Estimating the Comparative Effectiveness of Feeding Interventions in the Pediatric Intensive Care Unit: A Demonstration of Longitudinal Targeted Maximum Likelihood Estimation. Issue 12 (24th June 2017)

• Record Type: Journal Article
• Authors: Kreif, Noémi; Tran, Linh; Grieve, Richard; De Stavola, Bianca; Tasker, Robert C; Petersen, Maya
• Is Part Of: American journal of epidemiology. Volume 186:Issue 12(2017)
• Page Start: 1370
• Page End: 1379
• Publication Date: 2017-06-24
• Subjects: causal inference -- epidemiologic methods -- longitudinal targeted maximum likelihood estimation -- machine learning -- Super Learner -- time-dependent confounding; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwx213
• Languages: English
• ISSNs: 0002-9262

Abstract

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.