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Developing a dynamic sampling algorithm for cohort studies

This study presents a novel stepwise sampling algorithm aimed at improving representativeness in cohort studies, particularly within diverse ethnic populations in urban settings like Amsterdam. In light of varying welfare and health outcomes across non-Dutch inhabitants, traditional random sampling methods have shown limitations, such as non-response and inadequate stratification. Our algorithm focuses on actively engaging participants based on known demographic characteristics while adjusting for those that are unknown. Simulation results indicate that the stepwise approach significantly enhances the representation of sampled populations, increasing the validity and applicability of health research across diverse ethnic groups.

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Developing a dynamic sampling algorithm for cohort studies

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  1. Developing a dynamic sampling algorithm for cohort studies M.H.P. Hof A.C.J. Ravelli M.B. Snijder K. Stronks A.H. Zwinderman

  2. Setting • Increasing number of non-Dutch inhabitants • Welfare, health, and illness varies between different ethnic groups • Why? • Unclear whether current healthcare and treatment (mainly based on the Dutch Caucasian population) guidelines can be used Source: O+S Amsterdam blabla

  3. Setting • HELIUS(HEalthy Life in an Urban Setting) Study • Large multi-ethnic cohort study among • Moroccan, • Surinamese (-Creole and –Hindustani) • Turkish, • West-African • Dutch/Caucasian • Group size ± 10,000 individuals • Participants will undergo extensive interviews, medical investigations, and biomaterial will be collected. • Recruitment period: ± 1 year

  4. Problem Definition • High generalizability • Representativeness • Sample size • Recruitment period of great importance • Sampling Design

  5. Current Sampling Designs • (Restricted) randomized sampling • Double stage sampling • Stage 1: Sample a large group and obtain distributions of characteristics • Stage 2: Use stratified randomization with stage 1 results

  6. Current Sampling Designs • Problems: • Expensive • Non-response differences in subgroups undetected • Limited number of strata possible • Results are very depended on pre-assumptions

  7. Stepwise Sampling Algorithm • Development of stepwise sampling algorithm • Actively invite participants with certain characteristics • Minimize difference population and sample • HELIUS study focusses on representativeness on 4 categorized variables • Known for each individual • x1 = Age (4 categories) • x2 = Gender (2 categories) • Unknown for each individual • x3 = Household situation (7 categories) • x4 = Income (5 categories)

  8. Stepwise Sampling Algorithm • Problems of active selection • Joint distribution of population composition f(x1, x2, x3, x4)unavailable • Estimation of population composition • Prior knowledge: f(x1 * x2) f(x3) * f(x4) • Without Prior knowledge • Updated with sample composition f(x1, x2, x3, x4) • Individuals could only be selected on x1 and x2 • x1 = Age • x2 = Gender • x3 = Household situation • x4 = Income

  9. Stepwise Sampling Algorithm • Recruitment period has n iterations • Each iteration: • Individuals were invited with optimal characteristics f(x1 , x2) and estimated f(x3) and f(x4) • Minimizing differences between sample- and estimated population-composition • Weighted for response and participation chance • Population Estimation was updated with f(x1, x2, x3, x4)from the sample • x1 = Age • x2 = Gender • x3 = Household situation • x4 = Income

  10. Stepwise Sampling Algorithm • Hypothesis: Random Sampling Stepwise Sampling

  11. Simulation Setting • Stepwise Sampling Algorithm versus Random sampling (With prior knowledge) (Without prior knowledge) • Recruitment period consists of 50 iterations and a sample size of 10,000 per ethnic group is desired • Population • O+S Research and Statistics Amsterdam Data from 2009 • Five ethnic groups • Dutch (Largest) • Surinamese • Moroccan • Turkish • Antillean (Smallest) . • Response rates varying between all characteristics • Invited persons responded and participated one iteration later • Non-responders were sent a reminder once • Performance measured by • Representativeness and Compared to Sample Size

  12. Stepwise Sampling Algorithm Characteristics

  13. Results Simulation

  14. Discussion • Stepwise Sampling Algorithm • Strengths • Non-response adjustment • Better representativeness and sample size • Large number of characteristics representative • Less depended on prior knowledge • Weakness • High burden of registration during recruitment • No increase in representativeness of individually unknown characteristics

  15. Conclusion • The Stepwise Sampling Algorithm outperforms Random Sampling on representativeness

  16. Questions

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