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。     

单因素重复测量设计样本含量的估算及不同计算方法之间的比较

目的:介绍并比较几种重复测量设计样本含量的估算方法。方法:通过实例分析,分别采用PASS 11,stata统计学软件以及相关的计算公式计算其所需样本含量。结果:对同一案例,PASS 11软件中Compound Symmetry法需27例,AR法需19例,Banded法需12例,Simple法需6例;Stata软件中post法需27例,change法需13例,ancova法需10例;公式计算结果为14例。结论:PASS 11,stata统计学软件可以方便地用来估计重复测量设计的样本含量;正确分析研究设计性质并针对性地设置软件中对应参数是获得合适估计结果的关键。     

如何把握实验设计的随机原则

随机、对照、重复和均衡是实验设计的4个基本原则。随机原则使样本具有极好的代表性,使各组受试对象在重要的非实验因素方面具有极好的均衡性,提高组间实验资料的可比性。     

浅谈样本含量估计

估计样本含量的目的，是在保证一定精确度的前提下，确定最少的观察单位数，为此必须在保证实验结果具有一定可靠性的条件下确定最少的样本含量以节约人力和经费，所以在实验设计中，要对样本的大小作出科学估计以满足数据处理的要求。     

血清标本溶血对大鼠血液生化指标的影响

在大鼠长期毒性试验中,血液生化指标的分析对结果的判断具有重要的意义。但长期毒性试验中大鼠的血液生化分析具有其特殊性,例如采血是不可重复的,血液生化测定结果样本的大小(n)有一定的限制等。当由于某种原因导致血液标本溶血时,溶血样本的测定结果能否参与统计处理是实验工     

定量实验中样本含量的设计与数据处理

直接测量量的样本含量的设计是定量性实验设计的重要环节,在给定间接测量量的误差限要求以后,通过对直接测量量的初测、误差性质的判断、偶然误差限的计算,即可得出样本含量的大小,再据此对直接测量量进行实际的测量、计算处理和误差分析,最终得出实验结论。     

多阶段抽样中样本含量的最佳配置及应用

本文以四阶段抽样为例给出一种多阶段抽样中各阶段样本含量的最佳配置方法,使其在实验成本一定的条件下估计误差最小;或在满足一定误差的条件下实验成本达到最低。     

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。     

去垢剂对单向免疫扩散法测定流感病毒疫苗血凝素含量的影响

目的研究单向免疫扩散法测定流感病毒亚单位疫苗不同生产阶段中间产品的血凝素含量实验中,去垢剂对检测结果的影响,寻找最佳裂解条件。方法以不同的去垢剂浓度和不同裂解时间分别对完整病毒和亚单位疫苗样本进行处理后,利用单向免疫扩散实验测定样本的血凝素含量,并对测定结果进行比较和分析。结果亚单位疫苗的单向免疫扩散沉淀圈在去垢剂Zwittergent浓度小于0．125％时随去垢剂浓度的增加而增大,当去垢剂浓度达到0．125％后不再继续增大：而完整病毒的沉淀圈在去垢剂浓度达到1％后才不再增大。以终浓度1％的去垢剂对完整病毒和亚单位疫苗分别处理30、60、120min后进行单向免疫扩散,测定结果比较差异无统计学意义。结论以终浓度1％的去垢剂Zwittergent对完整病毒或亚单位疫苗样本裂解30min后进行单向免疫扩散测定血凝素含量,均可得到稳定准确的结果,有利于提高工作效率、缩小实验间误差、节约实验成本。     

肠膜明串珠菌发酵草鱼骨酶解液工艺条件的优化

目的采用乳酸菌发酵的方法对鱼骨中的钙进行转化,并优化出最优发酵工艺。方法采用泡菜汤汁中分离出的肠膜明串珠菌对草鱼骨风味蛋白酶酶解液进行发酵,以发酵液游离钙含量为筛选指标,对鱼骨含量、碳源种类、碳源含量、发酵起始pH、不同无机盐种类进行单因素实验,初步确定了各因素最优水平,在鱼骨含量、碳源含量、发酵起始pH单因素结果为零水平的基础上,零水平重复实验3次,进行三因素三水平响应面实验。结果与结论响应面实验确定肠膜明串珠菌发酵草鱼骨酶解液的最优工艺条件为鱼骨含量81.25mg/mL,碳源含量7mg/mL,发酵初始pH=5.75,预测发酵液中游离钙含量为5.56mg/g鱼骨,在优化出的条件下进行验证实验,得到的游离钙含量为5.57±0.13mg/g鱼骨。     

