The Experts below are selected from a list of 192 Experts worldwide ranked by ideXlab platform
Tiejun Tong - One of the best experts on this subject based on the ideXlab platform.
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optimally estimating the Sample Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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.
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optimally estimating the Sample Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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, 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 Standard Deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the Sample Standard Deviation 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 Standard Deviation. 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.
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optimally estimating the Sample mean and Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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, 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 Standard Deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the Sample Standard Deviation 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 Standard Deviation. 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.
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how to estimate the Sample mean and Standard Deviation from the five number summary
2018Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Tiejun TongAbstract:In some clinical studies, researchers may report 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. To conduct meta-analysis for pooling studies, one needs to first estimate the Sample mean and Standard Deviation from the five number summary. A number of studies have been proposed in the recent literature to solve this problem. However, none of the existing estimators for the Standard Deviation is satisfactory for practical use. After a brief review of the existing literature, we point out that Wan et al.'s method (BMC Med Res Methodol 14:135, 2014) has a serious limitation in estimating the Standard Deviation from the five number summary. To improve it, we propose a smoothly weighted estimator by incorporating the Sample size information and derive the optimal weight for the new estimator. For ease of implementation, we also provide an approximation formula of the optimal weight and a shortcut formula for estimating the Standard Deviation from the five number summary. The performance of the proposed estimator is evaluated through two simulation studies. In comparison with Wan et al.'s estimator, our new estimator provides a more accurate estimate for normal data and performs favorably for non-normal data. In real data analysis, our new method is also able to provide a more accurate estimate of the true Sample Standard Deviation than the existing method. In this paper, we propose an optimal estimator of the Standard Deviation from the five number summary. Together with the optimal mean estimator in Luo et al. (Stat Methods Med Res, in press, 2017), our new methods have improved the existing literature and will make a solid contribution to meta-analysis and evidence-based medicine.
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estimating the Sample mean and Standard Deviation from the Sample size median range and or interquartile range
2014Co-Authors: Wenqian Wang, Tiejun TongAbstract:In systematic reviews and meta-analysis, researchers often pool the results of the Sample mean and Standard Deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the Sample mean and Standard Deviation for such trials. In this paper, we propose to improve the existing literature in several directions. First, we show that the Sample Standard Deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the Sample size. Second, we systematically study the Sample mean and Standard Deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true Sample Standard Deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the Sample mean and Standard Deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. In this paper, we discuss different approximation methods in the estimation of the Sample mean and Standard Deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations.
Jiandong Shi - One of the best experts on this subject based on the ideXlab platform.
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optimally estimating the Sample Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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.
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optimally estimating the Sample Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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, 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 Standard Deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the Sample Standard Deviation 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 Standard Deviation. 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.
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optimally estimating the Sample mean and Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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, 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 Standard Deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the Sample Standard Deviation 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 Standard Deviation. 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.
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how to estimate the Sample mean and Standard Deviation from the five number summary
2018Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Tiejun TongAbstract:In some clinical studies, researchers may report 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. To conduct meta-analysis for pooling studies, one needs to first estimate the Sample mean and Standard Deviation from the five number summary. A number of studies have been proposed in the recent literature to solve this problem. However, none of the existing estimators for the Standard Deviation is satisfactory for practical use. After a brief review of the existing literature, we point out that Wan et al.'s method (BMC Med Res Methodol 14:135, 2014) has a serious limitation in estimating the Standard Deviation from the five number summary. To improve it, we propose a smoothly weighted estimator by incorporating the Sample size information and derive the optimal weight for the new estimator. For ease of implementation, we also provide an approximation formula of the optimal weight and a shortcut formula for estimating the Standard Deviation from the five number summary. The performance of the proposed estimator is evaluated through two simulation studies. In comparison with Wan et al.'s estimator, our new estimator provides a more accurate estimate for normal data and performs favorably for non-normal data. In real data analysis, our new method is also able to provide a more accurate estimate of the true Sample Standard Deviation than the existing method. In this paper, we propose an optimal estimator of the Standard Deviation from the five number summary. Together with the optimal mean estimator in Luo et al. (Stat Methods Med Res, in press, 2017), our new methods have improved the existing literature and will make a solid contribution to meta-analysis and evidence-based medicine.
Kalpesh S Tailor - One of the best experts on this subject based on the ideXlab platform.
