率的等效性检验方法的比较

目的 :对 10种率的等效性检验方法的Ⅰ型误差和检验效能进行比较 ,并据此选择较好的方法估计样本含量 ,以便实际工作者应用。方法 :采用MonteCarlo模拟试验。结果 :10种方法中 ,以Dun nett Gent方法为最佳 ,并按该方法估计了样本含量。模拟试验表明 ,率的等效性检验所需要的样本含量非常大 ,当试验组和参考组各只有 10 0例时 ,任何等效性检验方法的检验效能均较低。结论 :按目前国家食品药品监督管理局规定的最低样本含量 10 0对 ,相应的检验效能很低 ,建议用统计学方法进行估计。     

个体生物等效性检验的样本含量估计

目的:对两种设计方法、三种检验方法的个体生物等效性的检验效能进行比较,并估计样本含量。方法:采用Monte-Carlo模拟研究。结果:2×4交叉设计所需的样本含量低于2×3设计。在个体内变异小于0.2时,可以采用估计法进行样本含量的估计;在个体内变异接近0.2时,可以采用检验法进行样本含量的估计;在个体内变异大于0.3时,可以选任一方法(估计法和检验法)估计样本含量,并选择合适的方法进行样本含量的估计。结论:个体生物等效性的样本含量因不同的个体内变异和个体与药物间的交互作用、设计而不同。     

相对率的非劣效性试验检验效能及样本量的模拟计算方法及SAS实现

目的 :当评价指标为定性指标时 ,利用相对率作为标准判断非劣效性是一种方便易行的方法 ,本文目的在于解决这一背景下的样本量计算问题。方法 :利用随机模拟方法 ,在SAS下编写宏 ,估算样本量。结果 :设计出两个宏 ,分别计算给定样本量时的检验效能和限定检验效能时的样本量。结论 :探索出一条在临床研究中利用随机模拟方法估算样本量的途径 ,为类似问题的解决提供了参考     

统计学上影响样本量的条件

<正>一般来说,统计学上样本含量估算取决于四个要素:(1)假设检验的Ⅰ类错误概率α的大小,Ⅰ类错误概率α越小,所需样本含量越大。(2)假设检验的Ⅱ类错误概率β或检验效能1-β的大小。一般要求检验效能最好在0.80及以上,Ⅱ类错误概率β越小,所需样本含量越大。(3)总体间差值δ的大小。如两总体均数的差值δ=|μ1-μ2|,或两总体率的差值δ=|π1-π2|。δ值越小,所需样本含量越大。若研究者无法获得δ的信息,可通过查阅文献     

P值大小不等价于差异或相关性大小

目的 探讨P值与差异或相关性大小的关系。方法 固定样本统计量[均数和标准差、样本率、相关系数、OR(95%CI)],分别模拟独立样本t检验、两组样本率比较的卡方检验、Pearson相关分析、单因素Logistic回归分析的数据,观察不同样本量下P值差异。结果 两组样本统计量相同或单组样本统计量一致但样本量不同时,P值可能大于0.05、介于0.001～0.05或小于0.001。结论 P值受样本量影响大,其大小不等价于差异或相关性大小。     

相关系数假设检验的模拟研究──t检验和u检验的比较

对各种条件下样本相关系数与已知总体相关系数比较的 t检验和 u检验进行了模拟研究 ,考察了两种方法犯第一类错误的概率和检验效能 ,并提出了两种方法的应用条件     

样本量和检验效能估计的Excel快速实现

目的:探讨对假设检验资料进行样本量和检验效能估计的Excel快速实现的方法。方法:在Excel工作表中,利用Excel函数ASIN、BINOMDIST、FINV、NORMSINV、NORMSDIST等,将α、β、容许误差δ、标准差σ、总体率π等原始数据与最终估计的样本量和检验效能部署在同一界面,将其他中间计算数据隐藏,最终统计分析结论可随原始数据立即呈现。结果:建立"样本量和检验效能估计"的Excel工作表后,对常用的几种假设检验资料进行样本量和检验效能估计时仅仅录入相关的α、β、容许误差δ、标准差σ、总体率π等,不须再录入任何统计公式和命令,就能立即得到样本量和检验效能的估计值。结论:利用Excel能直观快速进行假设检验的样本量和检验效能的估计。     

叶黄素热稳定性及热降解动力学研究

目的考察叶黄素的热稳定性并估算其不同温度下保存时间、降解反应级数及其动力学数据。方法利用紫外分光光度法考察叶黄素含量与紫外吸收值的线性关系,并以此测定叶黄素在不同温度下热降解数据。用积分法及Arrhenius公式估算其降解反应级数和反应动力学数据以及保存时间。结果叶黄素含量与紫外吸收值呈很好的线性关系;其热降解反应为1.5级较为合适。结论其热降解反应活化能为77.16 kJ.mol-1,指前因子K0=5.66E+10h-1。在摄氏4℃下保存时间t1/2为241 d。     

