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在临床试验中，混杂因素是需要考虑的重要因素

在临床试验中，混杂因素是需要考虑的重要因素

=== Block randomization within strata ===

 

===Block randomization within strata===

[[Randomized block design|Block randomization]], sometimes called permuted block randomization, applies blocks to allocate subjects from the same strata equally to each group in the study. In block randomization, allocation ratio (ratio of the number of one specific group over other groups) and group sizes are specified. The block size must be the multiples of the number of treatments so that samples in each stratum can be assigned to treatment groups with the intended ratio.<ref name=":0" /> For instance, there should be 4 or 8 strata in a clinical trial concerning breast cancer where age and nodal statuses are two prognostic factors and each factor is split into two-level. The different blocks can be assigned to samples in multiple ways including random list and computer programming.<ref>{{Cite web|url=https://www.sealedenvelope.com/help/redpill/latest/block/|title=Sealed Envelope {{!}} Random permuted blocks|date=Feb 25, 2020|website=www.sealedenvelope.com|access-date=2020-04-07}}</ref><ref>{{Citation|last1=Friedman|first1=Lawrence M.|title=Introduction to Clinical Trials|date=2010|work=Fundamentals of Clinical Trials|pages=1–18|publisher=Springer New York|isbn=978-1-4419-1585-6|last2=Furberg|first2=Curt D.|last3=DeMets|first3=David L.|doi=10.1007/978-1-4419-1586-3_1}}</ref>

[[Randomized block design|Block randomization]], sometimes called permuted block randomization, applies blocks to allocate subjects from the same strata equally to each group in the study. In block randomization, allocation ratio (ratio of the number of one specific group over other groups) and group sizes are specified. The block size must be the multiples of the number of treatments so that samples in each stratum can be assigned to treatment groups with the intended ratio.<ref name=":0" /> For instance, there should be 4 or 8 strata in a clinical trial concerning breast cancer where age and nodal statuses are two prognostic factors and each factor is split into two-level. The different blocks can be assigned to samples in multiple ways including random list and computer programming.<ref>{{Cite web|url=https://www.sealedenvelope.com/help/redpill/latest/block/|title=Sealed Envelope {{!}} Random permuted blocks|date=Feb 25, 2020|website=www.sealedenvelope.com|access-date=2020-04-07}}</ref><ref>{{Citation|last1=Friedman|first1=Lawrence M.|title=Introduction to Clinical Trials|date=2010|work=Fundamentals of Clinical Trials|pages=1–18|publisher=Springer New York|isbn=978-1-4419-1585-6|last2=Furberg|first2=Curt D.|last3=DeMets|first3=David L.|doi=10.1007/978-1-4419-1586-3_1}}</ref>

与简单随机抽样相比，分层随机抽样具有更高的统计准确性，因为所选择的元素代表总体具有高度的相关性。分层随机化的步骤是非常重要的，它试图确保没有偏差，取样或偶然，影响研究中患者样本的代表性。它增加了研究力量，特别是在小型临床试验(n < 400) ，因为这些已知的临床特征分层被认为影响干预的结果。它有助于防止 i 型错误的发生，这在临床研究中是很有价值的。它还对主动控制等效试验的样本容量有重要影响，并在理论上简化了亚组分析和中期分析。

与简单随机抽样相比，分层随机抽样具有更高的统计准确性，因为所选择的元素代表总体具有高度的相关性。分层随机化的步骤是非常重要的，它试图确保没有偏差，取样或偶然，影响研究中患者样本的代表性。它增加了研究力量，特别是在小型临床试验(n < 400) ，因为这些已知的临床特征分层被认为影响干预的结果。它有助于防止 i 型错误的发生，这在临床研究中是很有价值的。它还对主动控制等效试验的样本容量有重要影响，并在理论上简化了亚组分析和中期分析。

如果总体不能完全分配到地层中，那么分层抽样就不能应用，这将导致样本大小与可用样本成比例，而不是整个子群总体。

如果总体不能完全分配到地层中，那么分层抽样就不能应用，这将导致样本大小与可用样本成比例，而不是整个子群总体。

如果受试者符合多个阶层的包含标准，将样本分配到各个子群组的过程可能会涉及重叠，这可能会导致人口的不正当手法引诱。

如果受试者符合多个阶层的包含标准，将样本分配到各个子群组的过程可能会涉及重叠，这可能会导致人口的不正当手法引诱。

=== Minimization method ===

===Minimization method===

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.<ref name=":0" /> If the sums for multiple treatment groups are the same, simple randomization would be conducted to assign the treatment. In practice, the minimization method needs to follow a daily record of treatment assignments by prognostic factors, which can be done effectively by using a set of index cards to record.  The minimization method effectively avoids imbalance among groups but involves less random process than block randomization because the random process is only conducted when the treatment sums are the same. A feasible solution is to apply an additional random list which makes the treatment groups with a smaller sum of marginal totals possess a higher chance (e.g.¾) while other treatments have a lower chance(e.g.¼ ).<ref name=":1">{{Cite journal|last=Pocock|first=S. J.|date=March 1979|title=Allocation of Patients to Treatment in Clinical Trials|journal=Biometrics|volume=35|issue=1|pages=183–197|doi=10.2307/2529944|jstor=2529944|pmid=497334|issn=0006-341X}}</ref>

