p for trend内涵与SPSS实现

2019-09-24 临床流行病学和循证医学 临床流行病学和循证医学

p for trend主要是指随着某分组变量的递增或递减(需要注意的是,该分组变量需是有序多分类变量),其他变量是否存在某种趋势变化。如下表中,每日观看电视时长为有序多分类变量:小于1.5h,1.5~3.0h,3.0~4.5h,大于等于4.5h。研究者探讨了随着电视观看时长的增加,其他变量的变化情况。我们知道卡方检验中的“线性关联”可以提供随着分组变量的递增,某分类变量的趋势变化。但是,随着某分组

p for trend主要是指随着某分组变量的递增或递减(需要注意的是,该分组变量需是有序多分类变量),其他变量是否存在某种趋势变化。如下表中,每日观看电视时长为有序多分类变量:小于1.5h,1.5~3.0h,3.0~4.5h,大于等于4.5h。研究者探讨了随着电视观看时长的增加,其他变量的变化情况。我们知道卡方检验中的“线性关联”可以提供随着分组变量的递增,某分类变量的趋势变化。但是,随着某分组变量的递增,可以探讨某连续变量的趋势变化吗?如下表中,随着观看电视时长的递增,年龄是否存在某种趋势关系?看一个例子先,比如,我们想研究随着年龄增加,腰围和臀围的变化情况。通过描述性结果可知,随着年龄递增,腰围和臀围有增加的趋势。但是这种趋势是否有统计学意义呢?操作其实很简单,在单因素方差分析中,将腰围和臀围选入“因变量列表”框,将年龄分组选入“因子”框。点击“对比”,弹出对话框。在弹出的对话框中,选择“多项式”,可根据实际情况选择相应的“等级”:线性~五次。通过散点图发现年龄和腰围、臀围呈现线性关系,因此,本例选择线性。以腰围的输出结果为例,其中“组间(组合)”对应的结果即为单因素方差分析的结

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  1. [GetPortalCommentsPageByObjectIdResponse(id=2188611, encodeId=c0662188611e5, content=<a href='/topic/show?id=08933018ea9' target=_blank style='color:#2F92EE;'>#关键词#</a>怎么操作啊 , beContent=null, objectType=article, channel=null, level=null, likeNumber=15, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=30187, encryptionId=08933018ea9, topicName=关键词)], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=05825492269, createdName=李婷婷, createdTime=Thu Feb 22 02:57:58 CST 2024, time=2024-02-22, status=1, ipAttribution=河北省), GetPortalCommentsPageByObjectIdResponse(id=1215013, encodeId=4644121501348, content=***, beContent=null, objectType=article, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=49858138183, createdName=ms7000001777405024, createdTime=Wed Apr 27 13:14:32 CST 2022, time=2022-04-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=961417, encodeId=2eb796141e07, content=谢谢,好用, beContent=null, objectType=article, channel=null, level=null, likeNumber=58, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=94435486466, createdName=ms7000000473620956, createdTime=Thu Apr 29 07:30:25 CST 2021, time=2021-04-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=887049, encodeId=f52988e0499d, content=有用, beContent=null, objectType=article, channel=null, level=null, likeNumber=66, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200922/fc683f168f114d4bb372152732c98f20/e2418a0479a34ddf96fee5c4c9169329.jpg, createdBy=5bab5420121, createdName=longerhans, createdTime=Mon Sep 21 11:02:26 CST 2020, time=2020-09-21, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1905720, encodeId=60121905e2056, content=<a href='/topic/show?id=2ebd1e7981a' target=_blank style='color:#2F92EE;'>#trend#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=28, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=17798, encryptionId=2ebd1e7981a, topicName=trend)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=ced3193, createdName=freve, createdTime=Wed Jul 22 00:53:00 CST 2020, time=2020-07-22, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=373253, encodeId=a3383e3253d8, content=学习了, beContent=null, objectType=article, channel=null, level=null, likeNumber=69, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=http://cdnapi.center.medsci.cn/medsci/head/2019/08/03/0e492dda8bcc1f98474b74c2863aca87.jpg, createdBy=068d2446376, createdName=Dr Z, createdTime=Fri Sep 27 07:51:51 CST 2019, time=2019-09-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=373123, encodeId=a0fc3e3123ec, content=学习了, beContent=null, objectType=article, channel=null, level=null, likeNumber=83, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=http://cdnapi.center.medsci.cn/medsci/head/2019/08/03/0e492dda8bcc1f98474b74c2863aca87.jpg, createdBy=068d2446376, createdName=Dr Z, createdTime=Tue Sep 24 08:31:15 CST 2019, time=2019-09-24, status=1, ipAttribution=)]
    2024-02-22 李婷婷 来自河北省