配对二项数据等效性／非劣效性评价的样本含量估计和假设检验

新药及医疗器械临床试验中，有时会涉及到两比较组采用配对设计获得的二项反应数据（配对二项数据）的等效性／非劣效性问题。两独立组率之间等效性／非劣效试验的样本含量估计及假设检验方法已较为成熟，但对于配对二项数据两组率之间的等效性／非劣效性试验的样本含量估计及假设检验方法还应用不多。本文介绍了一种渐进的基于约束极大似然估计的方法用于配对二项数据两组率之间的等效性／非劣效性试验的样本含量估计和假设检验，借助一个超声诊断仪临床试验的例子阐明了本方法的应用，还就有关实际问题进行了讨论。     

均匀分布资料总体中位数可信区间估计Bootstrap法样本含量的设置

探讨了Bootstrap样本含量n*对Bootstrap法总体中位数可信区间估计效果的影响。首先模拟从均匀分布总体中随机抽样;然后用Bootstrap法进行总体中位数可信区间估计,重复1000次,得到1000个可信区间,统计1000个可信区间包含总体中位数的正确率。结果表明,Bootstrap样本含量n*对总体中位数可信区间估计的正确率影响很大,Bootstrap样本含量n*越小,正确率越高;Bootstrap样本含量n*越大,正确率越低;Bootstrap样本含量n*不能任意设置,当Bootstrap样本含量n*=n-3时,效果最好。     

处方评价工作存在问题探讨

目的：探讨处方点评制度中存在的问题,落实《处方管理办法》。方法：分析本院开展处方评价工作的情况,就处方指标、抽样方法及评价标准三个方面提出意见和建议。结果：处方评价指标应作适当调整,增加住院用药调研指标,并推广“人均”概念;不同的处方抽样方法和样本量对统计结果有影响,采用分层按比例抽样,再按总体概率最低值计算样本量较为合理;处方评价缺乏科学合理的比较标准,应统一制订国家标准。结论：处方评价工作还需进一步提高科学性和可操作性。     

肿瘤药物临床试验中成组序贯设计的统计学考虑

成组序贯设计因其拥有较少的病例样本数和较早终止试验的可能性成为肿瘤药物临床试验设计方法的较好选择。如何科学有效地设计和应用成组序贯设计,本文通过Monte Carlo试验模拟,探讨肿瘤药物临床试验中成组序贯设计的期中分析次数、实施时间以及α消耗函数选取等问题,为读者系统指明如何去规划一次成组序贯试验以及如何确定其最优的试验参数。模拟结果表明,成组序贯设计以时间点2∶1∶1折半划分的三次期中分析为好,其期望样本含量仅为420.53。Lan-Demets的五种α消耗函数中,1.5次幂和2次幂的α消耗函数拥有最小期望样本含量约393例,相对于O＇Brien-Fleming设计和Po-cock设计在整体上更显优势。     

Sample Size Calculations for Population Pharmacodynamic Experiments Involving Repeated Dichotomous Observations

Analysis of repeated binary measurements presents a challenge in terms of the correlation between measurements within an individual and a mixed-effects modelling approach has been used for the analysis of such data. Sample size calculation is an important part of clinical trial design and it is often based on the method of analysis. We present a method for calculating the sample size for repeated binary pharmacodynamic measurements based on analysis by mixed-effects modelling and using a logit transformation. Wald test is used for hypothesis testing. The method can be used to calculate the sample size required for detecting parameter differences between subpopulations. Extensions to account for unequal allocation of subjects across groups and unbalanced sampling designs between and within groups were also derived. The proposed method has been assessed via simulation of a linear model and estimation using NONMEM. The results showed good agreement between nominal power and power estimated from the NONMEM simulations. The results also showed that sample size increases with increased variability at a rate that depends on the difference in parameter estimates between groups, and designs that involve sampling based on an optimal design can help to reduce cost.     

Sample size calculations for population pharmacodynamic experiments involving repeated dichotomous observations

Analysis of repeated binary measurements presents a challenge in terms of the correlation between measurements within an individual and a mixed-effects modelling approach has been used for the analysis of such data. Sample size calculation is an important part of clinical trial design and it is often based on the method of analysis. We present a method for calculating the sample size for repeated binary pharmacodynamic measurements based on analysis by mixed-effects modelling and using a logit transformation. Wald test is used for hypothesis testing. The method can be used to calculate the sample size required for detecting parameter differences between subpopulations. Extensions to account for unequal allocation of subjects across groups and unbalanced sampling designs between and within groups were also derived. The proposed method has been assessed via simulation of a linear model and estimation using NONMEM. The results showed good agreement between nominal power and power estimated from the NONMEM simulations. The results also showed that sample size increases with increased variability at a rate that depends on the difference in parameter estimates between groups, and designs that involve sampling based on an optimal design can help to reduce cost.     

