治疗—对照差的临床意义及其判别方法

目的　说明治疗—对照差的临床意义及其判别方法 ,为此提出一种非虚假设检验。方法　以同源有效样本量取代样本量 ,将用于比较两率差和最小临床承认疗效差量的经典非虚假设检验加以扩展以覆盖终检。结果　这种检验可用于实现这种判别。在无终检时 ,它还原为经典检验。结论　与通常的药物疗效评价方法相比 ,这显然提高了一步。     

治疗一对照差的临床意义及其判别方法

目的 说明治疗－对照差的临床意义及其判别方法,为此提出一种非虚假设检验。方法 以同源有效样本量取代样本量,将用于比较两率差和最小临床承认效差量的经典非假设检验加以扩展以覆盖终检。结果 这种检验可用于实现这种判别,在无终检时,它还原为经典检验,结论 与通常的药物疗效评价方法相比,这显著提高了一步。     

治疗-对照差临床意义判别所需样本量的测定

目的 提出用于治疗一对照差临床意义判别的样本量测定方法。方法 依据治疗一对照差与最小临床承认疗效差量的比较推导出所需样本量测定公式,以Monte Carlo方法展示其行为。结果 当最小临床承认疗效差量取值为零时,本文方法还原为渐近正态法。由所测样本量产生的观测功效与预定功效吻合。结论 对于以判别治疗一对照差临床意义为目的的临床试验,可用本文方法测定所需样本量。     

多中心两组临床试验所需样本量的测定

目的：提出用于多中心两组临床试验的样本量测定方法。方法：由Cochran检验或Mantel-Haenszel检验反推出所需总样本量，按层样本分数和组样本分数进行样本量的分配。结果：如此获得的样本量测定方法与检验方法一一匹配。该方法具有同质性，当只有一层时还原为同质假设下的简化正态法或同质假设下的简化正态法加1。针对Cochran检验的设计可为等层或非等层设计，而针对Mantel-Haenszel检验的只能是等层设计。结论：该方法可用于多中心两组临床试验方案的设计。附有工作实例描述设计过程。     

检验Cox模型成比例危险性假设的探讨

目的：探讨如何对Cox模型成比例危险性假设进行检验,以及协变量与危险函数之间非成比例危险性的解决方法。方法：以Ⅲc期卵巢浆液性囊腺癌数据为例,用图形法对影响Ⅲc期卵巢浆液怀囊腺癌生存时间的预后因素。做了成比例危险性假设的检验。结果：术前一般状态这一 后因素违背了成比例危险性假设。结论：在应用Cox模型时,检验预后因素是否违背成比例危险性假设应当引起重视。     

不同方法制备金银花水提液对大鼠免疫功能影响的研究

目的探讨不同方法制备的金银花水提液对大鼠免疫功能的影响,为金银花的临床应用提供理论依据。方法分别采用水煎法和浸泡法制备金银花水提液,大鼠灌胃2周后,分别检测其巨噬细胞吞噬功能、淋巴细胞转化能力及TH1细胞分泌的IL-2、TNF-α、IFN-γ的mRNA表达情况。结果两种方法制备的金银花水提液均能改善机体的非特异性免疫功能,但只有水煎法制备的提取液能改善机体的细胞免疫功能。结论用水煎法制备金银花提取液改善机体免疫功能的功效优于浸泡法。     

寒凉类外用中药的性味功效研究

目的:研究分析寒凉类外用中药的性味功效,为临床外用提供相关依据.方法:查阅《中药学》教材,以临床确有的寒凉类外用中药报道为基础,选取有记载的202味进行研究,研究内容包括四气、五味、用法与功效等.结果:202味寒凉类外用中药中主要为性寒药物(占57.9％),其次为性微寒药物(占28.2％);以味苦、味甘为主,分别占51.0％、292％;大部分属于无毒类(占87.6％),大毒占1.5％,微毒占4.4％,归胃经占3.5％,其中归肝经比例最高(34.2％),其次为归肺经(26.7％);其中清热解毒中药最多(占40.6％);外用常用方法为外敷法＞外洗法＞烟熏法.结论:寒凉类外用中药以味苦、甘为主,具有止痛、清热凉血、祛湿、消肿、杀虫等功效,临床上主要用于跌打损伤、皮炎、延后中通、痔疮、舌虫咬伤、牙痛等疾病的治疗.     

