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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
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A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Adjusting Expected Mortality Rates Using Information From a Control Population: An Example Using Socioeconomic

Hannah Bower1,1, Therese M-L Andersson1, Michael J Crowther2,2

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

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Summary

Adjusting expected mortality rates for cancer survival studies can be done using control population data. Two methods, Poisson generalized linear models and flexible parametric survival models, provide similar results when stratifying by socioeconomic status.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Expected mortality rates are crucial for population-based cancer survival studies (e.g., relative survival, standardized mortality ratios).
  • Standard stratification by age, sex, and calendar year may not suffice when investigating other factors like socioeconomic status, potentially introducing bias.
  • When population-level data for stratification are unavailable, control populations offer a viable alternative for adjusting expected rates.

Purpose of the Study:

  • To present and compare two novel approaches for adjusting expected mortality rates using control population data.
  • To illustrate these methods using socioeconomic status as a risk factor of interest in a Swedish breast cancer cohort.
  • To assess the similarity of adjusted mortality rates derived from Poisson and flexible parametric survival models.

Main Methods:

  • Employed a Poisson generalized linear model to adjust expected mortality rates.
  • Utilized a flexible parametric survival model for adjusting expected mortality rates.
  • Applied these methods to a Swedish breast cancer cohort (BCBaSe) diagnosed between 1992 and 2012, using a control group and stratifying by socioeconomic status.

Main Results:

  • Both the Poisson generalized linear model and the flexible parametric survival model yielded similar adjusted mortality rates when stratifying by socioeconomic status.
  • The study demonstrated the practical application of these adjustment methods in a real-world cohort.
  • Parametric bootstrap can account for additional uncertainty in stratified expected mortality rate estimation, though its impact diminishes with larger control populations.

Conclusions:

  • Adjusting expected mortality rates using control population data is feasible and effective for studies requiring stratification beyond standard demographic factors.
  • Poisson and flexible parametric survival models offer comparable results for estimating socioeconomic status-stratified mortality rates.
  • These methods enhance the accuracy and reduce potential bias in cancer survival analyses when investigating specific risk factors.