One-sample aggregate data meta-analysis of medians

An aggregate data meta-analysis is a statistical method that pools the summary statistics of several selected studies to estimate the outcome of interest. When considering a continuous outcome, typically each study must report the same measure of the outcome variable and its spread (e.g., the sample mean and its standard error). However, some studies may instead report the median along with various measures of spread. Recently, the task of incorporating medians in meta-analysis has been achieved by estimating the sample mean and its standard error from each study that reports a median in order to meta-analyze the means. In this paper, we propose two alternative approaches to meta-analyze data that instead rely on medians. We systematically compare these approaches via simulation study to each other and to methods that transform the study-specific medians and spread into sample means and their standard errors. We demonstrate that the proposed median-based approaches perform better than the transformation-based approaches, especially when applied to skewed data and data with high inter-study variance. In addition, when meta-analyzing data that consists of medians, we show that the median-based approaches perform considerably better than or comparably to the best-case scenario for a transformation approach: conducting a meta-analysis using the actual sample mean and standard error of the mean of each study. Finally, we illustrate these approaches in a meta-analysis of patient delay in tuberculosis diagnosis

Simulation Experiments as a Causal Problem

Simulation methods are among the most ubiquitous methodological tools in statistical science. In particular, statisticians often is simulation to explore properties of statistical functionals in models for which developed statistical theory is insufficient or to assess finite sample properties of theoretical results. We show that the design of simulation experiments can be viewed from the perspective of causal intervention on a data generating mechanism. We then demonstrate the use of causal tools and frameworks in this context. Our perspective is agnostic to the particular domain of the simulation experiment which increases the potential impact of our proposed approach. In this paper, we consider two illustrative examples. First, we re-examine a predictive machine learning example from a popular textbook designed to assess the relationship between mean function complexity and the mean-squared error. Second, we discuss a traditional causal inference method problem, simulating the effect of unmeasured confounding on estimation, specifically to illustrate bias amplification. In both cases, applying causal principles and using graphical models with parameters and distributions as nodes in the spirit of influence diagrams can 1) make precise which estimand the simulation targets , 2) suggest modifications to better attain the simulation goals, and 3) provide scaffolding to discuss performance criteria for a particular simulation design.Comment: 19 pages, 17 figures. Under review at Statistical Scienc

Estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis

Researchers increasingly use meta-analysis to synthesize the results of several studies in order to estimate a common effect. When the outcome variable is continuous, standard meta-analytic approaches assume that the primary studies report the sample mean and standard deviation of the outcome. However, when the outcome is skewed, authors sometimes summarize the data by reporting the sample median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles, but do not report the mean or standard deviation. To include these studies in meta-analysis, several methods have been developed to estimate the sample mean and standard deviation from the reported summary data. A major limitation of these widely used methods is that they assume that the outcome distribution is normal, which is unlikely to be tenable for studies reporting medians. We propose two novel approaches to estimate the sample mean and standard deviation when data are suspected to be non-normal. Our simulation results and empirical assessments show that the proposed methods often perform better than the existing methods when applied to non-normal data

The geology of zinc in coals of the Illinois Basin

Final report to the U.S. Geological Survey, Branch of Eastern Mineral Resources, U.S. Department of Interior. June 1975 to September 1977.U.S. Department of Interior Grant 14-08-0001-G-249Ope

Influence of the Valine Zipper Region on the Structure and Aggregation of the Basic Leucine Zipper (bZIP) Domain of Activating Transcription Factor 5 (ATF5)

Protein aggregation is a major problem for biopharmaceuticals. While the control of aggregation is critically important for the future of protein pharmaceuticals, mechanisms of aggregate assembly, particularly the role that structure plays, are still poorly understood. Increasing evidence indicates that partially folded intermediates critically influence the aggregation pathway. We have previously reported the use of the basic leucine zipper (bZIP) domain of Activating Transcription Factor 5 (ATF5) as a partially folded model system to investigate protein aggregation. This domain contains three regions with differing structural propensity: a N-terminal polybasic region, a central helical leucine zipper region, and a C-terminal extended valine zipper region. Additionally, a centrally positioned cysteine residue readily forms an intermolecular disulfide bond that reduces aggregation. Computational analysis of ATF5 predicts that the valine zipper region facilitates self-association. Here we test this hypothesis using a truncated mutant lacking the C-terminal valine zipper region. We compare the structure and aggregation of this mutant to the wild-type (WT) form under both reducing and non-reducing conditions. Our data indicate that removal of this region results in a loss of alpha-helical structure in the leucine zipper and a change in the mechanism of self-association. The mutant form displays increased association at low temperature but improved resistance to thermally induced aggregation

Procedures for assessing psychological predictors of injuries in circus artists: a pilot prospective study

Background: Research on psychological risk factors for injury has focused on stable traits. Our objective was to test the feasibility of a prospective longitudinal study designed to examine labile psychological states as risk factors of injury. Methods: We measured psychological traits at baseline (mood, ways of coping and anxiety), and psychological states every day (1-item questions on anxiety, sleep, fatigue, soreness, self-confidence) before performances in Cirque du Soleil artists of the show “O”. Additional questions were added once per week to better assess anxiety (20-item) and mood. Questionnaires were provided in English, French, Russian and Japanese. Injury and exposure data were extracted from electronic records that are kept as part of routine business practices. Results: The 43.9% (36/82) recruitment rate was more than expected. Most artists completed the baseline questionnaires in 15 min, a weekly questionnaire in <2 min and a daily questionnaire in <1 min. We improved the formatting of some questions during the study, and adapted the wording of other questions to improve clarity. There were no dropouts during the entire study, suggesting the questionnaires were appropriate in content and length. Results for sample size calculations depend on the number of artists followed and the minimal important difference in injury rates, but in general, preclude a purely prospective study with daily data collection because of the long follow-up required. However, a prospective nested case-crossover design with data collection bi-weekly and at the time of injury appears feasible. Conclusion: A prospective study collecting psychological state data from subjects who train and work regularly together is feasible, but sample size calculations suggest that the optimal study design would use prospective nested case-crossover methodology

Measles virus causes immunogenic cell death in human melanoma

Oncolytic viruses (OV) are promising treatments for cancer, with several currently undergoing testing in randomised clinical trials. Measles virus (MV) has not yet been tested in models of human melanoma. This study demonstrates the efficacy of MV against human melanoma. It is increasingly recognised that an essential component of therapy with OV is the recruitment of host anti-tumour immune responses, both innate and adaptive. MV-mediated melanoma cell death is an inflammatory process, causing the release of inflammatory cytokines including type-1 interferons and the potent danger signal HMGB1. Here, using human in vitro models, we demonstrate that MV enhances innate antitumour activity, and that MV-mediated melanoma cell death is capable of stimulating a melanoma-specific adaptive immune response