Sample Size Calculation for Clustered Binary Data with Sign Tests Using Different Weighting Schemes

We propose a sample size calculation approach for testing a proportion using the weighted sign test when binary observations are dependent within a cluster. Sample size formulas are derived with nonparametric methods using three weighting schemes: equal weights to observations, equal weights to clusters, and optimal weights that minimize the variance of the estimator. Sample size formulas are derived incorporating intracluster correlation and the variability in cluster sizes. Simulation studies are conducted to evaluate a finite sample performance of the proposed sample size formulas. Empirical powers are generally close to nominal levels. The number of clusters required increases as the imbalance in cluster size increases and the intracluster correlation increases. The estimator using optimal weights yields the smallest sample size estimate among three estimators. For small values of intracluster correlation the sample size estimates derived from the optimal weight estimator are close to that derived from the estimator assigning equal weights to observations. For large values of intracluster correlation, the optimal weight sample size estimate is close to the sample size estimate assigning equal weights to clusters.     

Sample size calculations for comparing two groups of count data

A sample size formula for comparing two groups of count data is derived using the method of moments by matching the first and second moments of the distribution of the count data, and it does not need any further distributional assumption. Compared to sample size formulas derived using a likelihood-based approach or using simulations, the proposed sample size formula applies to count data following any distribution in addition to the negative binomial distribution. The proposed sample size formula can be used even when the study is analyzed with a likelihood-based approach. Because asymptotically, the method of moments is no more efficient than likelihood-based approaches, the proposed sample size formula can be viewed as an upper bound of the required sample size by likelihood-based approaches to start the study. Applications of the sample size formula are illustrated using an asthma study design.     

Adaptive statistical analysis following sample size modification based on interim review of effect size

In designing a comparative clinical trial, the required sample size is a function of the effect size, the value of which is unknown and at best may be estimated from historical data. Insufficiency in sample size as a result of overestimating the effect size can be destructive to the success of the clinical trial. Sample size re-estimation may need to be properly considered as a part of clinical trial planning. This paper is intended to give the motivations for the sample size re-estimation based partly on the effect size observed at an interim analysis and for a resulting simple adaptive test strategy. The performance of this adaptive design strategy is assessed by comparing it with a fixed maximum sample size design that is properly adjusted in anticipation of the possible sample size adjustment.     

Adaptive Statistical Analysis Following Sample Size Modification Based on Interim Review of Effect Size

ABSTRACT

In designing a comparative clinical trial, the required sample size is a function of the effect size, the value of which is unknown and at best may be estimated from historical data. Insufficiency in sample size as a result of overestimating the effect size can be destructive to the success of the clinical trial. Sample size re-estimation may need to be properly considered as a part of clinical trial planning. This paper is intended to give the motivations for the sample size re-estimation based partly on the effect size observed at an interim analysis and for a resulting simple adaptive test strategy. The performance of this adaptive design strategy is assessed by comparing it with a fixed maximum sample size design that is properly adjusted in anticipation of the possible sample size adjustment.     

Adjustment for unbalanced sample size for analytical biosimilar equivalence assessment

ABSTRACT

Large sample size imbalance is not uncommon in the biosimilar development. At the beginning of a product development, sample sizes of a biosimilar and a reference product may be limited. Thus, a sample size calculation may not be feasible. During the development stage, more batches of reference products may be added at a later stage to have a more reliable estimate of the reference variability. On the other hand, we also need a sufficient number of biosimilar batches in order to have a better understanding of the product. Those challenges lead to a potential sample size imbalance. In this paper, we show that large sample size imbalance may increase the power of the equivalence test in an unfavorable way, giving higher power for less similar products when the sample size of biosimilar is much smaller than that of the reference product. Thus, it is necessary to make some sample size imbalance adjustments to motivate sufficient sample size for biosimilar as well. This paper discusses two adjustment methods for the equivalence test in analytical biosimilarity studies. Please keep in mind that sufficient sample sizes for both biosimilar and reference products (if feasible) are desired during the planning stage.     

