A versatile test for equality of two survival functions based on weighted differences of Kaplan-Meier curves

With censored event time observations, the logrank test is the most popular tool for testing the equality of two underlying survival distributions. Although this test is asymptotically distribution-free, it may not be powerful when the proportional hazards assumption is violated. Various other novel testing procedures have been proposed, which generally are derived by assuming a class of specific alternative hypotheses with respect to the hazard functions. The test considered by Pepe and Fleming (1989) is based on a linear combination of weighted differences of two Kaplan-Meier curves over time and is a natural tool to assess the difference of two survival functions directly. In this article, we take a similar approach, but choose weights which are proportional to the observed standardized difference of the estimated survival curves at each time point. The new proposal automatically makes weighting adjustments empirically. The new test statistic is aimed at a one-sided general alternative hypothesis, and is distributed with a short right tail under the null hypothesis, but with a heavy tail under the alternative. The results from extensive numerical studies demonstrate that the new procedure performs well under various general alternatives. The survival data from a recent cancer comparative study are utilized for illustrating the implementation of the process

Effectively Selecting a Target Population for a Future Comparative Study

When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. Using this system, we specify a desired level of treatment difference and create a subgroup of patients, defined as those whose estimated scores exceed this threshold. An empirically calibrated group-specific treatment difference curve across a range of threshold values is constructed. The population of patients with any desired level of treatment benefit can then be identified accordingly. To avoid any ``self-serving\u27\u27 bias, we utilize a cross-training-evaluation method for implementing the above two-step procedure. Lastly, we show how to select the best scoring system among all competing models. The proposals are illustrated with the data from two clinical trials in treating AIDS and cardiovascular diseases. Note that if we are not interested in designing a new study for comparing similar treatments, the new procedure can also be quite useful for the management of future patients who would receive nontrivial benefits to compensate for the risk or cost of the new treatment

Influence of Sacubitril/Valsartan (LCZ696) on 30-day readmission after heart failure hospitalization

Background: Patients with heart failure (HF) are at high risk for hospital readmission in the first 30 days following HF hospitalization. Objectives: This study sought to determine if treatment with sacubitril/valsartan (LCZ696) reduces rates of hospital readmission at 30-days following HF hospitalization compared with enalapril. Methods: We assessed the risk of 30-day readmission for any cause following investigator-reported hospitalizations for HF in the PARADIGM-HF trial, which randomized 8,399 participants with HF and reduced ejection fraction to treatment with LCZ696 or enalapril. Results: Accounting for multiple hospitalizations per patient, there were 2,383 investigator-reported HF hospitalizations, of which 1,076 (45.2%) occurred in subjects assigned to LCZ696 and 1,307 (54.8%) occurred in subjects assigned to enalapril. Rates of readmission for any cause at 30 days were 17.8% in LCZ696-assigned subjects and 21.0% in enalapril-assigned subjects (odds ratio: 0.74; 95% confidence interval: 0.56 to 0.97; p = 0.031). Rates of readmission for HF at 30-days were also lower in subjects assigned to LCZ696 (9.7% vs. 13.4%; odds ratio: 0.62; 95% confidence interval: 0.45 to 0.87; p = 0.006). The reduction in both all-cause and HF readmissions with LCZ696 was maintained when the time window from discharge was extended to 60 days and in sensitivity analyses restricted to adjudicated HF hospitalizations. Conclusions: Compared with enalapril, treatment with LCZ696 reduces 30-day readmissions for any cause following discharge from HF hospitalization

A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations.

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel 'rain plot' approach to display the results of these analyses. The 'rain plot' combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically

Renal effects and associated outcomes during angiotensin-neprilysin inhibition in heart failure

Objectives: The purpose of this study was to evaluate the renal effects of sacubitril/valsartan in patients with heart failure and reduced ejection fraction. Background: Renal function is frequently impaired in patients with heart failure with reduced ejection fraction and may deteriorate further after blockade of the renin–angiotensin system. Methods: In the PARADIGM-HF (Prospective Comparison of ARNI with ACE inhibition to Determine Impact on Global Mortality and Morbidity in Heart Failure) trial, 8,399 patients with heart failure with reduced ejection fraction were randomized to treatment with sacubitril/valsartan or enalapril. The estimated glomerular filtration rate (eGFR) was available for all patients, and the urinary albumin/creatinine ratio (UACR) was available in 1872 patients, at screening, randomization, and at fixed time intervals during follow-up. We evaluated the effect of study treatment on change in eGFR and UACR, and on renal and cardiovascular outcomes, according to eGFR and UACR. Results: At screening, the eGFR was 70 ± 20 ml/min/1.73 m2 and 2,745 patients (33%) had chronic kidney disease; the median UACR was 1.0 mg/mmol (interquartile range: 0.4 to 3.2 mg/mmol) and 24% had an increased UACR. The decrease in eGFR during follow-up was less with sacubitril/valsartan compared with enalapril (−1.61 ml/min/1.73 m2/year; [95% confidence interval: −1.77 to −1.44 ml/min/1.73 m2/year] vs. −2.04 ml/min/1.73 m2/year [95% CI: −2.21 to −1.88 ml/min/1.73 m2/year ]; p < 0.001) despite a greater increase in UACR with sacubitril/valsartan than with enalapril (1.20 mg/mmol [95% CI: 1.04 to 1.36 mg/mmol] vs. 0.90 mg/mmol [95% CI: 0.77 to 1.03 mg/mmol]; p < 0.001). The effect of sacubitril/valsartan on cardiovascular death or heart failure hospitalization was not modified by eGFR, UACR (p interaction = 0.70 and 0.34, respectively), or by change in UACR (p interaction = 0.38). Conclusions: Compared with enalapril, sacubitril/valsartan led to a slower rate of decrease in the eGFR and improved cardiovascular outcomes, even in patients with chronic kidney disease, despite causing a modest increase in UACR

Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations