Correlates of Achieving Statin Therapy Goals in Children and Adolescents with Dyslipidemia.

OBJECTIVE: To determine the real-world effectiveness of statins and impact of baseline factors on low-density lipoprotein cholesterol (LDL-C) reduction among children and adolescents. STUDY DESIGN: We analyzed data prospectively collected from a quality improvement initiative in the Boston Children's Hospital Preventive Cardiology Program. We included patients ≤21 years of age initiated on statins between September 2010 and March 2014. The primary outcome was first achieving goal LDL-C, defined as <130 mg/dL, or <100 mg/dL with high-level risk factors (eg, diabetes, etc). Cox proportional hazards models were used to assess the impact of baseline clinical and lifestyle factors. RESULTS: Among the 1521 pediatric patients evaluated in 3813 clinical encounters over 3.5 years, 97 patients (6.3%) were started on statin therapy and had follow-up data (median age 14 [IQR 7] years, 54% were female, and 24% obese, 62% with at least one lifestyle risk factor). The median baseline LDL-C was 215 (IQR 78) mg/dL, and median follow-up after starting statin was 1 (IQR 1.3) year. The cumulative probability of achieving LDL-C goal within 1 year was 60% (95% CI 47-69). A lower probability of achieving LDL-C goals was associated with male sex (HR 0.5 [95% CI 0.3-0.8]) and higher baseline LDL-C (HR 0.92 [95% CI 0.87-0.98] per 10 mg/dL), but not age, body mass index percentile, lifestyle factors, or family history. CONCLUSIONS: The majority of pediatric patients started on statins reached LDL-C treatment goals within 1 year. Male patients and those with greater baseline LDL-C were less likely to be successful and may require increased support.

Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy.

A personalized treatment strategy formalizes evidence-based treatment selection by mapping patient information to a recommended treatment. Personalized treatment strategies can produce better patient outcomes while reducing cost and treatment burden. Thus, among clinical and intervention scientists, there is a growing interest in conducting randomized clinical trials when one of the primary aims is estimation of a personalized treatment strategy. However, at present, there are no appropriate sample size formulae to assist in the design of such a trial. Furthermore, because the sampling distribution of the estimated outcome under an estimated optimal treatment strategy can be highly sensitive to small perturbations in the underlying generative model, sample size calculations based on standard (uncorrected) asymptotic approximations or computer simulations may not be reliable. We offer a simple and robust method for powering a single stage, two-armed randomized clinical trial when the primary aim is estimating the optimal single stage personalized treatment strategy. The proposed method is based on inverting a plugin projection confidence interval and is thereby regular and robust to small perturbations of the underlying generative model. The proposed method requires elicitation of two clinically meaningful parameters from clinical scientists and uses data from a small pilot study to estimate nuisance parameters, which are not easily elicited. The method performs well in simulated experiments and is illustrated using data from a pilot study of time to conception and fertility awareness.

Multivariate analysis of functional metagenomes.

Metagenomics is a primary tool for the description of microbial and viral communities. The sheer magnitude of the data generated in each metagenome makes identifying key differences in the function and taxonomy between communities difficult to elucidate. Here we discuss the application of seven different data mining and statistical analyses by comparing and contrasting the metabolic functions of 212 microbial metagenomes within and between 10 environments. Not all approaches are appropriate for all questions, and researchers should decide which approach addresses their questions. This work demonstrated the use of each approach: for example, random forests provided a robust and enlightening description of both the clustering of metagenomes and the metabolic processes that were important in separating microbial communities from different environments. All analyses identified that the presence of phage genes within the microbial community was a predictor of whether the microbial community was host-associated or free-living. Several analyses identified the subtle differences that occur with environments, such as those seen in different regions of the marine environment.