Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

<p>Abstract</p> <p>Background</p> <p>Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome.</p> <p>Methods</p> <p>Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data.</p> <p>Results</p> <p>Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET.</p> <p>Conclusions</p> <p>The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.</p

Relative survival: a useful tool to assess generalisability in longitudinal studies of health in older persons

Generalisability of longitudinal studies is threatened by issues such as choice of sampling frame, representativeness of the initial sample, and attrition. To determine representativeness, cohorts are often compared with the population of interest at baseline on demographic and health characteristics. This study illustrates the use of relative survival as a tool for assessing generalisability of results from a cohort of older people among whom death is a potential threat to generalisability.The authors used data from the 1921-26 cohort (n = 12,416, aged 70-75 in 1996) of the Australian Longitudinal Study on Women's Health (ALSWH). Vital status was determined by linkage to the National Death Index, and expected deaths were derived using Australian life tables. Relative survival was estimated using observed survival in the cohort divided by expected survival among women of the same age and State or Territory.Overall, the ALSWH women showed relative survival 9.5% above the general population. Within States and Territories, the relative survival advantage varied from 6% to 23%. The interval-specific relative survival remained relatively constant over the 12 years (1996-2008) under review, indicating that the survival advantage of the cohort has not diminished over time.This study demonstrates that relative survival can be a useful measure of generalisability in a longitudinal study of the health of the general population, particularly when participants are older

Characteristics of Australian cohort study participants who do and do not take up an additional invitation to join a long-term biobank: The 45 and Up Study

BACKGROUND: Large-scale population biobanks are critical for future research integrating epidemiology, genetic, biomarker and other factors. Little is known about the factors influencing participation in biobanks. This study compares the characteristics of biobank participants with those of non-participants, among members of an existing cohort study. METHODS: Individuals aged 45 and over participating in The 45 and Up Study and living ≤20km from central Wagga Wagga, New South Wales (NSW), Australia (rural/regional area) or ≤10km from central Parramatta, NSW (urban area) (n=2340) were invited to join a biobank, giving a blood sample and having additional measures taken, including height, weight, waist circumference, heart rate and blood pressure. RESULTS: The overall uptake of the invitation to participate was 33% (762/2340). The response rate was 41% (410/1002) among participants resident in the regional area, and 26% (352/1338) among those resident in the urban area. Characteristics associated with significantly decreased participation were being aged 80 and over versus being aged 45–64 (participation rate ratio: RR = 0.45, 95%CI 0.34-0.60), not being born in Australia versus being born in Australia (0.69, 0.59-0.81), having versus not having a major disability (0.54, 0.38-0.76), having full-time caregiving responsibilities versus not being a full-time carer (0.62, 0.42-0.93) and being a current smoker versus never having smoked (0.66, 0.50-0.89). Factors associated with increased participation were being in part-time work versus not being in paid work (1.24, 1.07-1.44) and having an annual household income of ≥$50,000 versus <$20,000 (1.50, 1.26-1.80). CONCLUSIONS: A range of socio-economic, health and lifestyle factors are associated with biobank participation among members of an existing cohort study, with factors relating to health-seeking behaviours and access difficulties or time limitations being particularly important. If more widespread participation in biobanking is desired, particularly to ensure sufficient numbers among those most affected by these issues, specific efforts may be required to increase participation in certain groups such as migrants, the elderly, and those in poor health. Whilst caution should be exercised when generalising estimates of absolute prevalence from biobanks, estimates for many internal comparisons are likely to remain valid

Relação do índice de massa corporal, da relação cintura-quadril e da circunferência abdominal com a mortalidade em mulheres idosas: seguimento de 5 anos Relationship between body mass index, waist circumference, and waist-to-hip ratio and mortality in elderly women: a 5-year follow-up study

Este estudo analisa a associação entre a relação cintura-quadril (RCQ), a circunferência abdominal (CA) e o índice de massa corporal (IMC) com a mortalidade total e cardiovascular em 575 mulheres idosas ambulatoriais por um seguimento de cinco anos. Os maiores quartis de RCQ, CA e IMC, bem como as categorias pré-determinadas de IMC, foram analisados como variáveis preditivas e analisada a interferência de algumas variáveis confundidoras. Oitenta e oito mulheres morreram durante o seguimento (15,4%). As mulheres com baixo peso (IMC < 18,5kg/m²) apresentavam uma associação positiva com a mortalidade total nas análises uni e multivariadas, independentemente da estratificação etária. Nas curvas de sobrevida e na análise univariada, o maior quartil de RCQ (> 0,97) estava associado com a maior mortalidade total, entretanto, na análise multivariada o aumento de RCQ apresentou uma associação independente com a mortalidade total, apenas entre as mulheres de 60 a 80 anos. Nenhuma medida antropométrica apresentou uma associação significativa com a mortalidade cardiovascular. Os resultados identificaram o baixo peso e a RCQ como preditores de mortalidade total em idosas, principalmente entre as mulheres com até 80 anos.<br>This study examines the association between body mass index (BMI), waist-to-hip ratio (WHR), and waist circumference (WC) and all-cause and cardiovascular mortality in elderly women in a 5-year longitudinal study of 575 female outpatients 60 years and over. The highest BMI, WHR, and WC quartiles and predefined BMI categories were analyzed as predictive variables. Death occurred in 88 (15.4%). Underweight (BMI < 18.5kg/m²) was associated with all-cause mortality in uni- and multivariate analyses, regardless of age bracket. The survival curves and univariate analysis showed that the highest WHR quartile (> 0.97) was associated with all-cause mortality. However, after adjustment for age, smoking, and previous cardiovascular diseases, the increase in WHR was positively associated only in women from 60 to 80 years of age. None of the anthropometric measurements was associated with cardiovascular mortality. The results indicate that underweight and increased waist-to-hip ratio were predictors of all-cause mortality in elderly women, mainly among those under 80 years