<b>tmle</b>: An<i>R</i>Package for Targeted Maximum Likelihood Estimation

Gruber, Susan; Laan, Mark J. van der

doi:10.18637/jss.v051.i13

Cited by 200 publications

(179 citation statements)

References 34 publications

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“…Furthermore, we provided r code for the application of weighted Mle, tMle, and the rare outcomes refinement to facilitate implementation by other researchers (Supplemental Digital content 1; http://links.lww.com/eDe/B34). 17,39,40 Flexibility to estimate a wider range of parameters allows the scientific question to drive the analysis and has potential to improve knowledge gained from case-control designs. 17,18 …”

Section: Discussionmentioning

confidence: 99%

Targeted Estimation of Marginal Absolute and Relative Associations in Case–Control Data

2016

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Section: Discussionmentioning

confidence: 99%

Targeted Estimation of Marginal Absolute and Relative Associations in Case–Control Data

2016

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“…TMLE was implemented using R package “Targeted Maximum Likelihood Estimation” version 1.2.0-4 [28]. In order to avoid unsubstantiated parametric assumptions on the data-generating process, we fit both g 0 and

\overset{Q_{0}}{true}

using SuperLearner (“SuperLearner Prediction” version 2.0-10, see Appendix B for a description), a machine-learning algorithm based on 10-fold cross-validation [29].…”

Section: Estimation Methodsmentioning

confidence: 99%

“…The confidence intervals (CI) were calculated assuming a normal distribution for the estimator, as well as by ordering the bootstrap estimates and taking the 2.5th and 97.5th percentile values. We also present robust influence curve based confidence intervals using the TMLE package [28]. Finally, we compared TMLE to two other methods of estimation: linear main term regression and inverse probability of treatment weighting [26, 27].…”

Section: Estimation Methodsmentioning

confidence: 99%

Assumption Trade-Offs When Choosing Identification Strategies for Pre-Post Treatment Effect Estimation: An Illustration of a Community-Based Intervention in Madagascar

Weber

Laan

Petersen

2015

Journal of Causal Inference

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Failure (or success) in finding a statistically significant effect of a large-scale intervention may be due to choices made in the evaluation. To highlight the potential limitations and pitfalls of some common identification strategies used for estimating causal effects of community-level interventions, we apply a roadmap for causal inference to a pre-post evaluation of a national nutrition program in Madagascar. Selection into the program was non-random and strongly associated with the pre-treatment (lagged) outcome. Using structural causal models (SCM), directed acyclic graphs (DAGs) and simulated data, we illustrate that an estimand with the outcome defined as the post-treatment outcome controls for confounding by the lagged outcome but not by possible unmeasured confounders. Two separate differencing estimands (of the pre- and post-treatment outcome) have the potential to adjust for a certain type of unmeasured confounding, but introduce bias if the additional identification assumptions they rely on are not met. In order to illustrate the practical impact of choice between three common identification strategies and their corresponding estimands, we used observational data from the community nutrition program in Madagascar to estimate each of these three estimands. Specifically, we estimated the average treatment effect of the program on the community mean nutritional status of children 5 years and under and found that the estimate based on the post-treatment estimand was about a quarter of the magnitude of either of the differencing estimands (0.066 SD vs. 0.26–0.27 SD increase in mean weight-for-age z-score). Choice of estimand clearly has important implications for the interpretation of the success of the program to improve nutritional status of young children. A careful appraisal of the assumptions underlying the causal model is imperative before committing to a statistical model and progressing to estimation. However, knowledge about the data-generating process must be sufficient in order to choose the identification strategy that gets us closest to the truth.

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“…All analyses were run on R 2.15.2 statistical software running on a Mac OsX platform (The R Foundation for Statistical Computing, Vienna, Austria), using the packages Super Learner, 18 cvAUC, 28 and TMLE. 29 …”

Section: Variable Importance Measurementioning

confidence: 99%

Collaborative targeted maximum likelihood estimation for variable importance measure: Illustration for functional outcome prediction in mild traumatic brain injuries

Pirracchio

Yue

Manley

et al. 2016

Stat Methods Med Res

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Standard statistical practice used for determining the relative importance of competing causes of disease typically relies on ad hoc methods, often byproducts of machine learning procedures (stepwise regression, random forest, etc.). Causal inference framework and data-adaptive methods may help to tailor parameters to match the clinical question and free one from arbitrary modeling assumptions. Our focus is on implementations of such semiparametric methods for a variable importance measure (VIM). We propose a fully automated procedure for VIM based on collaborative targeted maximum likelihood estimation (cTMLE), a method that optimizes the estimate of an association in the presence of potentially numerous competing causes. We applied the approach to data collected from traumatic brain injury patients, specifically a prospective, observational study including three US Level-1 trauma centers. The primary outcome was a disability score (Glasgow Outcome Scale – Extended (GOSE)) collected three months post-injury. We identified clinically important predictors among a set of risk factors using a variable importance analysis based on targeted maximum likelihood estimators (TMLE) and on cTMLE. Via a parametric bootstrap, we demonstrate that the latter procedure has the potential for robust automated estimation of variable importance measures based upon machine-learning algorithms. The cTMLE estimator was associated with substantially less positivity bias as compared to TMLE and larger coverage of the 95% CI. This study confirms the power of an automated cTMLE procedure that can target model selection via machine learning to estimate VIMs in complicated, high-dimensional data.

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tmle: AnRPackage for Targeted Maximum Likelihood Estimation

Cited by 200 publications

References 34 publications

Targeted Estimation of Marginal Absolute and Relative Associations in Case–Control Data

Targeted Estimation of Marginal Absolute and Relative Associations in Case–Control Data

Assumption Trade-Offs When Choosing Identification Strategies for Pre-Post Treatment Effect Estimation: An Illustration of a Community-Based Intervention in Madagascar

Collaborative targeted maximum likelihood estimation for variable importance measure: Illustration for functional outcome prediction in mild traumatic brain injuries

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