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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. [ABSTRACT FROM AUTHOR]

Chain event graphs (CEGs) are a graphical representation of a statistical model derived from event trees. They have previously been applied to cohort studies but not to case-control studies. In this paper, we apply the CEG framework to a Yorkshire, United Kingdom, case-control study of childhood type 1 diabetes (1993-1994) in order to examine 4 exposure variables associated with the mother, 3 of which are fully observed (her school-leaving-age, amniocenteses during pregnancy, and delivery type) and 1 with missing values (her rhesus factor), while incorporating previous type 1 diabetes knowledge. We conclude that the unknown rhesus factor values were likely to be missing not at random and were mainly rhesus-positive. The mother's school-leaving-age and rhesus factor were not associated with the diabetes status of the child, whereas having at least 1 amniocentesis procedure and, to a lesser extent, birth by cesarean delivery were associated; the combination of both procedures further increased the probability of diabetes. This application of CEGs to case-control data allows for the inclusion of missing data and prior knowledge, while investigating associations in the data. Communication of the analysis with the clinical expert is more straightforward than with traditional modeling, and this approach can be applied retrospectively or when assumptions for traditional analyses are not held. [ABSTRACT FROM AUTHOR]

There has been a worldwide epidemic of obesity in recent decades. In animal studies, there is convincing evidence that light exposure causes weight gain, even when calorie intake and physical activity are held constant. Disruption of sleep and circadian rhythms by exposure to light at night (LAN) might be one mechanism contributing to the rise in obesity, but it has not been well-investigated in humans. Using multinomial logistic regression, we examined the association between exposure to LAN and obesity in questionnaire data from over 100,000 women in the Breakthrough Generations Study, a cohort study of women aged 16 years or older who were living in the United Kingdom and recruited during 2003–2012. The odds of obesity, measured using body mass index, waist:hip ratio, waist:height ratio, and waist circumference, increased with increasing levels of LAN exposure (P < 0.001), even after adjustment for potential confounders such as sleep duration, alcohol intake, physical activity, and current smoking. We found a significant association between LAN exposure and obesity which was not explained by potential confounders we could measure. While the possibility of residual confounding cannot be excluded, the pattern is intriguing, accords with the results of animal experiments, and warrants further investigation. [ABSTRACT FROM PUBLISHER]