非整支药物换算在静脉用药调配中心儿科药物调配中的应用

目的 研究在静脉用药调配中心儿科药物调配中,应用非整支药物换算方法辅助的临床作用及价值.方法 选择2019年1—6月30名药师/护士调配的940份药物医嘱单,对照组采用约分法进行非整支药物换算,观察组采用倍数法进行非整支药物换算,每组各470份.汇总统计研究采集的医嘱单,进行分类整理;同时对比不同换算方法的应用效率(药...     

样本量估计及其在nQuery和SAS软件上的实现——相关分析

4.相关分析 4.1.单样本相关性分析 4.1.1.差异性检验 4.1.1.1.kappa系数检验(二分类变量) 方法:Donner和Eliasziw(1992)[1]给出的单样本二分类变量kappa系数双侧检验的样本量估计方法,是建立在自由度为1,非中心参数为λ(1,1-β,α)的非中心x2分布上的,其样本量的计算公式为:n=λ (1,1-β,α){[π(1-π)(κ1-κ0)]2/π2+π(1-π)k0+2[π(1-π)(κ1-κ0)] 2/π(1-π)(1-k0)+[π(1-π)(k1-k0)]2/(1-π)2+π(1-π)k0}(4-1)式中,π为研究对象被判为阳性的概率,κ0为原假设kappa系数,κ1为备择假设kappa系数.在自由度为1的情况下,非中心参数λ(1,1-β,α)近似等于(Z1-α/2+Z1-β)2,在计算样本量时,将其代入(4-1)进行计算.     

中药炮制中大黄不同炮制品的功效研究

目的 探讨中药炮制中大黄的不同炮制品对功效的影响,为大黄不同炮豺品的临床使用提供依据.方法 以小鼠为实验对象进行药效学实验,观察大黄不同炮制品水提取液泻下和止血作用的差异.结果 大黄不同炮制品中,泻下作用以生大黄最强,熟大黄、酒大黄次之,大黄炭无泻下作用,有止泻作用;止血作用以大黄炭最强,生大黄次之,熟大黄、酒大黄无明显的止血作用.结论 大黄不同炮制品所舍的有效成分不同,使其药理作用显著不同.在临床用药时应针对不同的病证选择合适的炮制品种,使大黄在临床使用中发挥良好的功效.     

Tests of non-null hypothesis on proportions for stratified data

It is more reasonable to interpret the efficacy of therapies under non-null hypothesis, hence tests of non-null hypothesis have been carried out. Most clinical trials adopt multi-center campaign and yield stratified data. However, the existing non-null hypothesis tests are not suitable for stratified data. This paper proposes the tests of non-null hypothesis on proportions for stratified data. Averaging the treatment-control difference in each stratum yields the mean treatment-control difference. Comparing its expectation with the minimal detectable difference leads to set up a non-null hypothesis. Its variance is used to construct the equation of the basic relationship for stratified designs under the non-null hypothesis. Then follow the derivations for the one- and two-sample tests. Their performance is demonstrated by the Monte Carlo method. As far as the two-sample tests are concerned, they reduce to the Cochran test and the Mantel-Haenszel test, on setting the minimal detectable difference equal to zero, and to the Dunnett-Gent test when there is only one stratum. As for the one-sample test, it also reduces to its classical counterparts in these situations. The observed power coincides with the prescribed power and the relevant operating characteristic curves. The tests can be applied to the active control clinical trials with multi-center or stratified designs for establishing the clinical superiority or non-inferiority of a tested drug versus control. Worked examples illustrate the methodology.     

Asymptotic power calculations: description, examples, computer code.

This paper describes two asymptotic methods for sample size and power calculation for hypothesis testing. Both methods assume that the distribution of the likelihood ratio is approximately distributed as a central chi(2) distribution under the null hypothesis and as a non-central chi(2) under the alternative hypothesis. The approximation to the non-centrality parameter differs between the methods. It is shown how these methods can be automatically extended from constraints setting parameters to constant values to constraints positing equality of parameters. Two very simple examples are presented; one demonstrates that the information method can produce arbitrarily incorrect results. Four more comprehensive examples are then discussed. In addition to demonstrating the wide range of applicability of these methods, the examples illustrate techniques that may be used in cases in which there is insufficient initial information available to perform a realistic calculation. The availability of a computer implementation of these methods in S-plus is announced, as are routines for computing the cumulative distribution function of the non-central chi(2) and its inverse.     