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.<ref name=":0" /> If the sums for multiple treatment groups are the same, simple randomization would be conducted to assign the treatment. In practice, the minimization method needs to follow a daily record of treatment assignments by prognostic factors, which can be done effectively by using a set of index cards to record.  The minimization method effectively avoids imbalance among groups but involves less random process than block randomization because the random process is only conducted when the treatment sums are the same. A feasible solution is to apply an additional random list which makes the treatment groups with a smaller sum of marginal totals possess a higher chance (e.g.¾) while other treatments have a lower chance(e.g.¼ ).<ref name=":1">{{Cite journal|last=Pocock|first=S. J.|date=March 1979|title=Allocation of Patients to Treatment in Clinical Trials|journal=Biometrics|volume=35|issue=1|pages=183–197|doi=10.2307/2529944|jstor=2529944|pmid=497334|issn=0006-341X}}</ref>

== Application ==

==Application==

[[File:Confounding_factors_are_important_to_consider_in_clinical_trials.png|thumb|219x219px|Confounding factors are important to consider in clinical trials]]

[[File:Confounding_factors_are_important_to_consider_in_clinical_trials.png|thumb|219x219px|Confounding factors are important to consider in clinical trials]]

In real life, stratified random sampling can be applied to results of election polling, investigations into income disparities among social groups, or measurements of education opportunities across nations.<ref name=":3" />

In real life, stratified random sampling can be applied to results of election polling, investigations into income disparities among social groups, or measurements of education opportunities across nations.<ref name=":3" />

== Stratified randomization in clinical trials ==

==Stratified randomization in clinical trials==

In [[clinical trial]]s, patients are stratified according to their social and individual backgrounds, or any factor that are relevant to the study, to match each of these groups within the entire patient population. The aim of such is to create a balance of clinical/prognostic factor as the trials would not produce valid results if the study design is not balanced.<ref>{{Cite book|last1=Polit|first1=DF|title=Nursing Research: Generating and Assessing Evidence for Nursing Practice, 9th ed.|last2=Beck|first2=CT|publisher=Lippincott Williams & Wilkins.|year=2012|location=Philadelphia, USA: Wolters Klower Health}}</ref> The step of stratified randomization is extremely important as an attempt to ensure that no bias, delibrate or accidental, affects the representative nature of the patient sample under study.<ref>{{Cite web|url=https://www.omixon.com/patient-stratification-in-clinical-trials/|title=Patient Stratification in Clinical Trials|date=2014-12-01|website=Omixon {{!}} NGS for HLA|language=en-US|access-date=2020-04-26}}</ref> It increases the study power, especially in small clinical trials(n<400), as these known clinical traits stratified are thought to effect the outcomes of the interventions.<ref>{{Cite web|url=https://www.statisticshowto.com/stratified-randomization/|title=Stratified Randomization in Clinical Trials|last=Stephanie|date=2016-05-20|website=Statistics How To|language=en-US|access-date=2020-04-26}}</ref> It helps prevent the occurrence of [[Type I and type II errors|type I error]], which is valued highly in clinical studies.<ref name=":6">{{Cite journal|last=Kernan|first=W|date=Jan 1999|title=Stratified Randomization for Clinical Trials|journal=Journal of Clinical Epidemiology|volume=52|issue=1|pages=19–26|doi=10.1016/S0895-4356(98)00138-3|pmid=9973070}}</ref>  It also has an important effect on sample size for active control equivalence trials and in theory, facilitates [[subgroup analysis]] and [[interim analysis]].<ref name=":6" />

In [[clinical trial]]s, patients are stratified according to their social and individual backgrounds, or any factor that are relevant to the study, to match each of these groups within the entire patient population. The aim of such is to create a balance of clinical/prognostic factor as the trials would not produce valid results if the study design is not balanced.<ref>{{Cite book|last1=Polit|first1=DF|title=Nursing Research: Generating and Assessing Evidence for Nursing Practice, 9th ed.|last2=Beck|first2=CT|publisher=Lippincott Williams & Wilkins.|year=2012|location=Philadelphia, USA: Wolters Klower Health}}</ref> The step of stratified randomization is extremely important as an attempt to ensure that no bias, delibrate or accidental, affects the representative nature of the patient sample under study.<ref>{{Cite web|url=https://www.omixon.com/patient-stratification-in-clinical-trials/|title=Patient Stratification in Clinical Trials|date=2014-12-01|website=Omixon {{!}} NGS for HLA|language=en-US|access-date=2020-04-26}}</ref> It increases the study power, especially in small clinical trials(n<400), as these known clinical traits stratified are thought to effect the outcomes of the interventions.<ref>{{Cite web|url=https://www.statisticshowto.com/stratified-randomization/|title=Stratified Randomization in Clinical Trials|last=Stephanie|date=2016-05-20|website=Statistics How To|language=en-US|access-date=2020-04-26}}</ref> It helps prevent the occurrence of [[Type I and type II errors|type I error]], which is valued highly in clinical studies.<ref name=":6">{{Cite journal|last=Kernan|first=W|date=Jan 1999|title=Stratified Randomization for Clinical Trials|journal=Journal of Clinical Epidemiology|volume=52|issue=1|pages=19–26|doi=10.1016/S0895-4356(98)00138-3|pmid=9973070}}</ref>  It also has an important effect on sample size for active control equivalence trials and in theory, facilitates [[subgroup analysis]] and [[interim analysis]].<ref name=":6" />

类别: 抽样(统计)

类别: 抽样(统计)

== Advantage ==

==Advantage==

Category:Sampling techniques

Category:Sampling techniques

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