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Sample Standard Deviation s chart under the assumption of moderateness and its performance analysis
2017Co-Authors: Kalpesh S TailorAbstract:Moderate distribution proposed by Naik V.D and Desai J.M., is a sound alternative of normal distribution, which has mean and mean Deviation as pivotal parameters and which has properties similar to normal distribution. Mean Deviation (δ) is a very good alternative of Standard Deviation (σ) as mean Deviation is considered to be the most intuitively and rationally defined measure of dispersion. This fact can be very useful in the field of quality control to construct the control limits of the control charts. On the basis of this fact Naik V.D. and Tailor K.S. have proposed 3δ control limits. In 3δ control limits, the upper and lower control limits are set at 3δ distance from the central line where δ is the mean Deviation of sampling distribution of the statistic being used for constructing the control chart. In this paper assuming that the underlying distribution of the variable of interest follows moderate distribution proposed by Naik V.D and Desai J.M, 3δ control limits of Sample Standard Deviation(s) chart are derived. Also the performance analysis of the control chart is carried out with the help of OC curve analysis and ARL curve analysis.
Lu Lin - One of the best experts on this subject based on the ideXlab platform.
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optimally estimating the Sample Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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.
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optimally estimating the Sample Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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, 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 Standard Deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the Sample Standard Deviation 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 Standard Deviation. 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.
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optimally estimating the Sample mean and Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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, 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 Standard Deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the Sample Standard Deviation 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 Standard Deviation. 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.
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how to estimate the Sample mean and Standard Deviation from the five number summary
2018Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Tiejun TongAbstract:In some clinical studies, researchers may report 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. To conduct meta-analysis for pooling studies, one needs to first estimate the Sample mean and Standard Deviation from the five number summary. A number of studies have been proposed in the recent literature to solve this problem. However, none of the existing estimators for the Standard Deviation is satisfactory for practical use. After a brief review of the existing literature, we point out that Wan et al.'s method (BMC Med Res Methodol 14:135, 2014) has a serious limitation in estimating the Standard Deviation from the five number summary. To improve it, we propose a smoothly weighted estimator by incorporating the Sample size information and derive the optimal weight for the new estimator. For ease of implementation, we also provide an approximation formula of the optimal weight and a shortcut formula for estimating the Standard Deviation from the five number summary. The performance of the proposed estimator is evaluated through two simulation studies. In comparison with Wan et al.'s estimator, our new estimator provides a more accurate estimate for normal data and performs favorably for non-normal data. In real data analysis, our new method is also able to provide a more accurate estimate of the true Sample Standard Deviation than the existing method. In this paper, we propose an optimal estimator of the Standard Deviation from the five number summary. Together with the optimal mean estimator in Luo et al. (Stat Methods Med Res, in press, 2017), our new methods have improved the existing literature and will make a solid contribution to meta-analysis and evidence-based medicine.
Xiantao Zeng - One of the best experts on this subject based on the ideXlab platform.
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optimally estimating the Sample Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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.
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optimally estimating the Sample Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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, 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 Standard Deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the Sample Standard Deviation 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 Standard Deviation. 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.
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optimally estimating the Sample mean and Standard Deviation from the five number summary
2020Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Haitao Chu, Tiejun TongAbstract: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, 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 Standard Deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the Sample Standard Deviation 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 Standard Deviation. 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.
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how to estimate the Sample mean and Standard Deviation from the five number summary
2018Co-Authors: Jiandong Shi, Dehui Luo, Hong Weng, Xiantao Zeng, Lu Lin, Tiejun TongAbstract:In some clinical studies, researchers may report 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. To conduct meta-analysis for pooling studies, one needs to first estimate the Sample mean and Standard Deviation from the five number summary. A number of studies have been proposed in the recent literature to solve this problem. However, none of the existing estimators for the Standard Deviation is satisfactory for practical use. After a brief review of the existing literature, we point out that Wan et al.'s method (BMC Med Res Methodol 14:135, 2014) has a serious limitation in estimating the Standard Deviation from the five number summary. To improve it, we propose a smoothly weighted estimator by incorporating the Sample size information and derive the optimal weight for the new estimator. For ease of implementation, we also provide an approximation formula of the optimal weight and a shortcut formula for estimating the Standard Deviation from the five number summary. The performance of the proposed estimator is evaluated through two simulation studies. In comparison with Wan et al.'s estimator, our new estimator provides a more accurate estimate for normal data and performs favorably for non-normal data. In real data analysis, our new method is also able to provide a more accurate estimate of the true Sample Standard Deviation than the existing method. In this paper, we propose an optimal estimator of the Standard Deviation from the five number summary. Together with the optimal mean estimator in Luo et al. (Stat Methods Med Res, in press, 2017), our new methods have improved the existing literature and will make a solid contribution to meta-analysis and evidence-based medicine.