    #关键词#怎么操作啊

    0

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    2022-04-27 ms7000001777405024

    ***

    0

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    2021-04-29 ms7000000473620956

    谢谢,好用

    0

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    2020-09-21 longerhans

    有用

    0

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    2020-07-22 freve
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    2019-09-27 Dr Z

    学习了

    0

  7. [GetPortalCommentsPageByObjectIdResponse(id=2188611, encodeId=c0662188611e5, content=<a href='/topic/show?id=08933018ea9' target=_blank style='color:#2F92EE;'>#关键词#</a>怎么操作啊 , beContent=null, objectType=article, channel=null, level=null, likeNumber=15, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=30187, encryptionId=08933018ea9, topicName=关键词)], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=05825492269, createdName=李婷婷, createdTime=Thu Feb 22 02:57:58 CST 2024, time=2024-02-22, status=1, ipAttribution=河北省), GetPortalCommentsPageByObjectIdResponse(id=1215013, encodeId=4644121501348, content=***, beContent=null, objectType=article, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=49858138183, createdName=ms7000001777405024, createdTime=Wed Apr 27 13:14:32 CST 2022, time=2022-04-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=961417, encodeId=2eb796141e07, content=谢谢,好用, beContent=null, objectType=article, channel=null, level=null, likeNumber=58, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=94435486466, createdName=ms7000000473620956, createdTime=Thu Apr 29 07:30:25 CST 2021, time=2021-04-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=887049, encodeId=f52988e0499d, content=有用, beContent=null, objectType=article, channel=null, level=null, likeNumber=66, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200922/fc683f168f114d4bb372152732c98f20/e2418a0479a34ddf96fee5c4c9169329.jpg, createdBy=5bab5420121, createdName=longerhans, createdTime=Mon Sep 21 11:02:26 CST 2020, time=2020-09-21, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1905720, encodeId=60121905e2056, content=<a href='/topic/show?id=2ebd1e7981a' target=_blank style='color:#2F92EE;'>#trend#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=28, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=17798, encryptionId=2ebd1e7981a, topicName=trend)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=ced3193, createdName=freve, createdTime=Wed Jul 22 00:53:00 CST 2020, time=2020-07-22, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=373253, encodeId=a3383e3253d8, content=学习了, beContent=null, objectType=article, channel=null, level=null, likeNumber=69, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=http://cdnapi.center.medsci.cn/medsci/head/2019/08/03/0e492dda8bcc1f98474b74c2863aca87.jpg, createdBy=068d2446376, createdName=Dr Z, createdTime=Fri Sep 27 07:51:51 CST 2019, time=2019-09-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=373123, encodeId=a0fc3e3123ec, content=学习了, beContent=null, objectType=article, channel=null, level=null, likeNumber=83, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=http://cdnapi.center.medsci.cn/medsci/head/2019/08/03/0e492dda8bcc1f98474b74c2863aca87.jpg, createdBy=068d2446376, createdName=Dr Z, createdTime=Tue Sep 24 08:31:15 CST 2019, time=2019-09-24, status=1, ipAttribution=)]
    2019-09-24 Dr Z

    学习了

    0

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近期看文献了吗,有没有看到p for trend呢

以前,觉得有个p值就很高大上了。怎料到,现在的trend已经变成了p for trend。p for trend主要是指随着某分组变量的递增或递减(需要注意的是,该分组变量需是有序多分类变量),其他变量是否存在某种趋势变化。如下表中,每日观看电视时长为有序多分类变量:小于1.5h,1.5~3.0h,3.0~4.5h,大于等于4.5h。研究者探讨了随着电视观看时长的增加,其他变量的变化情况。我们知道

文献中常看到P for trend,到底是个啥,如何操作实现?

提到趋势性检验,在医咖会的SPSS教程中,介绍过了Cochran-Armitage趋势检验,用于检验两个分类变量之间是否存在一定的变化趋势。但是我们在阅读文献时,也常常会看到文章结果展示的表格中,给出了P for trend的检验结果,如下表所示。针对上表内容,首先复习一下我们前期介绍的有关回归模型中设置哑变量的内容,对于连续性变量,可以考虑将其转化为哑变量后带入到回归模型中,详见:想将连续变