Survival Of Cancer Cases Research Articles

Evaluating cancer screening programs using survival analysis.

The main purpose of cancer screening programs is to provide early treatment to patients that are diagnosed with cancer on a screening test, thus increasing their chances of survival. To test this hypothesis directly, one should compare the survival of screen-detected cases to the survival of their counterparts not included to the program. In this study, we develop a general notation and use it to formally define the comparison of interest. We explain why the naive comparison between screen-detected and interval cases is biased and show that the total bias that arises in this case can be decomposed as a sum of lead time bias, length time bias, and bias due to overdetection. With respect to the estimation, we show what can be estimated using existing methods. To fill in the missing gap, we develop a new nonparametric estimator that allows us to estimate the survival of the control group, that is, the survival of cancer cases that would be screen-detected among those not included to the program. By joining the proposed estimator with existing methods, we show that the contrast of interest can be estimated without neglecting any of the biases. Our approach is illustrated using simulations and empiricaldata.

Cancer Registration in Nepal: Current Status and Way Forward

Cancer registration is an organization for the systematic collection, storage, analysis, interpretation and reporting of data on subjects with cancer. Cancer Registry was initiated in 1995 and expanded as National Cancer Registry Program since 2003 by B.P. Koirala Memorial Cancer Hospital with the support of World Health Organization. National cancer registry program currently includes 12 hospital-based registries. First time in Nepal, B.P. Koirala Memorial Cancer Hospital piloted population-based cancer registry in 2013, which included 15 districts covering 25.8% of total population of Nepal. National cancer registry program is important to assure the quality of data from all the registries to ensure the availability of reliable and valid data of cancer cases. This will further help policymakers to develop preventive and control strategies against cancer. This paper reviews the current status of cancer registries in Nepal and discusses challenges and future perspectives related to national cancer registry program. National cancer registry should further include major hospitals in Nepal to give scientific information on cancer trends by community, provinces and regions and to analyze on survival of cancer cases. Keywords: cancer; national cancer registry program; Nepal.

A model approach to calculate cancer prevalence from 5 years survival data for selected cancer sites in India--part II.

Prevalence is a statistic of primary interest in public health. In the absence of good follow-up facilities, it is often difficult to assess the complete prevalence of cancer for a given registry area. An attempt is made to arrive at the complete prevalence including limited duration prevalence with respect of selected sites of cancer for India by fitting appropriate models to 1, 3 and 5 year cancer survival data available for selected registries of India. Cancer survival data, available for the registries of Bhopal, Chennai, Karunagappally, and Mumbai was pooled to generate survival for the selected cancer sites. With the available data on survival for 1, 3 and 5 years, a model was fitted and the survival curve was extended beyond 5 years (up to 30 years) for each of the selected sites. This helped in generation of survival proportions by single year and thereby survival of cancer cases. With the help of estimated survived cases available year wise and the incidence, the prevalence figures were arrived for selected cancer sites and for selected periods. In our previous paper, we have dealt with the cancer sites of breast, cervix, ovary, lung, stomach and mouth (Takiar and Jayant, 2013). The prevalence to incidence ratio (PI ratio) was calculated for 30 years duration for all the selected cancer sites using the model approach showing that from the knowledge of incidence and P/I ratio, the prevalence can be calculated. The validity of the approach was shown in our previous paper (Takiar and Jayant, 2013). The P/I ratios for the cancer sites of lip, tongue, oral cavity, hypopharynx, oesophagus, larynx, nhl, colon, prostate, lymphoid leukemia, myeloid leukemia were observed to be 10.26, 4.15, 5.89, 2.81, 1.87, 5.43, 5.48, 5.24, 4.61, 3.42 and 2.65, respectively. Cancer prevalence can be readily estimated with use of survival and incidence data.

