五种最小不平衡指数法的正确预测率比较

Taves[1]、Pocock & Simon[2]提出的最小不平衡指数法具有较好的组间人数和变量平衡能力,在优度法(GM)、动态上限法(UM)、信号法(SM)、极差法(RM)和方差法(VM)中,优度法的平衡能力最强[3],但无论哪一种最小不平衡指数法本质上都是决定性分组方法,容易预测到下一个研究对象的分组去向.正确预测率有多大,上述五种最小不平衡指数法中哪一种的正确预测率最小尚无报道.本研究拟采用计算机模拟方法来探讨这一问题.     

REML法和Bayesian法对小样本不平衡单因素随机效应模型方差成分估计的模拟比较分析

目的 比较限制性极大似然估计(REML)法和贝叶斯法(Bayesian)对小样本不平衡单因素随机效应模型方差成分估计的偏差和精密度,同时考虑在样本量的大小、单位的数量和单位内相关系数(ICC)的大小不同的情况下对方差成分估计的精确程度的影响.方法 通过计算机模拟7组不同设计的数据集,用SAS软件MIXED模块进行方差成分估计.结果 不同的设计中,REML法估计比Bayesian法估计更加接近真值,但Bayesian法对组间方差的区间估计更加精密.对于两种方法 而言,样本和单位数量的增加,估计结果 更加准确.组内方差的估计,比组间方差的估计更准确和精密.结论 对小样本不平衡结构数据,当ICC为小或中等时,REML估计比Bayesian估计的偏差和均方误差要小,推荐使用.但是Bayesian法的区间估计比REML法的区间估计更加精密.     

综合平衡训练(基于体感互动)改善脑卒中偏瘫患者平衡能力的效果

目的探讨综合平衡训练(基于体感互动)改善脑卒中偏瘫患者平衡能力的效果。方法选取医院2018年2月至2019年3月诊治的80例脑卒中偏瘫患者,以随机数字表法分组,每组40例。两组均采取相同的康复治疗方案,对照组在该基础上采取传统平衡训练,试验组则采取基于体感互动综合平衡训练,比较两组治疗前后Berg平衡量表(BBS)、Fugl-Meyer运动功能量表(FMA)评分及康复效果。结果两组治疗前BBS、FMA评分均较低,差异无统计学意义(P>0.05);治疗后试验组BBS、FMA评分高于对照组(P<0.05);试验组康复优良率为95.0%,对照组为85.0%,差异有统计学意义(P<0.05)。结论针对脑卒中偏瘫患者开展综合平衡训练(基于体感互动)有利于改善其平衡能力和肢体运动功能,有助于促进患者康复。     

疾病诊断相关分组分类节点变量的选择及其分组结果的合理性评价

目的:通过确立科学的分类节点变量,建立符合陕西省的DGRs分组,为相关决策提供依据。方法:(1)用非参数检验Kruskal Wallis方法和多因素回归方法分析选取分组的分类节点变量;(2)用费用评价法、方差减少量和ROC曲线对分组结果的合理性进行检验和评价。结果:(1)筛选了年龄、性别、是否手术、伴随症、疾病危重度和是否主要手术6个因素作为分类节点变量进行分组;(2)费用评价法显示总的分组可以解释实际消耗费用的78.38%;方差减少量计算的结果是0.546 5,说明分组后的组间异质性较好;ROC曲线结果说明各组之间的费用存在差异。结论:选取的分类节点变量科学,分组结果合理。     

基于相对检测次数最小的突发疫情和公共卫生的检测分组方法

目的 建立一种相对检测次数最小的突发疫情和公共卫生的病原检测分组方法。方法 通过建立以相对检测次数的数学期望最小为目标函数的优化模型,进行最优的每组人数的求解方法。结果 模型计算表明,在检测指标结果判定为阳性是小概率的情况下,可降低相对检测次数,如在人群中指标异常概率为1%时,按照模型确定的小组人数为11,相对检测次数为19.56%,即可节约80.44%的检测工作量和检测成本。结论 所建立的分组检测方法可以减少检测时间,节约成本,为及时有效地采取有针对性的控制措施争取先机。     

