ArticleAmerican journal of epidemiology2017
Estimating the Comparative Effectiveness of Feeding Interventions in the Pediatric Intensive Care Unit: A Demonstration of Longitudinal Targeted Maximum Likelihood Estimation.
Article in American journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Estimate Time-Varying Exposure Effects via Ensemble Learning-Based Marginal Structural Model With Application to Adolescent Cognitive Development Study.Biometrical journal. Biometrische Zeitschrift · 2026Article
- 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
- Improvement science as the engine for the next generation of learning health networks.Frontiers in health services · 2026Article
- Statistical Methods to Adjust for Treatment Switching in Real-World Clinical Studies: A Scoping Review and Descriptive Comparison.Clinical pharmacology and therapeutics · 2025Article
- Empirical Sandwich Variance Estimator for Iterated Conditional Expectation g-Computation.Statistics in medicine · 2024Article
- Causal Inference for Continuous Multiple Time Point Interventions.Statistics in medicine · 2024Article
- Comparisons of mental health treatment frequency and risk of suicidal thoughts and behaviors among youth.Psychiatry research · 2024Article
- Machine learning in causal inference for epidemiology.European journal of epidemiology · 2024Article
- Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.Biostatistics (Oxford, England) · 2024Article
- Invited Perspective: Opportunities and Obstacles of Longitudinal Data in Pregnancy to Quantify Mechanisms and Understand Etiology.Environmental health perspectives · 2023Article
- MULTIPLY ROBUST ESTIMATORS OF CAUSAL EFFECTS FOR SURVIVAL OUTCOMES.Scandinavian journal of statistics, theory and applications · 2022Article
- G-Computation and Agent-Based Modeling for Social Epidemiology: Can Population Interventions Prevent Posttraumatic Stress Disorder?American journal of epidemiology · 2022Article
- Parametric g-formula implementations for causal survival analyses.Biometrics · 2021Article
- Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey.Journal of medical Internet research · 2021Article
- Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference.International journal of epidemiology · 2021Article
- Intersections of machine learning and epidemiological methods for health services research.International journal of epidemiology · 2021Article
- Long-term effects of asthma medication on asthma symptoms: an application of the targeted maximum likelihood estimation.BMC medical research methodology · 2020Article
- The Critical Importance of Asking Good Questions: The Role of Epidemiology Doctoral Training Programs.American journal of epidemiology · 2020Article
- Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions.Statistics in medicine · 2019Article
- "Complete Streets" and Adult Bicyclist Fatalities: Applying G-Computation to Evaluate an Intervention That Affects the Size of a Population at Risk.American journal of epidemiology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Longitudinal data sources offer new opportunities for the evaluation of sequential interventions. To adjust for time-dependent confounding in these settings, longitudinal targeted maximum likelihood based estimation (TMLE), a doubly robust method that can be coupled with machine learning, has been proposed. This paper provides a tutorial in applying longitudinal TMLE, in contrast to inverse probability of treatment weighting and g-computation based on iterative conditional expectations. We apply these methods to estimate the causal effect of nutritional interventions on clinical outcomes among critically ill children in a United Kingdom study (Control of Hyperglycemia in Paediatric Intensive Care, 2008-2011). We estimate the probability of a child's being discharged alive from the pediatric intensive care unit by a given day, under a range of static and dynamic feeding regimes. We find that before adjustment, patients who follow the static regime "never feed" are discharged by the end of the fifth day with a probability of 0.88 (95% confidence interval: 0.87, 0.90), while for the patients who follow the regime "feed from day 3," the probability of discharge is 0.64 (95% confidence interval: 0.62, 0.66). After adjustment for time-dependent confounding, most of this difference disappears, and the statistical methods produce similar results. TMLE offers a flexible estimation approach; hence, we provide practical guidance on implementation to encourage its wider use.
Indexed as
Identifiers
What OpenQuestion holds
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.