Andrew Smith Describing childhood diet with cluster analysis Young Statisticians meeting. 12th April 2011.

Andrew Smith

Describing childhood diet with cluster analysisYoung Statisticians’ meeting. 12th April 2011

Describing diet with cluster analysis

• Pauline M. Emmett

• P. Kirstin Newby

• Kate Northstone

• World Cancer Research Fund

• MRC, Wellcome Trust, University of Bristol

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Outline

• Introductions• ALSPAC• Food frequency questionnaires• Dietary patterns• Cluster analysis

• k-means cluster analysis

• Results• 3 cluster solution• Associations with socio-demographic variables

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ALSPAC

• Avon Longitudinal Study of Parents and Children

• Birth cohort study

• 14,541 pregnant women and their children

• www.bris.ac.uk/alspac

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Food frequency questionnaires5

Dietary patterns

• Examine diet as a whole

• Analyse multivariate FFQ data

• Use correlations between foods

• PCA

• Cluster analysis

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Image: Paul / FreeDigitalPhotos.net

Cluster analysis

• Separate subjects into

non-overlapping

groups

• Based on ‘distances’

between individuals

• Unsupervised learning

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Image: Boaz Yiftach / FreeDigitalPhotos.net

k-means cluster analysis

• Most widely used for dietary patterns

• Number of clusters, k, is specified beforehand

• Minimises – Distance from each subject to his/her cluster

mean– Summed over all subjects in that cluster– Summed over all clusters

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k-means cluster analysis9

Problems with the standard algorithm

• Short-sighted

• Tends to find solutions that are at a local minimum– So run algorithm 100 times and choose solution

that is minimum out of all minima

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Standardising the input variables11

Reliability of the cluster solution

• Split sample in half

• Perform separate analyses on each half

• See how many children change clusters

• Repeat 5 times– 32 out of 8,279 children changed cluster (0.4%)

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Processed4177 children13

Image: Suat Eman, Rawich, Master Isolated Images / FreeDigitalPhotos.net

Plant-based2065 children14

Image: Suat Eman, Paul, Rob Wiltshire, Simon Howden, winnond / FreeDigitalPhotos.net

Traditional British2037 children15

Image: Suat Eman, Filomena Scalise, Maggie Smith / FreeDigitalPhotos.net

Associations with socio-demographic vars

Processed

Plant-based

Plant-based

Traditional British

Traditional British

Processed

Girls 3,115 1 1 1

Boys 2,941 0.82 (0.72, 0.93)

1.03(0.89, 1.20)

1.18 (1.04, 1.34)

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Associations with socio-demographic vars

Maternal age

Processed

Plant-based

Plant-based

Traditional British

Traditional British

Processed

< 21 130 1 1 1

21-25 994 0.59 (0.33, 1.07)

1.07 (0.56, 2.05)

1.57(1.02, 2.43)

26-30 2,644 0.52(0.29, 0.92)

1.20(0.64, 2.28)

1.60(1.04, 2.46)

31+ 2,288 0.37(0.21, 0.67)

1.50(0.79, 2.88)

1.77(1.13, 2.76)

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Associations with socio-demographic vars

Maternal education

Processed

Plant-based

Plant-based

Traditional British

Traditional British

Processed

CSE 740 1 1 1

Vocational 504 0.84(0.60, 1.17)

1.19(0.82, 1.72)

1.01(0.76, 1.32)

O level 2,163 0.65(0.51, 0.83)

1.46(1.10, 1.94)

1.05(0.86, 1.30)

A level 1,604 0.42(0.33, 0.55)

2.01(1.50, 2.69)

1.18(0.95, 1.48)

Degree 1,045 0.30(0.23, 0.39)

2.75(2.00, 3.76)

1.22(0.94, 1.57)

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Associations with socio-demographic vars

Siblings

Processed

Plant-based

Plant-based

Traditional British

Traditional British

Processed

0 older 2,755 1 1 1

1 older 2,317 1.21(1.03, 1.42)

1.12 (0.94, 1.36)

0.73(0.62, 0.86)

2+ older 984 1.58(1.28, 1.97)

0.99(0.76, 1.27)

0.64(0.52, 0.80)

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Associations with socio-demographic vars

Siblings

Processed

Plant-based

Plant-based

Traditional British

Traditional British

Processed

0 younger 2,946 1 1 1

1 younger 2,490 1.01(0.86, 1.19)

0.58(0.48, 0.71)

1.69(1.44, 1.99)

2+ younger 620 1.21(0.92, 1.57)

0.43(0.33, 0.58)

1.90(2.50, 2.40)

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Summary

• Multivariate methods to compress FFQ data into

dietary patterns

• k-means cluster analysis is widespread but must

be applied carefully

• Processed, Plant-based and Traditional British

clusters in 7-year-old children

• Associated with various socio-demographic

variables

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