An efficient sampling strategy for selection of biobank samples using risk scores

Abstract

Aim:

The aim of this study was to suggest a new sample-selection strategy based on risk scores in case-control studies with biobank data.

Methods:

An ongoing Swedish case-control study on fetal exposure to endocrine disruptors and overweight in early childhood was used as the empirical example. Cases were defined as children with a body mass index (BMI) ⩾18 kg/m² (n=545) at four years of age, and controls as children with a BMI of ⩽17 kg/m² (n=4472 available). The risk of being overweight was modelled using logistic regression based on available covariates from the health examination and prior to selecting samples from the biobank. A risk score was estimated for each child and categorised as low (0–5%), medium (6–13%) or high (⩾14%) risk of being overweight.

Results:

The final risk-score model, with smoking during pregnancy (p=0.001), birth weight (p<0.001), BMI of both parents (p<0.001 for both), type of residence (p=0.04) and economic situation (p=0.12), yielded an area under the receiver operating characteristic curve of 67% (n=3945 with complete data). The case group (n=416) had the following risk-score profile: low (12%), medium (46%) and high risk (43%). Twice as many controls were selected from each risk group, with further matching on sex. Computer simulations showed that the proposed selection strategy with stratification on risk scores yielded consistent improvements in statistical precision.

Conclusions:

Using risk scores based on available survey or register data as a basis for sample selection may improve possibilities to study heterogeneity of exposure effects in biobank-based studies.

Keywords

Endocrine disruptors childhood obesity overweight research design case-control study propensity score

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References

Ode

Källen

Gustafsson

. Fetal exposure to perfluorinated compounds and attention deficit hyperactivity disorder in childhood. PloS One 2014;9:e95891.

Axelsson

Rylander

Rignell-Hydbom

. Phthalate exposure and reproductive parameters in young men from the general Swedish population. Environ Int 2015;85:54–60.

Janesick

Blumberg

Obesogens: an emerging threat to public health. Am J Obstet Gynecol 2016;214:559–565.

Vandenbroucke

Pearce

Case-control studies: basic concepts. Int J Epidemiol 2012;41:1480–1489.

Pearce

What does the odds ratio estimate in a case-control study?

Int J Epidemiol 1993;22:1189–1192.

Mangrio

Lindström

Rosvall

. Early life factors and being overweight at 4 years of age among children in Malmo, Sweden. BMC Public Health 2010;10:764.

Morandi

Meyre

Lobbens

. Estimation of newborn risk for child or adolescent obesity: lessons from longitudinal birth cohorts. PloS One 2012;7:e49919.

Strauss

. On Miettinen’s multivariate confounder score. J Clin Epidemiol 1998;51:233–236.

Weimann

Rylander

Albin

. Effects of changing exposure to neighbourhood greenness on general and mental health: a longitudinal study. Health Place 2015;33:48–56.

10.

Miettinen

. Stratification by a multivariate confounder score. Am J Epidemiol 1976;104:609–620.

11.

Rosenbaum

Rubin

. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984;79:516–524.

12.

Hansen

. The prognostic analogue of the propensity score. Biometrika 2008;95:481–488.

13.

Pike

Anderson

Day

. Some insights into Miettinen’s multivariate confounder score approach to case-control study analysis. J Epidemiol Commun Health 1979;33:104–106.