Sample-size analysis for confidence intervals in Stata 16
The new ciwidth command performs Precision and Sample Size (PrSS) analysis, which is sample-size analysis for confidence intervals (CIs). This method is used when you are planning a study and you want to optimally allocate resources when CIs are to be used for inference. Said differently, you use this method when you want to estimate the sample size required to achieve the desired precision of a CI in a planned study.
ciwidth produces sample sizes, precision, and more that are required for the
• CI for one mean
• CI for one variance
• CI for two independent means
• CI for two paired means
The control panel interface lets you select the analysis type and input assumptions to obtain desired results.
ciwidth allows results to be displayed in customizable tables and graphs.
ciwidth also provides facilities for you to add your own methods.
2019年6月Stata 15正式发布。这是Stata有史以来较大的一次版本更新。我们贴出了Statalist并且列出了16项较重要的新功能。这篇文章会重点谈谈这些新功能：
扩展回归模型
潜在类别分析（LCA）
贝叶斯前缀指令
线性动态随机一般均衡（DSGE）模型
web 的动态Markdown文档
非线性混合效应模型
空间自回归模型（SAR）
区间删失参数生存时间模型
有限混合模型（FMMs）
混合Logit模型
非参数回归
聚类随机设计和回归模型的功率分析
Word和PDF文档
图形颜色透明度/不透明度
ICD-10-CM/PCS支持
联邦储备经济数据（FRED）支持
其他
上面列出的十六功能当然是重要的, 但还有其他值得一提的。比较*想到的是:
. 贝叶斯多级模型
. 门限回归
. 具有随机系数的面板数据tobit
. 区间测量结果的多层回归
. 删失结果的多级Tobit回归
. 面板数据的协整测试
. 时间序列中多断点的测试
. 多组广义 SEM
. 异方差的线性回归
. Heckman风格的样本选择Poisson模型
. 具有随机系数的面板数据非线性模型
. 贝叶斯面板数据模型
. 随机系数的面板数据区间回归
. SVG的导出
. 贝叶斯生存模型
. 零膨胀有序概率
. 添加您自己的电源和样本大小的方法
. 贝叶斯样本选择模型
. 支持瑞典语
. 对DO文件编辑器的改进
. 流随机数生成器
. 对于java插件的改进
. Stata / MP更多的并行化
Stata 16 has a new suite of commands for performing meta-analysis. This suite lets you explore and combine the results from different studies. For instance, if you have collected results from 20 studies about the effect of a particular drug on blood pressure, you can summarize these studies and estimate the overall effect using meta-analysis.
The new meta suite is broad, but what sets it apart is its simplicity.
You can type, for instance,
. meta set effectsize stderr
to declare precomputed effect sizes or use meta esize to compute effects from summary data. With this, you can perform random-effects, fixed-effects, or common-effect meta-analysis.
To estimate an overall effect size and its confidence interval, obtain heterogeneity statistics, and more, you simply type
. meta summarize
And visualizing the results is as easy as typing
. meta forestplot
But the meta suite provides much more.
Meta-regression and subgroup analysis allow you to evaluate the heterogeneity of studies. These are available via meta regress and meta forestplot, subgroup() or meta summarize, subgroup().
You can investigate potential publication bias. Check visually for funnel-plot asymmetry using meta funnelplot; formally test for funnel-plot asymmetry using meta bias; and assess publication bias using the trim-and-fill method with meta trimfill.
You can even perform cumulative meta-analysis with meta summarize, cumulative().
All the meta-analysis features are documented in the new Meta-analysis Reference Manual.
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