 수정된 누적한계 추정량

임상시험 연구나 역학 연구에서 환자들의 예후는 흔히 생존을 추정을 통해 수량화 되곤 한다. 하지만 코호트 분석이나 완전분석에 의한 생존율 추정량들은 수년 전에 진단된 환자에 크게 의존하기 때문에 실제 생존율보다 더 낮게 추정하곤 한다. 본 연구에서는 최근의 생존정보를 잘 반영하는 생존을 추정을 위해 기간분석 방법을 통한누적한계 추정량을 제안하였고, 그 방법을 1993년 1월-1997년 12월 사이에 조사된 서울시 암등록 자료(Ahn등, 2002)에 적용하여 결과를 고찰하였다. Long-term survival rates are the most commonly used outcome measures for patients with cancer. However, traditional long-term survival statistics, which are derived by cohort analysis or complete analysis, essentially reflect the survival expectations of patients diagnosed many years ago. They are often outdated at the time they become available. In this article, we propose a modified product-limit method to obtain up-to-date estimates of long-term survival rates via a period analysis. The proposed method is illustrated with cancer registry data collected from January 1993 to December 1997.

Period Analysis of Prostate Cancer Survival

More than 220,000 men will be diagnosed with prostate cancer this year, representing about one third of all cancers diagnosed among men, and more than any other cancer. Once diagnosed, men and their families often face difficult treatment decisions, ranging from radical prostatectomy and radiation to watchful waiting. Unfortunately, for some men, the disease continues to progress, resistant to all forms of treatment. Many questions remain in our quest to understand better ways to prevent, diagnose, and treat prostate cancer. In the 1990s and early 2000s, we witnessed a marked initial increase in new diagnoses, followed by a plateau in the incidence of the disease, likely attributable to the increasing use of prostate-specific antigen (PSA). After a frightening increase to approximately 40,000 American prostate cancer deaths per year, we have experienced a decline in prostate cancer-related deaths to approximately 28,900 in 2003. Why did this reduction in the death rate occur? What can we do to sustain this rate of improvement in survival? How does a patient improve his chances of surviving his prostate cancer 5 or 10 years from now? And who should be implementing the changes in how we approach this disease? Brenner and Arndt, in this issue of the Journal of Clinical Oncology, do not provide definitive answers to any of these questions. But, they do provide helpful, up-to-date information about the survival experience among men diagnosed with prostate cancer. The bottom line is that most men diagnosed with the disease today can expect to live as long as, or longer than, men their age without the disease. Given the many uncertainties about this disease, this information alone will be helpful for clinicians and their patients when discussing treatment options and when considering what life will be like, living as a prostate cancer survivor. The authors of the study used a novel method to estimate long-term survival experiences of men diagnosed with the disease in an era of rapidly changing patterns of care of this disease. This is not a report on the value of PSA screening. PSA screening started to enter clinical practice in the late 1980s in the United States. By 2000, more than 50% of men aged 65 years or older had undergone a PSA test within the past year. Clearly, the 1990s represented a transition decade, when American men and their physicians were increasingly utilizing PSA screening. This resulted in an increase in diagnoses and in stage migration to more localized cancers, as well as an increase in the number of men undergoing therapy. In such a dynamic setting, standard cohort analyses for 5and 10-year survival estimates would follow men diagnosed more than a decade ago; therefore, any impact of changes in patterns of care introduced recently might not be detected. The study by Brenner and Arndt used period analysis of more recent data; therefore, the impact of current treatment and diagnostic approaches is more likely reflected in the results. What is period analysis? In the context of this study, estimates of 5and 10-year survival were generated using data on the survival experience of patients in the year 2000. To obtain survival estimates with this approach, the entire experience of cohorts of patients diagnosed in the previous 10 consecutive years (1990 to 1999) is used (not just those diagnosed a decade or more ago). The data from these more recent cohorts are then used to estimate 5and 10-year absolute and relative survival rates. In contrast, more traditional cohort analysis would use data solely from men diagnosed in 1995 and 1990 to calculate the 5and 10-year survival estimates. Importantly, this period analysis approach has been shown in other evaluations to provide more timely estimates of long-term survival rates than traditional survival analyses, as period analysis survival estimates for a particular year have closely reflected the survival eventually observed in patients diagnosed in that year. In the Brenner and Arndt study, Surveillance, Epidemiology, and End Results data on 180,605 men diagnosed in the United States between 1990 and 2000 were used to calculate both absolute and relative survival rates of prostate cancer patients compared with the general population. The results presented are very encouraging. Absolute JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 23 NUMBER 3 JANUARY 2

Period analysis for ‘up-to-date’ cancer survival data: theory, empirical evaluation, computational realisation and applications

Long-term survival rates are the most commonly used outcome measures for patients with cancer. However, traditional long-term survival statistics, which are derived by cohort-based types of analysis, essentially reflect the survival expectations of patients diagnosed many years ago. They are therefore often severely outdated at the time they become available. A couple of years ago, a new method of survival analysis, denoted period analysis, has been introduced to derive more ‘up-to-date’ estimates of long-term survival rates. We give a comprehensive review of the new methodology, its statistical background, empirical evaluation, computational realisation and applications. We conclude that period analysis is a powerful tool to provide more ‘up-to-date’ cancer survival rates. More widespread use by cancer registries should help to increase the use of cancer survival statistics for patients, clinicians, and public health authorities.

