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A dynamic regime is a function that takes treatment and covariate history and baseline covariates as inputs and returns a decision to be made. Murphy (2003, Journal of the Royal Statistical Society, Series B 65, 331-366) and Robins (2004, Proceedings of the Second Seattle Symposium on Biostatistics, 189-326) have proposed models and developed semiparametric methods for making inference about the optimal regime in a multi-interval trial that provide clear advantages over traditional parametric approaches. We show that Murphy's model is a special case of Robins's and that the methods are closely related but not equivalent. Interesting features of the methods are highlighted using the Multicenter AIDS Cohort Study and through simulation.

The proposed prevalent new-user cohort design for comparative drug effects studies allows the use of all or most patients exposed to the newer drug, thus permitting a more comprehensive assessment of a new drug's safety. Copyright © 2016 John Wiley & Sons, Ltd.

Personalized medicine is a rapidly expanding area of health research wherein patient level information is used to inform their treatment. Dynamic treatment regimens (DTRs) are a means of formalizing the sequence of treatment decisions that characterize personalized management plans. Identifying the DTR which optimizes expected patient outcome is of obvious interest and numerous methods have been proposed for this purpose. We present a new approach which builds on two established methods: Q-learning and G-estimation, offering the doubly robust property of the latter but with ease of implementation much more akin to the former. We outline the underlying theory, provide simulation studies that demonstrate the double-robustness and efficiency properties of our approach, and illustrate its use on data from the Promotion of Breastfeeding Intervention Trial.

Social epidemiologists often seek to determine the mechanisms that underlie health disparities. This work is typically based on mediation procedures that may not be justified with exposures of common interest in social epidemiology. In this analysis, we explored the consequences of using standard approaches, referred to as the difference and generalized product methods, when mediator-outcome confounders are associated with the exposure. We compared these with inverse probability-weighted marginal structural models, the structural transformation method, doubly robust g-estimation of a structural nested model, and doubly robust targeted minimum loss-based estimation. We used data on 900,726 births from 2003 to 2007 in the Penn Moms study, conducted in Pennsylvania, to assess the extent to which breastfeeding prior to hospital discharge explained the racial disparity in infant mortality. Overall, for every 1,000 births, 3.36 more infant deaths occurred among non-Hispanic black women relative to all other women (95% confidence interval: 2.78, 3.93). Using the difference and generalized product methods to assess the disparity that would remain if everyone breastfed prior to discharge suggested a complete elimination of the disparity (risk difference = -0.87 per 1,000 births; 95% confidence interval: -1.39, -0.35). In contrast, doubly robust methods suggested a reduction in the disparity to 2.45 (95% confidence interval: 2.20, 2.71) more infant deaths per 1,000 births among non-Hispanic black women. Standard approaches for mediation analysis in health disparities research can yield misleading results.

BackgroundAlthough interferon-gamma release assays (IGRA) are promising alternatives to the tuberculin skin test, interpretation of repeated testing results is hampered by lack of evidence on optimal cut-offs for conversions and reversions. A logical start is to determine the within-person variability of T-cell responses during serial testing.Methodology/Principal FindingsWe performed a pilot study in India, to evaluate the short-term reproducibility of QuantiFERON-TB Gold In Tube assay (QFT) among 14 healthcare workers (HCWs) who underwent 4 serial QFT tests on day 0, 3, 9 and 12. QFT ELISA was repeated twice on the same sets of specimens. We assessed two types of reproducibility: 1) test-retest reproducibility (between-test variability), and 2) within-person reproducibility over time. Test-retest reproducibility: with dichotomous test results, extremely high concordance was noticed between two tests performed on the same sets of specimens: of the 56 samples, the test and re-test results agreed for all but 2 individuals (κ = 0.94). Discordance was noted in subjects who had IFN-γ values around the cut-off point, with both increases and decreases noted. With continuous IFN-γ results, re-test results tended to produce higher estimates of IFN-γ than the original test. Within-person reproducibility: when continuous IFN-γ data were analyzed, the within-person reproducibility was moderate to high. While persons with negative QFT results generally stayed negative, positive results tended to vary over time. Our data showed that increases of more than 16% in the IFN-γ levels are statistically improbable in the short-term.ConclusionsConservatively assuming that long-term variability might be at least twice higher than short-term, we hypothesize that a QFT conversion requires two conditions to be met: 1) change from negative to positive result, and 2) at least 30% increase in the baseline IFN-γ response. Larger studies are needed to confirm our preliminary findings, and determine the conversion thresholds for IGRAs.

BackgroundChronic malnutrition, termed stunting, is defined as suboptimal linear growth, affects one third of children in developing countries, and leads to increased mortality and poor developmental outcomes. The causes of childhood stunting are unknown, and strategies to improve growth and related outcomes in children have only had modest impacts. Recent studies have shown that the ecosystem of microbes in the human gut, termed the microbiota, can induce changes in weight. However, the specific changes in the gut microbiota that contribute to growth remain unknown, and no studies have investigated the gut microbiota as a determinant of chronic malnutrition.ResultsWe performed secondary analyses of data from two well-characterized twin cohorts of children from Malawi and Bangladesh to identify bacterial genera associated with linear growth. In a case-control analysis, we used the graphical lasso to estimate covariance network models of gut microbial interactions from relative genus abundances and used network analysis methods to select genera associated with stunting severity. In longitudinal analyses, we determined associations between these selected microbes and linear growth using between-within twin regression models to adjust for confounding and introduce temporality. Reduced microbiota diversity and increased covariance network density were associated with stunting severity, while increased relative abundance of Acidaminococcus sp. was associated with future linear growth deficits.ConclusionsWe show that length growth in children is associated with community-wide changes in the gut microbiota and with the abundance of the bacterial genus, Acidaminococcus. Larger cohorts are needed to confirm these findings and to clarify the mechanisms involved.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-015-0089-2) contains supplementary material, which is available to authorized users.

Objectives To determine whether antibiotic treatment leads to improvements in growth in prepubertal children in low and middle income countries, to determine the magnitude of improvements in growth, and to identify moderators of this treatment effect.Design Systematic review and meta-analysis.Data sources Medline, Embase, Scopus, the Cochrane central register of controlled trials, and Web of Science.Study selection Randomised controlled trials conducted in low or middle income countries in which an orally administered antibacterial agent was allocated by randomisation or minimisation and growth was measured as an outcome. Participants aged 1 month to 12 years were included. Control was placebo or non-antimicrobial intervention.Results Data were pooled from 10 randomised controlled trials representing 4316 children, across a variety of antibiotics, indications for treatment, treatment regimens, and countries. In random effects models, antibiotic use increased height by 0.04 cm/month (95% confidence interval 0.00 to 0.07) and weight by 23.8 g/month (95% confidence interval 4.3 to 43.3). After adjusting for age, effects on height were larger in younger populations and effects on weight were larger in African studies compared with other regions. ConclusionAntibiotics have a growth promoting effect in prepubertal children in low and middle income countries. This effect was more pronounced for ponderal than for linear growth. The antibiotic growth promoting effect may be mediated by treatment of clinical or subclinical infections or possibly by modulation of the intestinal microbiota. Better definition of the mechanisms underlying this effect will be important to inform optimal and safe approaches to achieving healthy growth in vulnerable populations.