多组间协变量均衡性评价方法的研究

目的构建用于评价三分组资料组间协变量均衡性的指标(简称FQ统计量);比较假设检验法、标准化差异法和FQ统计量这三种方法检验三分组资料组间协变量均衡性的能力。方法利用合并方差构建FQ统计量;采用有序多分类和无序多分类logistic回归计算各组研究个体的倾向性评分值;采用Monte Carlo模拟比较上述三种方法检验三分组资料组间协变量均衡性的能力。结果假设检验法检验三组间协变量均衡性的能力受样本量大小的影响,而标准化差异法和FQ统计量则不受样本量大小的影响。标准化差异法和FQ统计量检验三组间协变量均衡性的能力均高于假设检验法,且两者保持高度一致。当协变量的FQ统计量小于0.2时,认为协变量在三组间的分布达到均衡。结论标准化差异法与FQ统计量是有效的协变量均衡性检验方法,而FQ统计量的计算步骤较标准化差异法简便,因此更具有应用的优势。     

最小化随机分组方法介绍及其SAS实现

目的介绍最小化随机分组方法的基本原理及运算过程,编制专用的SAS宏程序。方法通过文献查阅,综述最小化法的基本原理及其运算过程。依照运算过程,结合模拟实例,编制专用的SAS宏程序,并给出模拟实例的分组结果。结果最小化法作为一种动态随机分组方法,依据已入组病例重要预后因素组间分布情况,动态确定新入组病例的分配概率,最大限度保障重要预后因素组间分布均衡。它根据因素不平衡函数、总体不平衡函数和最优分配概率三个参数确定病例的分组,实施过程相对繁复是限制其应用的一个重要方面。通过编制SAS宏程序,可提高最小化法的分组效率。结论最小化法适用于小样本、基线特征复杂的临床试验,SAS宏程序能简化实施过程,有利于最小化法的应用。     

平衡仪训练对帕金森病平衡及自理能力的影响

目的观察平衡仪训练对帕金森病(PD)患者平衡功能和自理生活能力的影响。方法选取2018年4月-2019年5月天津市环湖医院收治的56例PD患者,随机分为观察组和对照组,各28例。对照组给予传统平衡功能训练方法进行康复,观察组根据平衡仪内置的训练模块进行平衡功能训练。评定并比较治疗前后两组患者的稳定极限测试、Berg平衡量表(BBS)评分和改良Barthel指数量表(MBI)评分。结果治疗后,两组稳定极限、BBS评分及MBI评分明显高于治疗前;且观察组稳定极限、BBS评分及MBI评分高于对照组,差异均有统计学意义(P<0.05)。结论平衡仪训练较传统的平衡训练在改善PD患者的平衡功能和提高生活自理能力方面更具优越性,值得临床推广应用。     

国际标准统计学符号与词汇 ISO 3534～1977(E/F)

X,Y, 戈,y,F(x),G(大),f(x),g(x), 充 N 摊 w,R X 大 石(X) 02 O S1PrP(万),P:(E)x全『.2U,“,之U欠,随机变量(元),变量,总体中特征的观察值观察值(x值的)分布函数连续随机变量(x值)的概率密度函数组数总体容量或批量样本容最样本极差总体均数样本均数随机变量X的期望,在某些情况下,用二与拼表示期望随机变量或总体的方差随机变量或总体的标准差样本方差注一一符号护通常用以表示离均差平方和被n一1除得之商(其平方根用符号s表示)作为所取样本的总体方差的估计值样本标准差(见前面注)(总体中两随机变量间的)相关系数(样本中的)相关系…     