Accurate and efficient power calculations for 2 x m tables in unmatched case-control designs

The aim of this article was to derive an approximation of the distribution of Pearson's statistic for 2 x m contingency tables to calculate power for case-control studies accurately and efficiently in terms of computer time. We first prove that, rather than a non-central chi-square distribution, Pearson's statistic is asymptotically equivalent with the sum of squares of m independent normal random variables whose variances are typically different under the alternative hypothesis. Based on this asymptotically equivalent (AE) we derive a cost-effective approximation (CE). Numerical results show that CE was almost as precise as AE, but computationally more efficient. Although somewhat less costly in terms of CPU time, we show that a commonly used approximation using the non-central chi-square distribution can be very inaccurate and overestimate power in some scenarios and underestimate it in others. Simulations and exact distributions, on the other hand, are accurate but computationally very intensive compared to our CE. The CE reached its lowest accuracy under the null hypothesis where it has a chi-square distribution with m - 1 degree of freedom. This suggests that CE can be used to calculate power for tables where researchers would feel comfortable using Pearson's chi-square test.     

Bayesian hypothesis testing-use in interpretation of measurements

Bayesian hypothesis testing may be used to qualitatively interpret a dataset as indicating something "detected" or not. Hypothesis testing is shown to be equivalent to testing the posterior distribution for positive true amounts by redefining the prior to be a mixture of the original prior and a delta-function component at 0 representing the null hypothesis that nothing is truly present. The hypothesis-testing interpretation of the data is based on the posterior probability of the usual modeling hypothesis relative to the null hypothesis. Real numerical examples are given and discussed, including the distribution of the non-null hypothesis probability over 4,000 internal dosimetry cases. Currently used comparable methods based on classical statistics are discussed.     

The summarizing of clinical experiments by significance levels

For a controlled clinical experiment in which two alternative treatments are compared, the statistical report often culminates in a significance test of the null hypothesis of no difference between the treatments, and significance at the 5 per cent level is taken as positive evidence of difference. It is argued that such an experiment serves primarily an inferential purpose; it is not a simple decision procedure, although its effect on practice may be considered in relation to ethical issues. Statistical inference should not be identified with testing this null hypothesis, despite the emphasis on such tests by R. A. Fisher in his work on design of experiments. This null hypothesis often has no interest or credibility.     

Restricted mean survival time for interval-censored data

Restricted mean survival time (RMST) evaluates the mean event-free survival time up to a prespecified time point. It has been used as an alternative measure of treatment effect owing to its model-free structure and clinically meaningful interpretation of treatment benefit for right-censored data. In clinical trials, another type of censoring called interval censoring may occur if subjects are examined at several discrete time points and the survival time falls into an interval rather than being exactly observed. The missingness of exact observations under interval-censored cases makes the nonparametric measure of treatment effect more challenging. Employing the linear smoothing technique to overcome the ambiguity, we propose a new model-free measure for the interval-censored RMST. As an alternative to the commonly used log-rank test, we further construct a hypothesis testing procedure to assess the survival difference between two groups. Simulation studies show that the bias of our proposed interval-censored RMST estimator is negligible and the testing procedure delivers promising performance in detecting between-group difference with regard to size and power under various configurations of survival curves. The proposed method is illustrated by reanalyzing two real datasets containing interval-censored observations.     

A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion

The 'textbook' approach to determining sample size in a clinical trial has some fundamental weaknesses which we discuss. We describe a new predictive method which takes account of prior clinical opinion about the treatment difference. The method adopts the point of clinical equivalence (determined by interviewing the clinical participants) as the null hypothesis. Decision rules at the end of the study are based on whether the interval estimate of the treatment difference (classical or Bayesian) includes the null hypothesis. The prior distribution is used to predict the probabilities of making the decisions to use one or other treatment or to reserve final judgement. It is recommended that sample size be chosen to control the predicted probability of the last of these decisions. An example is given from a multi-centre trial of superficial bladder cancer.     

TESTING FOR INDIVIDUAL AND POPULATION EQUIVALENCE BASED ON THE PROPORTION OF SIMILAR RESPONSES

This paper focuses on the classical problem of comparison of treatment effects. We show that we can base a simple and intuitive approach to comparison of two treatments on the proportion of similar responses. This approach is equivalent to the standard comparison of the treatment means in the normal case with equal known variances, but is quite different in other cases. Our approach applies under two different settings: testing a null hypothesis of no treatment difference against an alternative hypothesis of a difference, and testing the null hypothesis of at least a specific difference against an alternative hypothesis of equivalence. We develop our approach both for parallel groups (independent samples), and cross-over (paired samples) studies. The two situations give rise to the known concepts of population and individual equivalence. We present a graphical procedure to supplement the method.