Developing a dynamic sampling algorithm for cohort studies

M.H.P. HofA.C.J. RavelliM.B. SnijderK. StronksA.H. Zwinderman

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

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

Problem Definition

High generalizability Representativeness Sample size

Recruitment period of great importance Sampling Design

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

Current Sampling Designs

Problems: Expensive Non-response

differences in subgroups undetected

Limited number of strata possible

Results are very depended on pre-assumptions

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)

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 = Genderx3 = Household situationx4 = Income

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 = Genderx3 = Household situationx4 = Income

Stepwise Sampling Algorithm

Hypothesis:

Random Sampling Stepwise Sampling

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

Stepwise Sampling Algorithm Characteristics

Results Simulation

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

Conclusion

The Stepwise Sampling Algorithm outperforms Random Sampling on representativeness

Questions