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For clinical studies with continuous outcomes, when the data are potentially skewed, researchers may choose to report the whole or part of the five-number summary (the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation. In the recent literature, it is often suggested to transform the five-number summary back to the sample mean and standard deviation, which can be subsequently used in a meta-analysis. However, if a study contains skewed data, this transformation and hence the conclusions from the meta-analysis are unreliable. Therefore, we introduce a novel method for detecting the skewness of data using only the five-number summary and the sample size, and meanwhile, propose a new flow chart to handle the skewed studies in a different manner. We further show by simulations that our skewness tests are able to control the type I error rates and provide good statistical power, followed by a simulated meta-analysis and a real data example that illustrate the usefulness of our new method in meta-analysis and evidence-based medicine. [ABSTRACT FROM AUTHOR]

A reference interval provides a basis for physicians to determine whether a measurement is typical of a healthy individual. It can be interpreted as a prediction interval for a new individual from the overall population. However, a reference interval based on a single study may not be representative of the broader population. Meta‐analysis can provide a general reference interval based on the overall population by combining results from multiple studies. Methods for estimating the reference interval from a random effects meta‐analysis have been recently proposed to incorporate the within and between‐study variation, but a random effects model may give imprecise estimates of the between‐study variation with only few studies. In addition, the normal distribution of underlying study‐specific means, and equal within‐study variance assumption in these methods may be inappropriate in some settings. In this article, we aim to estimate the reference interval based on the fixed effects model assuming study effects are unrelated, which is useful for a meta‐analysis with only a few studies (e.g., ≤5). We propose a mixture distribution method only assuming parametric distributions (e.g., normal) for individuals within each study and integrating them to form the overall population distribution. This method is compared to an empirical method only assuming a parametric overall population distribution. Simulation studies have shown that both methods can estimate a reference interval with coverage close to the targeted value (i.e., 95%). Meta‐analyses of women daytime urination frequency and frontal subjective postural vertical measurements are reanalyzed to demonstrate the application of our methods. [ABSTRACT FROM AUTHOR]

High-throughput omics data are becoming more and more popular in various areas of science. Given that many publicly available datasets address the same questions, researchers have applied meta-analysis to synthesize multiple datasets to achieve more reliable results for model estimation and prediction. Due to the high dimensionality of omics data, it is also desirable to incorporate variable selection into meta-analysis. Existing meta-analyzing variable selection methods are often sensitive to the presence of outliers, and may lead to missed detections of relevant covariates, especially for lasso-type penalties. In this paper, we develop a robust variable selection algorithm for meta-analyzing high-dimensional datasets based on logistic regression. We first search an outlier-free subset from each dataset by borrowing information across the datasets with repeatedly use of the least trimmed squared estimates for the logistic model and together with a hierarchical bi-level variable selection technique. We then refine a reweighting step to further improve the efficiency after obtaining a reliable non-outlier subset. Simulation studies and real data analysis show that our new method can provide more reliable results than the existing meta-analysis methods in the presence of outliers. [ABSTRACT FROM AUTHOR]

When reporting the results of clinical studies, some researchers may choose the five‐number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation (SD), particularly for skewed data. For these studies, when included in a meta‐analysis, it is often desired to convert the five‐number summary back to the sample mean and SD. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this article, we propose to further advance the literature by developing a smoothly weighted estimator for the sample SD that fully utilizes the sample size information. For ease of implementation, we also derive an approximation formula for the optimal weight, as well as a shortcut formula for the sample SD. Numerical results show that our new estimator provides a more accurate estimate for normal data and also performs favorably for non‐normal data. Together with the optimal sample mean estimator in Luo et al., our new methods have dramatically improved the existing methods for data transformation, and they are capable to serve as "rules of thumb" in meta‐analysis for studies reported with the five‐number summary. Finally for practical use, an Excel spreadsheet and an online calculator are also provided for implementing our optimal estimators. [ABSTRACT FROM AUTHOR]

