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第43行： − Stratified randomization decides one or multiple prognostic factors to make subgroups, on average, have similar entry characteristics. The patient factor can be accurately decided by examining the outcome in previous studies.<ref>{{Cite journal|last=Sylvester|first=Richard|date=December 1982|title=Fundamentals of clinical trials|journal=Controlled Clinical Trials|volume=3|issue=4|pages=385–386|doi=10.1016/0197-2456(82)90029-0|issn=0197-2456}}</ref> − The number of subgroups can be calculated by multiplying the number of strata for each factor. Factors are measured before or at the time of randomization and experimental subjects are divided into several subgroups or strata according to the results of measurements.<ref name=":0">{{Cite book|last=Pocock, Stuart J.|title=Clinical trials : a practical approach|publisher=John Wiley & Sons Ltd|date=Jul 1, 2013|isbn=978-1-118-79391-6|location=Chichester|oclc=894581169}}</ref>+ 第44行：
第54行： − Within each stratum, several randomization strategies can be applied, which involves [[Simple random sample|simple randomization]], [[Blocking (statistics)#Blocking used for nuisance factors that can be controlled|blocked randomization]], and [[Minimisation (clinical trials)|minimization]].+
→Techniques
== Techniques ==
== Techniques ==
[[File:Simple_random_sampling_after_stratification_step.png|thumb|Simple random sampling after stratification step]]
[[File:Simple_random_sampling_after_stratification_step.png|thumb|分层后简单随机抽样]]
Stratified randomization decides one or multiple prognostic factors to make subgroups, on average, have similar entry characteristics. The patient factor can be accurately decided by examining the outcome in previous studies.<ref>{{Cite journal|last=Sylvester|first=Richard|date=December 1982|title=Fundamentals of clinical trials|journal=Controlled Clinical Trials|volume=3|issue=4|pages=385–386|doi=10.1016/0197-2456(82)90029-0|issn=0197-2456}}</ref>
<font color="#32cd32"> 分层随机试验决定一个或多个预后因素，使亚组平均具有相似的进入特征。Stratified randomization decides one or multiple prognostic factors to make subgroups, on average, have similar entry characteristics.</font> 通过检查先前研究的结果，可以准确地确定患者因素。<ref>{{Cite journal|last=Sylvester|first=Richard|date=December 1982|title=Fundamentals of clinical trials|journal=Controlled Clinical Trials|volume=3|issue=4|pages=385–386|doi=10.1016/0197-2456(82)90029-0|issn=0197-2456}}</ref>
The number of subgroups can be calculated by multiplying the number of strata for each factor. Factors are measured before or at the time of randomization and experimental subjects are divided into several subgroups or strata according to the results of measurements.<ref name=":0">{{Cite book|last=Pocock, Stuart J.|title=Clinical trials : a practical approach|publisher=John Wiley & Sons Ltd|date=Jul 1, 2013|isbn=978-1-118-79391-6|location=Chichester|oclc=894581169}}</ref>
子群的数量可以通过乘以每个因素的层数来计算。在随机化前或随机化时测量因素，并根据测量结果将实验对象分为若干亚组或层。
Within each stratum, several randomization strategies can be applied, which involves [[Simple random sample|simple randomization]], [[Blocking (statistics)#Blocking used for nuisance factors that can be controlled|blocked randomization]], and [[Minimisation (clinical trials)|minimization]].
在每一层中，可以应用几种随机试验策略，包括<font color="#ff8000"> '''简单随机试验 Simple randomization''' </font>、<font color="#ff8000"> '''分块随机试验你Blocked randomization''' </font>和<font color="#ff8000"> '''最小化试验 Minimization''' </font>。
Block randomization is commonly used in the experiment with a relatively big sampling size to avoid the imbalance allocation of samples with important characteristics. In certain fields with strict requests of randomization such as clinical trials, the allocation would be predictable when there is no blinding process for conductors and the block size is limited. The blocks permuted randomization in strata could possibly cause an imbalance of samples among strata as the number of strata increases and the sample size is limited, For instance, there is a possibility that no sample is found meeting the characteristic of certain strata.
Block randomization is commonly used in the experiment with a relatively big sampling size to avoid the imbalance allocation of samples with important characteristics. In certain fields with strict requests of randomization such as clinical trials, the allocation would be predictable when there is no blinding process for conductors and the block size is limited. The blocks permuted randomization in strata could possibly cause an imbalance of samples among strata as the number of strata increases and the sample size is limited, For instance, there is a possibility that no sample is found meeting the characteristic of certain strata.
为了避免重要特征样本分配不平衡的问题，实验中常采用分块随机化的方法，采样规模较大。在某些严格要求随机化的领域，例如临床试验，当没有导体的盲法和块大小有限时，分配是可以预测的。随着地层数量的增加和样本容量的限制，地层中的块体随机化可能导致地层之间样本的不平衡，例如，有可能找不到符合特定地层特征的样本。
为了避免重要特征样本分配不平衡的问题，实验中常采用分块随机化的方法，采样规模较大。在某些严格要求随机化的领域，例如临床试验，当没有导体的盲法和块大小有限时，分配是可以预测的。随着地层数量的增加和样本容量的限制，地层中的块体随机化可能导致地层之间样本的不平衡，例如，有可能找不到符合特定地层特征的样本。
In order to guarantee the similarity of each treatment group, the "minimization" method attempts are made, which is more direct than random permuted block within strats. In the minimization method, samples in each stratum are assigned to treatment groups based on the sum of samples in each treatment group, which makes the number of subjects keep balance among the group.
In order to guarantee the similarity of each treatment group, the "minimization" method attempts are made, which is more direct than random permuted block within strats. In the minimization method, samples in each stratum are assigned to treatment groups based on the sum of samples in each treatment group, which makes the number of subjects keep balance among the group.