IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v2y2014i1p14n4.html
   My bibliography  Save this article

Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package

Author

Listed:
  • Decker Anna L.

    (University of California – Berkeley, Berkeley, CA 94704, USA)

  • Hubbard Alan

    (Division of Biostatistics, University of California – Berkeley, Berkeley, CA, USA)

  • Crespi Catherine M.

    (University of California – Los Angeles, Los Angeles, CA, USA)

  • Seto Edmund Y.W.

    (University of Washington – Seattle, Seattle, WA, 98195, USA)

  • Wang May C.

    (University of California – Los Angeles, Los Angeles, CA, USA)

Abstract

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multivariable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice.

Suggested Citation

  • Decker Anna L. & Hubbard Alan & Crespi Catherine M. & Seto Edmund Y.W. & Wang May C., 2014. "Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package," Journal of Causal Inference, De Gruyter, vol. 2(1), pages 95-108, March.
    • Handle: RePEc:bpj:causin:v:2:y:2014:i:1:p:14:n:4
    DOI: 10.1515/jci-2013-0025
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2013-0025
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2013-0025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Morrison, J.A., 1992. "Obesity and cardiovascular disease risk factors in Black and White girls: The NHLBI Growth and Health Study," American Journal of Public Health, American Public Health Association, vol. 82(12), pages 1613-1620.
    2. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Philipp Baumann & Enzo Rossi & Michael Schomaker, 2022. "Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    2. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    3. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    4. Görg Holger & Marchal Léa, 2019. "Die Effekte deutscher Direktinvestitionen im Empfängerland vor dem Hintergrund des Leistungsbilanzüberschusses: Empirische Evidenz mit Mikrodaten für Frankreich," Perspektiven der Wirtschaftspolitik, De Gruyter, vol. 20(1), pages 53-69, June.
    5. Tran Linh & Petersen Maya & Schwab Joshua & van der Laan Mark J., 2023. "Robust variance estimation and inference for causal effect estimation," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-27, January.
    6. Léa Marchal & Clément Nedoncelle, 2019. "Immigrants, occupations and firm export performance," Review of International Economics, Wiley Blackwell, vol. 27(5), pages 1480-1509, November.
    7. Hisaki Kono & Yasuyuki Sawada & Abu S. Shonchoy, 2016. "DVD-based Distance-learning Program for University Entrance Exams: Experimental Evidence from Rural Bangladesh," CIRJE F-Series CIRJE-F-1027, CIRJE, Faculty of Economics, University of Tokyo.
    8. Masahiro Kato & Masaaki Imaizumi & Takuya Ishihara & Toru Kitagawa, 2023. "Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds," Papers 2302.02988, arXiv.org, revised Jul 2023.
    9. Takahiro Hoshino & Yuya Shimizu, 2019. "Doubly Robust-type Estimation of Population Moments and Parameters in Biased Sampling," Keio-IES Discussion Paper Series 2019-006, Institute for Economics Studies, Keio University.
    10. Uysal, S. Derya, 2013. "Doubly Robust Estimation of Causal Effects with Multivalued Treatments," Economics Series 297, Institute for Advanced Studies.
    11. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    12. Ignaciuk, Ada & Malevolti, Giulia & Scognamillo, Antonio & Sitko, Nicholas J., 2022. "Can food aid relax farmers’ constraints to adopting climate-adaptive agricultural practices? Evidence from Ethiopia, Malawi and the United Republic of Tanzania," ESA Working Papers 324073, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    13. Everding, Jakob & Marcus, Jan, 2020. "The effect of unemployment on the smoking behavior of couples," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 29(2), pages 154-170.
    14. Li Liang & Greene Tom, 2013. "A Weighting Analogue to Pair Matching in Propensity Score Analysis," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 215-234, July.
    15. Frank Fossen & Martin Simmler, 2016. "Personal taxation of capital income and the financial leverage of firms," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 23(1), pages 48-81, February.
    16. repec:ags:unassr:234387 is not listed on IDEAS
    17. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    18. Brian J. Reich & Shu Yang & Yawen Guan & Andrew B. Giffin & Matthew J. Miller & Ana Rappold, 2021. "A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications," International Statistical Review, International Statistical Institute, vol. 89(3), pages 605-634, December.
    19. repec:osf:osfxxx:yve6u_v1 is not listed on IDEAS
    20. Masahiro Kato, 2024. "Debiased Nonparametric Regression for Statistical Inference and Distributionally Robustness," Papers 2412.20173, arXiv.org, revised Mar 2025.
    21. Satoshi Hattori & Masayuki Henmi, 2014. "Stratified doubly robust estimators for the average causal effect," Biometrics, The International Biometric Society, vol. 70(2), pages 270-277, June.
    22. Stephanie A Kovalchik, 2020. "‘In search of lost time’: Identifying the causative role of cumulative competition load and competition time-loss in professional tennis using a structural nested mean model," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-18, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:causin:v:2:y:2014:i:1:p:14:n:4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.