Rapid Sample Size Calculations for a Defined Likelihood Ratio Test-Based Power in Mixed-Effects Models

Efficient power calculation methods have previously been suggested for Wald test-based inference in mixed-effects models but the only available alternative for Likelihood ratio test-based hypothesis testing has been to perform computer-intensive multiple simulations and re-estimations. The proposed Monte Carlo Mapped Power (MCMP) method is based on the use of the difference in individual objective function values (ΔiOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The ΔiOFV is sampled and summed (∑ΔiOFVs) for each study at each sample size of interest to study, and the percentage of ∑ΔiOFVs greater than the significance criterion is taken as the power. The power versus sample size relationship established via the MCMP method was compared to traditional assessment of model-based power for six different pharmacokinetic and pharmacodynamic models and designs. In each case, 1,000 simulated datasets were analysed with the full and reduced models. There was concordance in power between the traditional and MCMP methods such that for 90% power, the difference in required sample size was in most investigated cases less than 10%. The MCMP method was able to provide relevant power information for a representative pharmacometric model at less than 1% of the run-time of an SSE. The suggested MCMP method provides a fast and accurate prediction of the power and sample size relationship.     

计量资料非劣效临床试验样本量及把握度的计算与SAS程序实现

目的：利用SAS简易快速对计量资料非劣效临床试验进行样本量及把握度的计算方法：比较公式及SAS编程两种计算方法,同时给出反推把握度的SAS宏程序。结果：公式和SAS程序两种计算方法的结果一致,利用SAS程序可以直接给出结果,无需要再进行查表获得相关参数,更简单、快捷。结论：利用本文提供的程序可以更好的帮助研究者理解与运用此程序进行样本量和把握度的估算,为此类新药临床试验服务。     

HPLC法测定肠安康颗粒中白术内酯I与苍术酮的含量

目的：建立肠安康颗粒中白术内酯I与苍术酮的含量测定方法。方法：采用高效液相色谱法,色谱柱为伊利特ODS色谱柱（250mm×4．6mm,5μm）,流动相为乙腈-水（梯度洗脱）,流速为1mL·min^-1,检测波长为220nm。结果：白术内酯I进样量在0．1～2．01μg范围内与峰面积积分值呈良好线性关系（r=0．9999）,平均回收率为98．84％,RSD=0．76％（n=6）;苍术酮进样量在0．5-10．0μg范围内与峰面积积分值呈良好线性关系（r=0．9999）,平均回收率为98．43％,RSD=1．14％（n=6）。结论：本方法简便、准确,可用于肠安康颗粒的质量控制。     

Sample Size Determination in Two-Sided Distribution-Free Treatment Versus Control Multiple Comparisons

The problem of power and sample size determination for distribution-free multiple comparison tests of K treatments versus a control group is addressed. We define the power as the probability of correctly rejecting one specified or all K hypotheses, corresponding to the per-pair and all-pairs power, respectively. The power formulas are derived for both joint ranking and pairwise ranking mechanism for general multiple comparison problems, followed by explicit form of these formulas when the single-step, step-down, or step-up adjustments are applied. The proposed power and sample size calculation methods apply to scenarios both when the underlying distributions are known and when they are unknown but a pilot study is available. Numerical methods via quasi-Monte Carlo integration and Monte Carlo integration are assessed. Our simulation studies show the accuracy of the power and sample size calculation formulas. We recommend the Monte Carlo integration as the calculation algorithm. An example from a mouse peritoneal cavity study is used to demonstrate the application of the methods.     

Probability monitoring procedures for sample size determination

ABSTRACT

In clinical research, power analysis is often performed for sample size calculation. The purpose is to achieve a desired power of correctly detecting a clinically meaningful difference at a pre-specified level of significance if such a difference truly exists. However, in some situations such as (i) clinical trials with extremely low incidence rates and (ii) for rare disease drug development clinical trials, power analysis for sample size calculation may not be feasible because (i) it may require a huge sample size for detecting a relatively small difference and (ii) eligible patients may not be available for a small target patient population. In these cases, other procedures for sample size determination with certain statistical assurance are needed. In this article, an innovative method based on a probability monitoring procedure is proposed for sample size determination. The concept is to select an appropriate sample size for controlling the probability of crossing safety and/or efficacy boundaries. For rare disease clinical development, an adaptive probability monitoring procedure may be applied if a multiple-stage adaptive trial design is used.     

肠安颗粒的质量标准研究

目的 建立肠安颗粒剂的质量控制方法。方法采用薄层色谱法对方中白术、延胡索、五味子进行定性研究，采用高效液相色谱法，以乙腈-水（17：83）为流动相，检测波长为230nm，采用Kromasil 100-5 C18分析柱（200mm×4．6mm，5 μm），测定方中白芍中芍药苷的含量。结果 白术、延胡索、五味子可用薄层色谱鉴别；芍药苷在0．092 8～1．856 μg线性关系良好（r＝0．9999），平均回收率为101．810，RSD＝1．31％（n＝6）。结论 所建立的方法简便、快速、准确，能有效控制肠安颗粒剂的质量。