ArticleStatistical methods in medical research2019
Scalable collaborative targeted learning for high-dimensional data.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Analyses of child cardiometabolic phenotype following assisted reproductive technologies using a pragmatic trial emulation approach.Nature communications · 2021Trial
- REFINE2: a simplified simulation tool to help epidemiologists evaluate the suitability and sensitivity of effect estimation within user-specified data.American journal of epidemiology · 2026Article
- Semiparametric efficient estimation of small genetic effects in large-scale population cohorts.Biostatistics (Oxford, England) · 2025Article
- Evaluation of Methods Adjusting for Unmeasured Confounding Using Large Healthcare Databases: An Empirical Study Concerning Drugs Inducing Prematurity.Clinical and translational science · 2025Article
- How Effective Are Machine Learning and Doubly Robust Estimators in Incorporating High-Dimensional Proxies to Reduce Residual Confounding?Pharmacoepidemiology and drug safety · 2025Article
- High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting.Pharmacoepidemiology and drug safety · 2023Article
- Stan and BART for Causal Inference: Estimating Heterogeneous Treatment Effects Using the Power of Stan and the Flexibility of Machine Learning.Entropy (Basel, Switzerland) · 2022Article
- Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.Pharmacoepidemiology and drug safety · 2022Review
- Synthetic Negative Controls: Using Simulation to Screen Large-scale Propensity Score Analyses.Epidemiology (Cambridge, Mass.) · 2022Article
- Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.Statistics in medicine · 2022Article
- A comparison of confounder selection and adjustment methods for estimating causal effects using large healthcare databases.Pharmacoepidemiology and drug safety · 2022Article
- When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?Clinical pharmacology and therapeutics · 2022Review
- Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data.Statistical methods in medical research · 2019Article
- Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods.Journal of applied statistics · 2019Article
- Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects.Clinical epidemiology · 2018Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well-behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the general template of the collaborative targeted minimum loss-based estimation procedure. The original instantiation of the collaborative targeted minimum loss-based estimation template can be presented as a greedy forward stepwise collaborative targeted minimum loss-based estimation algorithm. It does not scale well when the number p of covariates increases drastically. This motivates the introduction of a novel instantiation of the collaborative targeted minimum loss-based estimation template where the covariates are pre-ordered. Its time complexity is
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.