A Model Approach to Calculate Cancer Prevalence From 5 Year Survival Data for Selected Cancer Sites in India

Prevalence is a statistic of primary interest in public health. In the absence of good follow- up facilities, it is difficult to assess the complete prevalence of cancer for a given registry area. An attempt was here made to arrive at complete prevalence including limited duration prevalence with respect to selected sites of cancer for India by fitting appropriate models to 1, 3 and 5 years cancer survival data available for selected population-based registries. Survival data, available for the registries of Bhopal, Chennai, Karunagappally, and Mumbai was pooled to generate survival for breast, cervix, ovary, lung, stomach and mouth cancers. With the available data on survival for 1, 3 and 5 years, a model was fitted and the survival curve was extended beyond 5 years (up to 35 years) for each of the selected sites. This helped in generation of survival proportions by single year and thereby survival of cancer cases. With the help of survival proportions available year-wise and the incidence, prevalence figures were arrived for selected cancer sites and for selected periods. The prevalence to incidence ratio (PI ratio) stabilized after a certain duration for all the cancer sites showing that from the knowledge of incidence, the prevalence can be calculated. The stabilized P/I ratios for the cancer sites of breast, cervix, ovary, stomach, lung, mouth and for life time was observed to be 4.90, 5.33, 2.75, 1.40, 1.37, 4.04 and 3.42 respectively. The validity of the model approach to calculate prevalence could be demonstrated with the help of survival data of Barshi registry for cervix cancer, available for the period 1988-2006.

Design of the prostate, lung, colorectal and ovarian (PLCO) cancer screening trial

The objectives of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial are to determine in screenees ages 55–74 at entry whether screening with flexible sigmoidoscopy (60-cm sigmoidoscope) can reduce mortality from colorectal cancer, whether screening with chest X-ray can reduce mortality from lung cancer, whether screenng men with digital rectal examination (DRE) plus serum prostate-specific antigen (PSA) can reduce mortality from prostate cancer, and whether screening women with CA125 and transvaginal ultrasound (TVU) can reduce mortality from ovarian cancer. Secondary objectives are to assess screening variables other than mortality for each of the interventions including sensitivity, specificity, and positive predictive value; to assess incidence, stage, and survival of cancer cases; and to investigate biologic and/or prognostic characterizations of tumor tissue and biochemical producs as intermediate endpoints. The design is a multicenter, two-armed, randomized trial with 37,000 females and 37,000 males in each of the two arms. In the intervention arm, the PSA and CA125 tests are performed at entry, then annually for 5 years. The DRE, TVU, and chest X-ray exams are performed at entry and then annually for 3 years. Sigmoidoscopy is performed at entry and then at the 5-year point. Participants in the control arm follow their usual medical care practices. Participants will be followed for at least 13 years from randomization to ascertain all cancers of the prostate, lung, colorectum, and ovary, as well as deaths from all causes. A pilot phase was undertaken to assess the randomization, screening, and data collection procedures of the trial and to estimate design parameters such as compliance and contamination levels. This paper describes eligibility, consent, and other design features of the trial, randomization and screening procedures, and an outline of the follow-up procedures. Sample-size calculations are reported, and a data analysis plan is presented.

Age-specific prostate-specific antigen: a reassessment.

Our objective was to compare expected survival benefits when screening for prostate cancer with PSA, using an age-specific bound relative to a cutoff of 4.0 ng/ml. We used a decision analysis modeling the cancer yield in a cohort screened by both screening tests, and the survival of cancer cases given screen detection and in the absence of screening. Expected cancer yields and positive predictive values were from an ultrasound-guided biopsy series. Stage distributions of screen-detected cases were obtained from the literature. For localized causes, survival given screen detection was assumed to be equal to normal life expectancy for the population. For these cases, survival in the absence of screening was modeled as time from clinical diagnosis to death added to time remaining after time of screen and before clinical diagnosis was made (lead time). For nonlocalized cases at screen detection, survival given screen detection was assumed to be equal to survival in the absence of screening. The average difference between expected survival with and without screening as calculated for age-specific PSA and for PSA > 4.0 ng/ml and compared. Average years of life saved per subject screened using PSA > 4.0 ng/ml were comparable to those using the age-specific bound. Average years of life saved per cancer case, however, appeared to be potentially greater for PSA > 4.0 ng/ml than for age-specific. PSA. PSA > 4.0 ng/ml detected markedly more prostate cancer cases than age-specific PSA. Using a bound of 4.0 ng/ml for all ages appears to be more efficient in identifying men with cancer in a screening cohort, which translates into a greater expected survival benefit per cancer case.