Background Big Data Final Remarks

De Toekomst Van Biostatistiek inhet Medisch Onderzoek

Jeanine Houwing-Duistermaat

Department of Medical Statistics and Bioinformatics, LUMC

Big Data: Een optie voor de toekomst van preventieve medischezorg? 29 januari 2015 Baarn

Background Big Data Final Remarks

Statistics

Background Big Data Final Remarks

New York Times

In his State of the Union address last week,President Obama encouraged the development ofprecision medicine, which would tailor treatmentsbased on individuals genetics or physiology.

Need for statistics

Background Big Data Final Remarks

Data in (Bio)medical research

Demographic variablesClassical parameters‐omicsGenetic dataHuman MicrobiomeImagingQuestionnaires

Simone Houtman

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Biomedical Research

Insight in biological mechanisms underlyinghuman diseases and health

Individiual predictionsScreeningTreatment effects

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Statistical Challenges: Big data

Combining various data sources: heterogeneity

Hierarchical data

Longitudinally measured profiles

Combination of the above mentioned datasets→ big data

Causality

Model Validation

Background Big Data Final Remarks

Background Big Data Final Remarks

Types of Datasets

Aggregated dataRegistrationSummaries from studies/EPD

Reference datasetsGenome NL1000 Genomes

Patient cohorts

HIS: Data from General Practioners

Epidemiological studies

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Combining data - heterogeneity

Populations

Measurementtechniques

Definitions

PatientCharacteristics

Study Designs

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Background Big Data Final Remarks

Hierarchy of data

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Longitudinal Data

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Causality

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Causality

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”Big” Data in Biomedical Research

Genetic Studies (DNA)Genome Wide Association studies: 10M SNPs.Typically well measured.Whole genome sequencing. Data preprocessingimportant.

Metabolomics, Proteomics, GlycomicsSpectra (10-2000 peaks)Noisy data

Lots of data, but most of it not informative

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Sample sizes of Genome Wide Associationstudies

Outcome Number of subjectsHeight 180KLipids 100KCVD 80K

Longevity 8K

Big numbers are obtained by collaborations betweenresearch groups

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Statistical analysis of Genome WideAssociation Studies

Test for association between single geneticmarker and outcome per studySingle study results are combined by usingmeta analysis tools

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Lessons learned from Genome WideAssociation Studies

Quality control

Statistical model should acknowledgeStudy designDistribution of outcome variableAdjust for or model the structure in the data

From Simon Heath et al, EJHG

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Post analysis quality control

Most tests are representing the null hypothesisDistribution of observed test statistics shouldcorrespond to theoretical distribution

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Omics data

Measurement process not automated andstandardized

Degeneration of samples

Detection limit

Non normal data

Technique differences can be huge

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Relationship omics and age, sex

Lucija Klaric

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Multidisciplinary

Medical Science: Need for statistics withrealistic datasets

Data Science: Need for more biological andchemistry

Background Big Data Final Remarks

Final remarks

New methodologies are needed for big data

The amount of data is growing fast but not theinformative part

New biostatistical methods are neededacknowledge hierarchy in datalongitudinal dataselection of correct statistical modelscausality