Simulation Program to Determine Sample Size and Power for a Multiple Logistic Regression Model with Unspecified Covariate Distributions

Binary logistic regression models are commonly used to assess the association between outcomes and covariates. Many covariates are inherently continuous, and have a variety of distributions, including those that are heavily skewed to the left or right. Existing theoretical formulas, criteria, and simulation programs cannot accurately estimate the sample size and power of non-standard distributions. Therefore, we have developed a simulation program that uses Monte Carlo methods to estimate the exact power of a binary logistic regression model. This power calculation can be used for distributions of any shape and covariates of any type (continuous, ordinal, and nominal), and can account for nonlinear relationships between covariates and outcomes. For illustrative purposes, this simulation program is applied to real data obtained from a study on the influence of smoking on 90-day outcomes after acute atherothrombotic stroke. Our program is applicable to all effect sizes and makes it possible to apply various statistical methods, logistic regression and related simulations such as Bayesian inference with some modifications.

Power analysis,sample size calculation for testing the largest binomial probability

A procedure is developed for power analysis and sample size calculation for a class of complex testing problems regarding the largest binomial probability under a combination of treatments.It is shown that the asymptotic null distribution of the likelihood-ratio statistic is not parameterfree,but χ_(1)^(2) is a conservative asymptotic null distribution.A nonlinear Gauss-Seidel algorithm is proposed to uniquely determine the alternative for the power and sample size calculation given the baseline binomial probability.An example from an animal clinical trial is discussed.

单样本和两样本单侧Z检验P值的理论分布及应用

目的 探讨单样本和两样本Z检验P值的理论分布及其与α、1-β、δ和σ的数量关系。 方法 分别以单样本均数 (率 )、两样本均数 (率 )比较的Z检验 ,说明P值的概率密度分布和累积分布函数及其曲线图。结果 给出了备择假设成立时 ,P值分布的百分位数及其与n、β、δ的数量关系。

燃煤电厂烟气湿度控制对PM_(2.5)采样结果的影响

为研究脱硫后高湿烟气相对湿度的改变对颗粒物采样结果的影响,使用扩散干燥管对烟气进行除湿,并分析不同相对湿度下PM_(2.5)的粒径分布、传输损失和质量浓度变化。结果显示,模拟烟气湿度越高,PM_(2.5)粒数浓度和质量浓度也随之升高。40%相对湿度下,模拟烟气中ρ(PM_(2.5))为22.77 mg/m^(3),粒数浓度约为1.16×10^(6)个/cm^(3),模拟烟气湿度提升至50%、60%和80%时,ρ(PM_(2.5))分别升高1.06,4.35,4.69倍,粒数浓度分别升高1.31,1.70,1.76倍;使用扩散干燥管对模拟高湿烟气除湿,采集到的颗粒物质量浓度有不同程度的下降,最高相对湿度(94.9%)下,ρ(PM_(2.5))为27.17 mg/Nm^(3),湿度81.4%、68.5%、48.7%和30.4%时,质量浓度相比最高湿度时分别降低了14.5%、28.8%、43.0%和45.7%;烟气除湿可降低PM_(2.5)在管路内和切割头内的损失,在94.9%、81.4%和68.5%相对湿度下采集到的ρ(PM_(2.5))分别是对照组的3.02,2.73,2.57倍。高湿烟气除湿降低了颗粒物的含水率,使得PM_(2.5)的质量浓度测量结果随着除湿强度增加而降低,但因除湿降低了颗粒物损失,相比对照组最终采集到的PM_(2.5)质量浓度随着除湿强度增加而显著提升。