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tmle: An R Package for Targeted Maximum Likelihood Estimation

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  • Gruber, Susan
  • Laan, Mark van der

Abstract

Targeted maximum likelihood estimation (TMLE) is a general approach for constructing an efficient double-robust semi-parametric substitution estimator of a causal effect parameter or statistical association measure. tmle is a recently developed R package that implements TMLE of the effect of a binary treatment at a single point in time on an outcome of interest, controlling for user supplied covariates, including an additive treatment effect, relative risk, odds ratio, and the controlled direct effect of a binary treatment controlling for a binary intermediate variable on the pathway from treatment to the out- come. Estimation of the parameters of a marginal structural model is also available. The package allows outcome data with missingness, and experimental units that contribute repeated records of the point-treatment data structure, thereby allowing the analysis of longitudinal data structures. Relevant factors of the likelihood may be modeled or fit data-adaptively according to user specifications, or passed in from an external estimation procedure. Effect estimates, variances, p values, and 95% confidence intervals are provided by the software.

Suggested Citation

  • Gruber, Susan & Laan, Mark van der, 2012. "tmle: An R Package for Targeted Maximum Likelihood Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i13).
    • Handle: RePEc:jss:jstsof:v:051:i13
    DOI: http://hdl.handle.net/10.18637/jss.v051.i13
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    References listed on IDEAS

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    1. Stitelman Ori M & van der Laan Mark J., 2010. "Collaborative Targeted Maximum Likelihood for Time to Event Data," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-46, June.
    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.
    3. Xiao Yongling & Abrahamowicz Michal & Moodie Erica E. M., 2010. "Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-30, March.
    4. van der Laan Mark J. & Gruber Susan, 2010. "Collaborative Double Robust Targeted Maximum Likelihood Estimation," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-71, May.
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    Cited by:

    1. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
    2. Sapp Stephanie & van der Laan Mark J. & Page Kimberly, 2014. "Targeted Estimation of Binary Variable Importance Measures with Interval-Censored Outcomes," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 77-97, May.
    3. Yunda Huang & Lily Zhang & Shelly Karuna & Philip Andrew & Michal Juraska & Joshua A. Weiner & Heather Angier & Evgenii Morgan & Yasmin Azzam & Edith Swann & Srilatha Edupuganti & Nyaradzo M. Mgodi & , 2023. "Adults on pre-exposure prophylaxis (tenofovir-emtricitabine) have faster clearance of anti-HIV monoclonal antibody VRC01," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    4. Brooks Jordan C. & van der Laan Mark J. & Singer Daniel E. & Go Alan S., 2013. "Targeted Minimum Loss-Based Estimation of Causal Effects in Right-Censored Survival Data with Time-Dependent Covariates: Warfarin, Stroke, and Death in Atrial Fibrillation," Journal of Causal Inference, De Gruyter, vol. 1(2), pages 235-254, November.
    5. Ronald Herrera & Ursula Berger & Ondine S. Von Ehrenstein & Iván Díaz & Stella Huber & Daniel Moraga Muñoz & Katja Radon, 2017. "Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation," IJERPH, MDPI, vol. 15(1), pages 1-15, December.
    6. van der Laan Mark, 2017. "A Generally Efficient Targeted Minimum Loss Based Estimator based on the Highly Adaptive Lasso," The International Journal of Biostatistics, De Gruyter, vol. 13(2), pages 1-35, November.
    7. Youmi Suk & Jee-Seon Kim & Hyunseung Kang, 2021. "Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 323-347, June.
    8. Amy J. Pickering & Sammy M. Njenga & Lauren Steinbaum & Jenna Swarthout & Audrie Lin & Benjamin F. Arnold & Christine P. Stewart & Holly N. Dentz & MaryAnne Mureithi & Benard Chieng & Marlene Wolfe & , "undated". "Effects of Single and Integrated Water, Sanitation, Handwashing, and Nutrition Interventions on Child Soil-Transmitted Helminth and Giardia Infections: A Cluster-Randomized Controlled Trial in Rural K," Mathematica Policy Research Reports b056c901c24c4dad92672a0eb, Mathematica Policy Research.
    9. Youmi Suk, 2024. "A Within-Group Approach to Ensemble Machine Learning Methods for Causal Inference in Multilevel Studies," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 61-91, February.
    10. Susan Gruber & Mark J. van der Laan, 2013. "An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data," Biometrics, The International Biometric Society, vol. 69(1), pages 254-262, March.
    11. Jeremiah Jones & Ashkan Ertefaie & Susan M. Shortreed, 2023. "Rejoinder to “Reader reaction to ‘Outcome‐adaptive Lasso: Variable selection for causal inference’ by Shortreed and Ertefaie (2017)”," Biometrics, The International Biometric Society, vol. 79(1), pages 521-525, March.
    12. Veronica Sciannameo & Gian Paolo Fadini & Daniele Bottigliengo & Angelo Avogaro & Ileana Baldi & Dario Gregori & Paola Berchialla, 2022. "Assessment of Glucose Lowering Medications’ Effectiveness for Cardiovascular Clinical Risk Management of Real-World Patients with Type 2 Diabetes: Targeted Maximum Likelihood Estimation under Model Mi," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
    13. Michael Schomaker & Christian Heumann, 2020. "When and when not to use optimal model averaging," Statistical Papers, Springer, vol. 61(5), pages 2221-2240, October.
    14. Ziyun Xu & Éric Archambault, 2015. "Chinese interpreting studies: structural determinants of MA students’ career choices," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1041-1058, November.
    15. Jenny Häggström, 2018. "Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 389-398, June.
    16. Youmi Suk & Hyunseung Kang, 2022. "Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 310-343, March.
    17. Jason Roy & Kirsten J. Lum & Bret Zeldow & Jordan D. Dworkin & Vincent Lo Re & Michael J. Daniels, 2018. "Bayesian nonparametric generative models for causal inference with missing at random covariates," Biometrics, The International Biometric Society, vol. 74(4), pages 1193-1202, December.
    18. Sherri Rose & Sharon‐Lise Normand, 2019. "Double robust estimation for multiple unordered treatments and clustered observations: Evaluating drug‐eluting coronary artery stents," Biometrics, The International Biometric Society, vol. 75(1), pages 289-296, March.
    19. Bryan Keller, 2020. "Variable Selection for Causal Effect Estimation: Nonparametric Conditional Independence Testing With Random Forests," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 119-142, April.
    20. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
    21. Xiang Zhou, 2022. "Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 794-821, July.

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