秧歌舞锻炼对中老年女性平衡能力影响的综合评定

目的探索秧歌舞锻炼与不同时间结合对中老年女性平衡能力的影响,评价秧歌舞锻炼在预防中老年女性摔倒中的作用。方法 2014年3—6月通过调查问卷,访谈法选出长期(2年以上)从事秧歌舞锻炼的中老女性32名作为长期规律秧歌舞锻炼组,无规律锻炼习惯中老年女性30名作为无规律锻炼组,两组受试对象在专业技术人员指导下每周4次(50 min/次)共12周秧歌舞训练,在训练前后用闭眼单足站立、强化Romberg的方法测试静态平衡能力;采用闭目原地踏步、平衡木上行走、起立-走的测试方法测试动态平衡能力。计量资料同组实验前后比较采用配对样本t检验,组间采用独立样本t检验,P0.05为差异有统计学意义。结果长期规律秧歌舞锻炼组闭目单足站立时间、强化Romberg征时间、起立-走测试时间分别为(12.76±0.12)、(13.40±0.74)、(6.15±0.62)s与对照组的(9.86±0.41)、(11.93±0.78)、(7.62±0.41)s比较差异均有统计学意义(均P0.05);平衡板行走时间、闭目原地踏步时间分别为(6.89±0.76)、(8.71±0.41)s与对照组的(8.11±0.54)(7.01±0.59)s比较差异均有显著统计学意义(均P0.05)。无规律锻炼组人员12周秧歌舞锻炼前后,平衡板行走、起立-走测试、闭目原地踏步三项动态平衡能力测试比较差异均有统计学意义(均P0.05)。结论中老年人长期参加秧歌舞锻炼可以提高机体的平衡能力,而对动态平衡的影响更为明显。     

The "best balance" allocation led to optimal balance in cluster-controlled trials

ObjectiveBalance of prognostic factors between treatment groups is desirable because it improves the accuracy, precision, and credibility of the results. In cluster-controlled trials, imbalance can easily occur by chance when the number of cluster is small. If all clusters are known at the start of the study, the “best balance” allocation method (BB) can be used to obtain optimal balance. This method will be compared with other allocation methods.Study Design and SettingWe carried out a simulation study to compare the balance obtained with BB, minimization, unrestricted randomization, and matching for four to 20 clusters and one to five categorical prognostic factors at cluster level.ResultsBB resulted in a better balance than randomization in 13–100% of the situations, in 0–61% for minimization, and in 0–88% for matching. The superior performance of BB increased as the number of clusters and/or the number of factors increased.ConclusionBB results in a better balance of prognostic factors than randomization, minimization, stratification, and matching in most situations. Furthermore, BB cannot result in a worse balance of prognostic factors than the other methods.     

Dynamic balancing randomization in controlled clinical trials

In the design of randomized clinical trials, balancing of treatment allocation across important prognostic factors (strata) improves the efficiency of the final comparisons. Whilst randomization methods exist which attempt to balance treatments across the strata (permuted blocks, minimization, biased coin), these approaches assign equal importance for all the strata. Dynamic balancing randomization (DBR) is a tree-based method proposed by Signorini et al. allowing different levels of imbalance in different strata which ensures a balance for each level of prognostic risk factors (conditional balance) whilst at the same time preserving randomness. We present a simple modification to the original approach to maintain a marginal balance over important strata and examine the properties of this modification. Two important measures of performance are used to provide comparisons between the approaches: a loss function, which can be interpreted as the squared norm of the imbalance vector, and a forcing index which conveys the degree of randomness. A comparison of DBR with minimization and a biased coin design is carried out by simulation on two simulated trials.     

Randomization by minimization for unbalanced treatment allocation

Minimization is a dynamic randomization technique that has been widely used in clinical trials for achieving a balance of prognostic factors across treatment groups, but most often it has been used in the setting of equal treatment allocations. Although unequal treatment allocation is frequently encountered in clinical trials, an appropriate minimization procedure for such trials has not been published. The purpose of this paper is to present novel strategies for applying minimization methodology to such clinical trials. Two minimization techniques are proposed and compared by probability calculation and simulation studies. In the first method, called naïve minimization, probability assignment is based on a simple modification of the original minimization algorithm, which does not account for unequal allocation ratios. In the second method, called biased‐coin minimization (BCM), probability assignment is based on allocation ratios and optimized to achieve an ‘unbiased’ target allocation ratio. The performance of the two methods is investigated in various trial settings including different number of treatments, prognostic factors and sample sizes. The relative merits of the different distance metrics are also explored. On the basis of the results, we conclude that BCM is the preferable method for randomization in clinical trials involving unequal treatment allocations. The choice of different distance metrics slightly affects the performance of the minimization and may be optimized according to the specific feature of trials. Copyright © 2009 John Wiley & Sons, Ltd.     