An evaluation of increasing sample size based on conditional power

We evaluate properties of sample size re-estimation (SSR) designs similar to the promising zone design considered by Mehta and Pocock (2011). We evaluate these designs under the assumption of a true effect size of 1.1 down to 0.4 of the protocol-specified effect size by six measures: 1. The probability of a sample size increase, 2. The mean proportional increase in sample size given an increase; 3 and 4. The mean true conditional power with and without a sample size increase; 5 and 6. The expected increase in sample size and power due to the SSR procedure. These measures show the probability of a sample size increase and the cost/benefit for given true effect sizes, particularly when the SSR may either be pursuing a small effect size of little clinical importance or be unnecessary when the true effect size is close to the protocol-specified effect size. The results show the clear superiority of conducting the SSR late in the study and the inefficiency of a mid-study SSR. The results indicate that waiting until late in the study for the SSR yields a smaller, better targeted set of studies with a greater increase in overall power than a mid-study SSR.     

Sample Size Computations for PK/PD Population Models

We describe an accurate, yet simple and fast sample size computation method for hypothesis testing in population PK/PD studies. We use a first order approximation to the nonlinear mixed effects model and chi-square distributed Wald statistic to compute the minimum sample size to achieve given degree of power in rejecting a null hypothesis in population PK/PD studies. The method is an extension of Rochon’s sample size computation method for repeated measurement experiments. We compute sample sizes for PK and PK/PD models with different conditions, and use Monte Carlo simulation to show that the computed sample size retrieves the required power. We also show the effect of different sampling strategies, such as minimal, i.e., as many observations per individual as parameters in the model, and intensive on sample size. The proposed sample size computation method can produce estimates of minimum sample size to achieve the desired power in hypothesis testing in a greatly reduced time than currently available simulation-based methods. The method is rapid and efficient for sample size computation in population PK/PD study using nonlinear mixed effect models. The method is general and can accommodate any type of hierarchical models. Simulation results suggest that intensive sampling allows the reduction of the number of patients enrolled in a clinical study.     

Bayesian sample size determination for longitudinal studies with continuous response based on different scientific questions of interest

Longitudinal study designs are commonly applied in much scientific research, especially in the medical, social, and economic sciences. Longitudinal studies allow researchers to measure changes in each individual’s responses over time and often have higher statistical power than cross-sectional studies. Choosing an appropriate sample size is a crucial step in a successful study. In longitudinal studies, because of the complexity of their design, including the selection of the number of individuals and the number of repeated measurements, sample size determination is less studied. This paper uses a simulation-based method to determine the sample size from a Bayesian perspective. For this purpose, several Bayesian criteria for sample size determination are used, of which the most important one is the Bayesian power criterion. We determine the sample size of a longitudinal study based on the scientific question of interest, by the choice of an appropriate model. Most of the methods of determining sample size are based on the definition of a single hypothesis. In this paper, in addition to using this method, we determine the sample size using multiple hypotheses. Using several examples, the proposed Bayesian methods are illustrated and discussed.     

Not Too Big,Not Too Small: A Goldilocks Approach To Sample Size Selection

We present a Bayesian adaptive design for a confirmatory trial to select a trial’s sample size based on accumulating data. During accrual, frequent sample size selection analyses are made and predictive probabilities are used to determine whether the current sample size is sufficient or whether continuing accrual would be futile. The algorithm explicitly accounts for complete follow-up of all patients before the primary analysis is conducted. We refer to this as a Goldilocks trial design, as it is constantly asking the question, “Is the sample size too big, too small, or just right?” We describe the adaptive sample size algorithm, describe how the design parameters should be chosen, and show examples for dichotomous and time-to-event endpoints.     

Nonparametric Sample Size Estimation for Sensitivity and Specificity with Multiple Observations per Subject

We propose a sample size calculation approach for the estimation of sensitivity and specificity of diagnostic tests with multiple observations per subjects. Many diagnostic tests such as diagnostic imaging or periodontal tests are characterized by the presence of multiple observations for each subject. The number of observations frequently varies among subjects in diagnostic imaging experiments or periodontal studies. Nonparametric statistical methods for the analysis of clustered binary data have been recently developed by various authors. In this paper, we derive a sample size formula for sensitivity and specificity of diagnostic tests using the sign test while accounting for multiple observations per subjects. Application of the sample size formula for the design of a diagnostic test is discussed. Since the sample size formula is based on large sample theory, simulation studies are conducted to evaluate the finite sample performance of the proposed method. We compare the performance of the proposed sample size formula with that of the parametric sample size formula that assigns equal weight to each observation. Simulation studies show that the proposed sample size formula generally yields empirical powers closer to the nominal level than the parametric method. Simulation studies also show that the number of subjects required increases as the variability in the number of observations per subject increases and the intracluster correlation increases.