需要治疗的病例数(number needed to treat,NNT)是近年来国际上用于评价临床疗效的一个简单而有效的指标,它通过对分类变量数据绝对危险度减低这一指标的计算,能够客观评价干预措施的临床疗效。然而,目前国内对于这一指标临床应用的介...需要治疗的病例数(number needed to treat,NNT)是近年来国际上用于评价临床疗效的一个简单而有效的指标,它通过对分类变量数据绝对危险度减低这一指标的计算,能够客观评价干预措施的临床疗效。然而,目前国内对于这一指标临床应用的介绍尚未见报道。本文通过已发表的临床研究文献的实例,解释NNT这一指标及其95%可信区间的计算,并通过其相关名词的解释、荟萃分析的方法等方面说明该指标的具体应用。展开更多
Assessing geographic variations in health events is one of the major tasks in spatial epidemiologic studies. Geographic variation in a health event can be estimated using the neighborhood-level variance that is derive...Assessing geographic variations in health events is one of the major tasks in spatial epidemiologic studies. Geographic variation in a health event can be estimated using the neighborhood-level variance that is derived from a generalized mixed linear model or a Bayesian spatial hierarchical model. Two novel heterogeneity measures, including median odds ratio and interquartile odds ratio, have been developed to quantify the magnitude of geographic variations and facilitate the data interpretation. However, the statistical significance of geographic heterogeneity measures was inaccurately estimated in previous epidemiologic studies that reported two-sided 95% confidence intervals based on standard error of the variance or 95% credible intervals with a range from 2.5th to 97.5th percentiles of the Bayesian posterior distribution. Given the mathematical algorithms of heterogeneity measures, the statistical significance of geographic variation should be evaluated using a one-tailed P value. Therefore, previous studies using two-tailed 95% confidence intervals based on a standard error of the variance may have underestimated the geographic variation in events of their interest and those using 95% Bayesian credible intervals may need to re-evaluate the geographic variation of their study outcomes.展开更多
文摘需要治疗的病例数(number needed to treat,NNT)是近年来国际上用于评价临床疗效的一个简单而有效的指标,它通过对分类变量数据绝对危险度减低这一指标的计算,能够客观评价干预措施的临床疗效。然而,目前国内对于这一指标临床应用的介绍尚未见报道。本文通过已发表的临床研究文献的实例,解释NNT这一指标及其95%可信区间的计算,并通过其相关名词的解释、荟萃分析的方法等方面说明该指标的具体应用。
文摘Assessing geographic variations in health events is one of the major tasks in spatial epidemiologic studies. Geographic variation in a health event can be estimated using the neighborhood-level variance that is derived from a generalized mixed linear model or a Bayesian spatial hierarchical model. Two novel heterogeneity measures, including median odds ratio and interquartile odds ratio, have been developed to quantify the magnitude of geographic variations and facilitate the data interpretation. However, the statistical significance of geographic heterogeneity measures was inaccurately estimated in previous epidemiologic studies that reported two-sided 95% confidence intervals based on standard error of the variance or 95% credible intervals with a range from 2.5th to 97.5th percentiles of the Bayesian posterior distribution. Given the mathematical algorithms of heterogeneity measures, the statistical significance of geographic variation should be evaluated using a one-tailed P value. Therefore, previous studies using two-tailed 95% confidence intervals based on a standard error of the variance may have underestimated the geographic variation in events of their interest and those using 95% Bayesian credible intervals may need to re-evaluate the geographic variation of their study outcomes.
