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分层随机试验 (查看源代码)

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→‎临床试验中的分层随机试验 Stratified randomization in clinical trials
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==临床试验中的分层随机试验 Stratified randomization in clinical trials==
 
==临床试验中的分层随机试验 Stratified randomization in clinical trials==
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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" />
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在'''<font color="#ff8000"> 临床试验 Clinical trials </font>'''中，根据患者的社会和个人背景或与研究相关的任何因素对患者进行分层，以匹配整个患者群体中的每个组。 这样做的目的是建立临床/预后因素（prognostic factor）的平衡，因为如果研究设计不平衡，试验将不会产生有效的结果。<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>  分层随机化的步骤非常重要，因为它试图确保没有偏见、有意或无意地影响所研究患者样本的代表性。 <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>  它增加了研究能力，尤其是在小型临床试验中（n<400），因为这些已知的临床特征分层被认为会影响干预的结果。<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>它有助于防止在临床研究中受到高度重视的 '''<font color="#ff8000"> I 型错误 Type I error </font>'''的发生。 <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>它还对主动对照等效试验的样本量产生重要影响，并且在理论上有助于'''<font color="#ff8000"> 亚组分析 Subgroup analysis </font>'''和'''<font color="#ff8000"> 中期分析 Interim analysis </font>'''。 <ref name=":6" />
 
在'''<font color="#ff8000"> 临床试验 Clinical trials </font>'''中，根据患者的社会和个人背景或与研究相关的任何因素对患者进行分层，以匹配整个患者群体中的每个组。 这样做的目的是建立临床/预后因素（prognostic factor）的平衡，因为如果研究设计不平衡，试验将不会产生有效的结果。<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>  分层随机化的步骤非常重要，因为它试图确保没有偏见、有意或无意地影响所研究患者样本的代表性。 <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>  它增加了研究能力，尤其是在小型临床试验中（n<400），因为这些已知的临床特征分层被认为会影响干预的结果。<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>它有助于防止在临床研究中受到高度重视的 '''<font color="#ff8000"> I 型错误 Type I error </font>'''的发生。 <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>它还对主动对照等效试验的样本量产生重要影响，并且在理论上有助于'''<font color="#ff8000"> 亚组分析 Subgroup analysis </font>'''和'''<font color="#ff8000"> 中期分析 Interim analysis </font>'''。 <ref name=":6" />
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