Combining cohort and period methods for retrospective time trend analyses of long-term cancer patient survival rates.

Assessing trends in long-term cancer patient survival is an essential component of monitoring progress against cancer by cancer registries. Traditional assessment of long-term survival (‘cohort analysis’) is very useful to disclose trends in long-term survival rates of patients diagnosed many years ago, but it does not allow the disclosure of recent trends in long-term survival rates. The latter can be achieved by an alternative method of survival analysis (‘period analysis’), which has been proposed a few years ago. On the other hand, unlike cohort analysis, period analysis does not provide estimates of long-term survival rates for patients diagnosed in the early years after initiation of cancer registration. In this paper, a method of retrospective analysis of time trends in long-term survival rates is introduced, which combines the advantages of both cohort and period analysis (‘mixed analysis’). This method thereby allows for a comprehensive monitoring of trends in long-term survival over an extended time span from the earliest to the most recent years of cancer registration. The use of the method is illustrated for retrospective time trend analyses of long-term survival of cancer patients in the United States with the 1973–1999 database of the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute.

Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis

Long-term survival rates for many types of cancer have substantially improved in past decades because of advances in early detection and treatment. However, much of this improvement is only seen many years later with traditional cohort-based methods of survival analysis. I aimed to assess achievements in cancer patients' survival by an alternative method of survival analysis,known as period analysis, which provides more up-to-date estimates of long-term survival rates than do conventional methods. The 1973-98 database of the Surveillance, Epidemiology, and End Results (SEER) programme of the US National Cancer Institute was analysed by period analysis. Estimates of 5-year, 10-year, 15-year, and 20-year relative survival rates for all types of cancer were 63%, 57%, 53%, and 51%, respectively, by period analysis. These estimates were 1%, 7%, 11%, and 11% higher, respectively, than corresponding estimates by cohort-based survival analysis. By period analysis, 20-year relative survival rates were close to 90% for thyroid and testis cancer, exceeded 80% for melanomas and prostate cancer, were about 80% for endometrial cancer, and almost 70% for bladder cancer and Hodgkin's disease. A 20-year relative survival rate of 65% was estimated for breast cancer, of 60% for cervical cancer, and of about 50% for colorectal, ovarian, and renal cancer. Timely detection of improvements in long-term survival rates might help to prevent clinicians and their patients from undue discouragement or depression by outdated and often overly pessimistic survival expectations. It also adds to the value of cancer surveillance as a basis for appropriate public-health decisions.

Use of period analysis for providing more up-to-date estimates of long-term survival rates: empirical evaluation among 370 000 cancer patients in Finland

Providing up-to-date estimates of cancer patient survival rates is an important task of cancer registries. A few years ago, a new method of survival analysis, denoted period analysis, was proposed to enhance the recency of long-term survival estimates. The aim of this paper is to provide a comprehensive empirical evaluation of the use of this method. Using data from the nationwide Finnish Cancer Registry, we compare 5-year and 10-year relative survival rates of 371 849 patients diagnosed with one of the 16 most common forms of cancer in Finland at various time intervals between 1953 and 1992 with the most up-to-date estimates of 5-year or 10-year relative survival that might have been obtained in those time intervals by traditional methods of survival analysis and by period analysis of survival. Survival rates strongly increased over time for most forms of cancer. For these cancers, traditional estimates of 5- and 10-year survival rates would have severely lagged behind the survival rates later observed for newly diagnosed patients, and period analysis would consistently have provided much more up-to-date estimates of survival rates. We conclude that period analysis should be implemented as a standard tool for providing up-to-date estimates of long-term survival rates by cancer registries.