Statistical properties of minimal sufficient balance and minimization as methods for controlling baseline covariate imbalance at the design stage of sequential clinical trials

When the number of baseline covariates whose imbalance needs to be controlled in a sequential randomized controlled trial is large, minimization is the most commonly used method for randomizing treatment assignments. The lack of allocation randomness associated with the minimization method has been the source of controversy, and the need to reduce even minor imbalances inherent in the minimization method has been challenged. The minimal sufficient balance (MSB) method is an alternative to the minimization method. It prevents serious imbalance from a large number of covariates while maintaining a high level of allocation randomness. In this study, the two treatment allocation methods are compared with regards to the effectiveness of balancing covariates across treatment arms and allocation randomness in equal allocation clinical trials. The MSB method proves to be equal or superior in both respects. In addition, type I error rate is preserved in analyses for both balancing methods, when using a binary endpoint.     

Minimization--reducing predictability for multi-centre trials whilst retaining balance within centre

Minimization is often used to assign patients to treatment groups to ensure good balance in patient numbers within centre and other prognostic factors. Balance within centre is preferable since large imbalances between treatment arms may have logistical implications for centres, such as cost and resource implications. However, recent concern over high predictability of treatment allocation by centres when using minimization has caused this method to be questioned. We used data from current clinical trials to assess predictability and summarize subsequent within-centre imbalances with the aim of finding the most effective minimization method for reducing predictability whilst still retaining sufficient balance within centre, when randomization is to one of two treatments. We compared prediction rates and imbalances for deterministic minimization, and minimization incorporating various random elements, p (p=0.95,0.90,0.80,0.75,0.70). We also compared prediction rates and imbalance when centre was and was not included as a stratification factor. Incorporating a random element proved successful in reducing prediction rates whilst minimizing the inevitable increase in within-centre imbalance, whereas excluding centre as a stratification factor incurred major within-centre imbalance. We therefore suggest that minimization can still be used, and that centre can be included as a stratification factor, but a random element has to be incorporated into the minimization algorithm. Minimization incorporating a random element of 0.80 is the most efficient method to use based upon the simulations undertaken in this study of real clinical trial data using different probabilities of allocation.     

Comparison of stratification and adaptive methods for treatment allocation in an acute stroke clinical trial

Achieving balance on prognostic factors between treatment groups in a clinical trial is important to ensure that any observed treatment effect may be attributed to the treatment itself. Improving the balance on prognostic factors also potentially increases the statistical power attained in a trial. Substantial imbalances may occur by chance if simple randomization is used. Allocation of the treatment according to stratified random blocks based on clinical features is the conventional approach to obtain treatment groups that are as similar as possible. An alternative approach, known as minimization (or more generally as adaptive stratification), has also been proposed. We assessed the feasibility of adaptive stratification in the context of a clinical trial of insulin to control plasma glucose level following acute stroke. We determined suitable settings for the parameters in the adaptive stratification procedure by simulation studies. Specifically, we assessed: the optimal probability for allocating a patient to the preferred (leading to least imbalance on prognostic factors) treatment group; the number of variables that could be incorporated in the adaptive stratification algorithm; the weighting that should be given to each variable; and whether interactions between variables should be included. We then compared the statistical power, across a range of simulated treatment effects, between trials where treatments were allocated by stratified random blocks and by adaptive stratification. Finally, we considered the importance of the method of analysis in realizing the gain in power which may potentially be achieved by allocating treatments using stratified random blocks or adaptive stratification.     

