Estimating the variance of cancer prevalence from population-based registries.

Cancer prevalence is the proportion of people in a population diagnosed with cancer in the past and still alive. One way to estimate prevalence is via population-based registries, where data on diagnosis and life status of all incidence cases occurring in the covered population are collected. In this paper, a method to estimate the complete prevalence and its variance from population-based registries is presented. In order to obtain unbiased estimates of the complete prevalence, its calculation can be thought as made by three steps. Step 1 counts the incidence cases diagnosed during the period of registration and still alive. Step 2 estimates the expected number of survivors among cases lost to follow-up. Step 3 estimates the complete prevalence by taking into account cases diagnosed before the start of registration. The combination of steps 1+2 is defined as the counting method, to estimate the limited duration prevalence; step 3 is the completeness index method, to estimate the complete prevalence. For early established registries, steps 1+2 are more important than step 3, because observation time is long enough to include all past diagnosed cases still alive in the prevalence data. For more recently established registries, step 3 is by far the most critical because a large part of prevalence might have been diagnosed before the period of registration (Corazziari I, Mariotto A, Capocaccia R. Correcting the completeness bias of observed prevalence. Tumori 1999; 85: 370-81). The work by Clegg LX, Gail MH, Feuer EJ. Estimating the variance of disease-prevalence estimates from population-based registries. Biometrics 2002; 55: 1137-44. considers the problem of the variability of the estimated prevalence up to step 2. To our knowledge, no other work has considered the variability induced by correcting for the unobserved cases diagnosed before the period of registration, crucial to estimate the prevalence in recent registries. An analytic approach is considered to calculate the variance of step 3. A unified expression for the variance of the prevalence allowing for steps 1 through 3 is obtained. Some applications to cancer data are presented.

Standard cancer patient population for age standardising survival ratios.

Standard adult cancer patients populations are derived in this paper as a tool for the calculation of age-standardised cancer survival figures. Previously used standards in survival analysis have been site- and/or study-specific. Here, multivariate methods have been used to define the smallest possible number of general standard cancer patient populations which are simple to use and provide standardised survival values close to the raw ones for the largest possible number of cancer sites. The analysis was based on data for over 1.1 million cancer patients included in the EUROCARE-2 study. The proposed standard populations consist of three age distributions, appropriate for cancers with incidence patterns: (1) increasing with age - the vast majority of cancers; (2) broadly constant with age and (3) mainly affecting young adults. The three standard distributions are presented by both broad and five-year age classes. The latter can be used to determine which of the three standards would be used for sites not included in the cluster analysis because their survival is generally calculated in unusual age groups. Overall, standard 1 is appropriate for over 91% of cases, standard 2 for just over 7%, and standard 3 for less than 2%. The proposed standards were tested on European (EUROCARE-2 and EUROCARE-3) and US (Surveillance, Epidemiology and End Results Program, SEER) relative survival data. There was very good correspondence between the raw (population weighted) and age-standardised survival figures.

Explaining gastric cancer survival differences among European countries.

Wide geographic variability in incidence and mortality rates for gastric cancer exists throughout the world despite persistent decreases over several decades. Variability in survival from gastric cancer is also evident and countries with higher incidence rates of gastric cancer show better survival rates than countries with lower incidence. The aim of this study was to identify reasons for the association between incidence and survival and to obtain survival estimates and differences corrected for this variation, thus facilitating further interpretation by clinical factors such as stage and treatment. Relative survival rates for gastric cancer derived from the EUROCARE-2 database for 47 cancer registries in 17 European countries were analyzed with regression methods to adjust differences by age, sex, period of diagnosis, subsite of the stomach, histologic type and stage at diagnosis. Overall, nearly 60% of the variability in gastric cancer relative survival was explained by differences in these variables. Factors are related to treatment and general management of patients is expected to explain the residual variability in gastric cancer survival between European countries. There is a need to improve completeness and standardization of detailed information collected on gastric cancer patients to allow detailed comparative analyses and interpretation.