Use of period analysis for providing more up-to-date estimates of long-term survival rates: empirical evaluation among 370 000 cancer patients in Finland

Providing up-to-date estimates of cancer patient survival rates is an important task of cancer registries. A few years ago, a new method of survival analysis, denoted period analysis, was proposed to enhance the recency of long-term survival estimates. The aim of this paper is to provide a comprehensive empirical evaluation of the use of this method.Using data from the nationwide Finnish Cancer Registry, we compare 5-year and 10-year relative survival rates of 371 849 patients diagnosed with one of the 16 most common forms of cancer in Finland at various time intervals between 1953 and 1992 with the most up-to-date estimates of 5-year or 10-year relative survival that might have been obtained in those time intervals by traditional methods of survival analysis and by period analysis of survival.Survival rates strongly increased over time for most forms of cancer. For these cancers, traditional estimates of 5- and 10-year survival rates would have severely lagged behind the survival rates later observed for newly diagnosed patients, and period analysis would consistently have provided much more up-to-date estimates of survival rates.We conclude that period analysis should be implemented as a standard tool for providing up-to-date estimates of long-term survival rates by cancer registries.

A computer program for period analysis of cancer patient survival

Monitoring of long-term survival rates, which is now routinely performed by many cancer registries throughout the world, should be as up-to-date as possible. A few years ago, a new method of survival analysis, denoted period analysis, has been proposed which provides more up-to-date estimates of long-term survival rates than traditional survival analysis by exclusively reflecting the survival experience of patients within a recent calendar period. However, application of this method has so far been hindered by the lack of pertinent computer programs. In this paper, we present a simple and easy-to-use computer program (SAS macro) that enables one to carry out period analysis (as well as conventional analysis) of both absolute and relative survival rates with the type of data commonly available in population-based cancer registries. We illustrate application of the program with examples from the nationwide Finnish Cancer Registry.

Long-term cancer patient survival achieved by the end of the 20th century: most up-to-date estimates from the nationwide Finnish cancer registry.

A new method of survival analysis, denoted period analysis, has recently been developed, which has been shown to provide more up-to-date estimates of long-term survival rates than traditional methods of survival analysis. We applied period analysis to data from the nationwide Finnish cancer registry to provide up-to-date estimates of 5-, 10-, 15- and 20-year relative survival rates (RSR) achieved by the end of the 20th century. For most forms of cancer, period estimates of long-term survival are much higher than corresponding traditional survival estimates which suggests that for these cancers there has been ongoing major progress in survival rates in recent years which so far has remained undisclosed by traditional methods of survival analysis. For example, period analysis reveals that 10 year RSR have come close to (or even exceed) 80% for cancer of the corpus uteri and melanoma, 75% for breast cancer, 70% for bladder cancer, 65% for cancer of the cervix uteri, and 55% for cancer of the colon and prostate. Period analysis further reveals that 20 year RSR have now come close to (or even exceed) 75% for endometrial cancer and melanoma, 60% for breast cancer and cervical cancer, 55% for colon cancer and bladder cancer, and 40%–50% for cancer of the rectum, the ovaries, kidneys and nervous system. © 2001 Cancer Research Campaign http://www.bjcancer.com

Long-term survival of children with leukemia achieved by the end of the second millennium

The prognosis for patients with childhood leukemia has improved steadily over the last decades due to major progress in therapy. Much of this progress remains unaccounted for in traditional estimates of long-term survival rates, which essentially reflect the survival experience of patients who were diagnosed many years ago. The authors applied a new method of survival analysis, called period analysis, to provide up-to-date estimates of long-term survival rates. The analysis is based on data from the nationwide German Childhood Cancer Registry and includes 8059 children who were diagnosed with leukemia between 1981 and 1998. The most up-to-date 5-year, 10-year, and 15-year survival estimates were obtained by period analysis and were compared with to the most up-to-date survival estimates from traditional methods of survival analysis. Period estimates (95% confidence intervals) of 5-year, 10-year, and 15-year survival rates achieved by 1998 were 81% (79-83%), 77% (74-79%), and 73% (70-76%), respectively, for all patients with leukemia combined; 86% (84-88%), 81% (79-84%), and 77% (74-81%), respectively, for patients with acute lymphocytic leukemia; and 59% (53-65%), 59% (53-65%), and 57% (49-64%), respectively, for patients with acute nonlymphocytic leukemia. Substantially lower estimates would have been obtained with traditional methods of survival analysis. These results from one of the world's largest childhood cancer registries reveal that cure rates of childhood leukemia achieved by the end of the second millennium are higher than suggested by previous estimates based on traditional methods of survival analysis.

More Up-To-Date Monitoring of Long-Term Survival Rates by Cancer Registries: An Empirical Example

A few years ago, a new method of survival analysis, denoted period analysis, has been developed to derive more up-to-date estimates of long-term survival rates of cancer patients, but this method has rarely been applied so far. Using empirical examples from the Saarland Cancer Registry, we illustrate how seriously traditional long-term survival estimates may lag behind survival expectations of newly diagnosed cancer patients in the case of recent improvement in prognosis, and to what extent this problem may be reduced by period analysis of survival. We conclude that period analysis should be more widely used for deriving more up-to-date long-term survival estimates.