Studies with group treatments required special power calculations, allocation methods, and statistical analyses

ObjectiveIn some trials, the intervention is delivered to individuals in groups, for example, groups that exercise together. The group structure of such trials has to be taken into consideration in the analysis and has an impact on the power of the trial. Our aim was to provide optimal methods for the design and analysis of such trials.Study Design and SettingWe described various treatment allocation methods and presented a new allocation algorithm: optimal batchwise minimization (OBM). We carried out a simulation study to evaluate the performance of unrestricted randomization, stratification, permuted block randomization, deterministic minimization, and OBM. Furthermore, we described appropriate analysis methods and derived a formula to calculate the study size.ResultsStratification, deterministic minimization, and OBM had considerably less risk of imbalance than unrestricted randomization and permuted block randomization. Furthermore, OBM led to unpredictable treatment allocation. The sample size calculation and the analysis of the study must be based on a multilevel model that takes the group structure of the trial into account.ConclusionTrials evaluating interventions that are carried out in subsequent groups require adapted treatment allocation, power calculation, and analysis methods. From the perspective of obtaining overall balance, we conclude that minimization is the method of choice. When the number of prognostic factors is low, stratification is an excellent alternative. OBM leads to better balance within the batches, but it is more complicated. It is probably most worthwhile in trials with many prognostic factors. From the perspective of predictability, a treatment allocation method, such as OBM, that allocates several subjects at the same time, is superior to other methods because it leads to the lowest possible predictability.     

Patterns of response by sociodemographic characteristics and recruitment methods for women in UK population surveys and cohort studies

ABSTRACT

Women are an important public health focus, because they are more likely to experience some social determinants of disease, and they influence family health. Little research has explored the sociodemographic representativeness of women in research studies. We examined the representativeness of female respondents across four sociodemographic factors in UK population surveys and cohort studies. Six UK population-based health surveys (from 2009–2013) and eight Medical Research Council cohort studies (from 1991 to 2014) were included. Percentages of women respondents by age, income/occupation, education status, and ethnicity were compared against contemporary population estimates. Women aged <35 years were under-represented. The oldest women were under-represented in four of nine studies. Within income/occupation, at the highest deprivation level, the range was 4 percent under-representation to 43 percent over-representation; at the lowest level, it was 6 percent under-representation to 21 percent over representation. Of nine studies reporting educational level, four under-represented women without school qualifications, and three under-represented women with degrees. One of five studies over-represented non-white groups and under-represented white women (by 9 percent). Response patterns varied by topic and recruitment and data collection methods. Future research should focus upon the methods used to identify, reach, and engage women to improve representativeness in studies addressing health behaviors.     

Sequential methods for random-effects meta-analysis

Although meta-analyses are typically viewed as retrospective activities, they are increasingly being applied prospectively to provide up-to-date evidence on specific research questions. When meta-analyses are updated account should be taken of the possibility of false-positive findings due to repeated significance tests. We discuss the use of sequential methods for meta-analyses that incorporate random effects to allow for heterogeneity across studies. We propose a method that uses an approximate semi-Bayes procedure to update evidence on the among-study variance, starting with an informative prior distribution that might be based on findings from previous meta-analyses. We compare our methods with other approaches, including the traditional method of cumulative meta-analysis, in a simulation study and observe that it has Type I and Type II error rates close to the nominal level. We illustrate the method using an example in the treatment of bleeding peptic ulcers.     

Studywise minimization: A treatment allocation method that improves balance among treatment groups and makes allocation unpredictable

ObjectivesIn randomized controlled trials with many potential prognostic factors, serious imbalance among treatment groups regarding these factors can occur. Minimization methods can improve balance but increase the possibility of selection bias. We described and evaluated the performance of a new method of treatment allocation, called studywise minimization, that can avoid imbalance by chance and reduce selection bias.Study Design and SettingThe studywise minimization algorithm consists of three steps: (1) calculate the imbalance for all possible allocations, (2) list all allocations with minimum imbalance, and (3) randomly select one of the allocations with minimum imbalance. We carried out a simulation study to compare the performance of studywise minimization with three other allocation methods: randomization, biased-coin minimization, and deterministic minimization. Performance was measured, calculating maximal and average imbalance as a percentage of the group size.ResultsIndependent of trial size and number of prognostic factors, the risk of serious imbalance was the highest in randomization and absent in studywise minimization. The largest differences among the allocation methods regarding the risk of imbalance were found in small trials.ConclusionStudywise minimization is particularly useful in small trials, where it eliminates the risk of serious imbalances without generating the occurrence of selection bias.