The era of big data is coming, and evidence-based medicine is attracting increasing attention to improve decision making in medical practice via integrating evidence from well designed and conducted clinical research. Meta-analysis is a statistical technique widely used in evidence-based medicine for analytically combining the findings from independent clinical trials to provide an overall estimation of a treatment effectiveness. The sample mean and standard deviation are two commonly used statistics in meta-analysis but some trials use the median, the minimum and maximum values, or sometimes the first and third quartiles to report the results. Thus, to pool results in a consistent format, researchers need to transform those information back to the sample mean and standard deviation. In this article, we investigate the optimal estimation of the sample mean for meta-analysis from both theoretical and empirical perspectives. A major drawback in the literature is that the sample size, needless to say its importance, is either ignored or used in a stepwise but somewhat arbitrary manner, e.g. the famous method proposed by Hozo et al. We solve this issue by incorporating the sample size in a smoothly changing weight in the estimators to reach the optimal estimation. Our proposed estimators not only improve the existing ones significantly but also share the same virtue of the simplicity. The real data application indicates that our proposed estimators are capable to serve as "rules of thumb" and will be widely applied in evidence-based medicine. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is one of the most common endocrine-metabolic disorders in women of reproductive age worldwide. Previous studies using randomized controlled trials (RCTs) have revealed that Xiao Yao San (XYS), a classic Chinese patent medicine formula, can effectively treat PCOS. However, the entire evidence has yet to be systematically summarized. The aim of this systematic review and meta-analysis of clinical trials was to assess the effect of XYS for the treatment of PCOS. 7 databases were thoroughly reviewed for RCTs published from inception to July 2022, assessing the effect of XYS in treating PCOS, including Cochrane Library, PubMed, Embase, Wan Fang Database, Chinese Biomedical Database, China National Knowledge Infrastructure, and China Science and Technology Journal Database. Outcome measures included ovulation rate, pregnancy rate, hormonal levels, and glycemic parameters. Either a random-effects model or a fixed-effect models was used to pool data. Pooled effect sizes were reported as odds ratios (ORs) or standardized mean differences (SMDs) with their 95% confidence intervals (CIs). A total of 9 trials including 736 PCOS patients met the selection criteria. Our results indicate that XYS plus conventional medicines for PCOS significantly improved ovulation rate (OR = 2.45, 95% CI = 1.94 to 3.08, P < 0.001) and pregnancy rate (OR = 2.65, 95% CI = 1.87 to 3.75, P < 0.001), meanwhile decreased levels of fasting insulin (FINS) (SMD = - 0.46, 95% CI: 0.65 to - 0.27, P < 0.001) and homeostatic model assessment for insulin resistance (HOMA-IR) (SMD = - 0.65, 95% CI = - 0.93 to - 0.37, P < 0.001). XYS plus conventional medicines for PCOS did not have a significant impact on levels of total testosterone (T), follicle-stimulating hormone (FSH), luteinizing hormone (LH), and fasting plasma glucose (FPG). No serious adverse reactions were observed. XYS combined with conventional medicines can improve ovulation and pregnancy rates, decrease FINS and HOMA-IR in PCOS patients, indicating that XYS treatment may be used as a promising adjuvant therapy to the conventional medicines of PCOS. However, due to significant heterogeneity and methodological shortcomings, these results should be interpreted with great caution. Larger, higher quality RCTs are needed to rigorously assess the effect of XYS as a complementary therapy in managing PCOS. [Display omitted] • This study is the first systematic review and meta-analysis that examines the efficacy of XYS, a classic Chinese patent medicine formula, in treating PCOS using RCTs. • Our results showed that XYS in combination with the conventional therapy significantly improved the ovulation and pregnancy rates, and decreased levels of FINS and HOMA-IR in PCOS women. • Our results showed that XYS was safe in PCOS patients. • XYS may be a promising candidate for use as an adjuvant therapy to the conventional therapy in treating PCOS. [ABSTRACT FROM AUTHOR]