European Journal of Epidemiology

, Volume 33, Issue 4, pp 381–392 | Cite as

Utility of inverse probability weighting in molecular pathological epidemiology

  • Li Liu
  • Daniel Nevo
  • Reiko Nishihara
  • Yin Cao
  • Mingyang Song
  • Tyler S. Twombly
  • Andrew T. Chan
  • Edward L. Giovannucci
  • Tyler J. VanderWeele
  • Molin Wang
  • Shuji Ogino
METHODS

Abstract

As one of causal inference methodologies, the inverse probability weighting (IPW) method has been utilized to address confounding and account for missing data when subjects with missing data cannot be included in a primary analysis. The transdisciplinary field of molecular pathological epidemiology (MPE) integrates molecular pathological and epidemiological methods, and takes advantages of improved understanding of pathogenesis to generate stronger biological evidence of causality and optimize strategies for precision medicine and prevention. Disease subtyping based on biomarker analysis of biospecimens is essential in MPE research. However, there are nearly always cases that lack subtype information due to the unavailability or insufficiency of biospecimens. To address this missing subtype data issue, we incorporated inverse probability weights into Cox proportional cause-specific hazards regression. The weight was inverse of the probability of biomarker data availability estimated based on a model for biomarker data availability status. The strategy was illustrated in two example studies; each assessed alcohol intake or family history of colorectal cancer in relation to the risk of developing colorectal carcinoma subtypes classified by tumor microsatellite instability (MSI) status, using a prospective cohort study, the Nurses’ Health Study. Logistic regression was used to estimate the probability of MSI data availability for each cancer case with covariates of clinical features and family history of colorectal cancer. This application of IPW can reduce selection bias caused by nonrandom variation in biospecimen data availability. The integration of causal inference methods into the MPE approach will likely have substantial potentials to advance the field of epidemiology.

Keywords

Etiologic heterogeneity Marginal structural model Missing at random Neoplasm Unique disease principle Selection bias 

Abbreviations

AUC

Area under receiver-operating characteristic curve

CCA

Complete case analysis

CI

Confidence interval

DAG

Directed acyclic graph

HR

Hazard ratio

IPW

Inverse probability weighting

MAR

Missing at random

METS

Mean metabolic equivalent task score

MCAR

Missing completely at random

MPE

Molecular pathological epidemiology

MSI

Microsatellite instability

NHS

Nurses’ Health Study

ROC

Receiver-operating characteristic curve

Notes

Acknowledgements

We would like to thank the participants and staff of the Nurses’ Health Study for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

Funding

This work was supported by U.S. National Institutes of Health (NIH) grants [P01 CA87969 to M.J. Stampfer; UM1 CA186107 to M.J. Stampfer; R01 CA137178 to A.T.C.; K24 DK098311 to A.T.C.; R01 CA151993 to S.O.; R35 CA197735 to S.O.; K07 CA190673 to R.N.]; and Nodal Award (to S.O.) from the Dana-Farber Harvard Cancer Center. L.L. is supported by the grant from National Natural Science Foundation of China No. 81302491, a scholarship grant from Chinese Scholarship Council and a fellowship grant from Huazhong University of Science and Technology. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

10654_2017_346_MOESM1_ESM.docx (38 kb)
Supplementary material 1 (DOCX 38 kb)

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  • Li Liu
    • 1
    • 2
    • 3
    • 4
  • Daniel Nevo
    • 5
    • 6
  • Reiko Nishihara
    • 2
    • 3
    • 6
    • 7
  • Yin Cao
    • 2
    • 8
    • 9
  • Mingyang Song
    • 2
    • 8
    • 9
  • Tyler S. Twombly
    • 1
  • Andrew T. Chan
    • 7
    • 8
    • 9
    • 10
  • Edward L. Giovannucci
    • 2
    • 6
    • 10
  • Tyler J. VanderWeele
    • 5
    • 6
  • Molin Wang
    • 5
    • 6
    • 10
  • Shuji Ogino
    • 1
    • 3
    • 6
    • 7
  1. 1.Department of Oncologic PathologyDana-Farber Cancer Institute and Harvard Medical SchoolBostonUSA
  2. 2.Department of NutritionHarvard T.H. Chan School of Public HealthBostonUSA
  3. 3.Program in MPE Molecular Pathological Epidemiology, Department of PathologyBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA
  4. 4.Department of Epidemiology and Biostatistics, and the Ministry of Education Key Lab of Environment and Health, School of Public HealthHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  5. 5.Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonUSA
  6. 6.Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonUSA
  7. 7.Broad Institute of MIT and HarvardCambridgeUSA
  8. 8.Division of GastroenterologyMassachusetts General HospitalBostonUSA
  9. 9.Clinical and Translational Epidemiology UnitMassachusetts General Hospital and Harvard Medical SchoolBostonUSA
  10. 10.Channing Division of Network Medicine, Department of MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA

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