什么是Nested case-control study(巢式病例对照研究)?

2012-08-26 MedSci MedSci原创

巢式病例对照研究(nested case control study,NCCS)又被译做嵌入式病例对照研究,也称队列内病例对照研究(case control study within a cohort),是在全队列内套用病例对照设计。这一设计方案于1973年由美国流行病学家Mantel最早提出,称其为综合性病例对照研究 (synthetic case control study),1982年正式提

巢式病例对照研究(nested case control study,NCCS)又被译做嵌入式病例对照研究,也称队列内病例对照研究(case control study within a cohort),是在全队列内套用病例对照设计。这一设计方案于1973年由美国流行病学家Mantel最早提出,称其为综合性病例对照研究 (synthetic case control study),1982年正式提出巢式病例对照研究(nested case-control study),这是将队列研究与病例对照研究相结合的一种研究方法。

它在国际流行病学协会出版的《流行病学词典》(John M.Last主编,1988)中的定义为:NCCS是这样一种病例对照研究,它的病例和对照人群都从一个全队列(即通常所说的队列研究)中选取。因为病例和对照的某些资料已经存在,一些潜在混杂因素的影响就能被减少或避免。如病例和对照的暴露资料等均在疾病或死亡未发生前获得,故不存在暴露与疾病的时间顺序问题,观察偏倚也能得到很好地控制。

1.概念:
是在队列内套用病例对照研究的一种设计,其研究对象是在队列研究的基础上确定的,以队列中所有的病例作为病例组,再根据病例发病时间,在研究队列的非病例中随机匹配一个或多个对照,组成对照组。但是其研究方法和分析方法仍与病例对照研究相同(主要是配比病例对照研究)。此种研究设计尤其适合于研究因素包括有复杂的化学或生化分析的前瞻性研究。

2.研究类型:
由于巢式病例对照研究是在队列研究的基础上设计和实施的,因此也有前瞻性,回顾性,双向性三类。

3.步骤 :
1)确定研究队列人群(注意:人群进入队列时都没有发生所要的疾病,和队列研究相同)
2)收集队列内每个成员的相关信息和生物标本。
3)预定期间的随访
4)确定随访期内发生的某疾病全部病例组成病例组
5)随访结束时还未发生所研究疾病的人群随机抽取一定数量组成对照组,一般采用比配的方式为每个病例抽取一定数量的对照组成对照组。
6)对以前收集的两组成员相关生物标本进行必要的化验。
7)统计分析,计算发病率、OR等。

4.特点:
1)暴露资料在疾病诊断前收集,选择偏倚和信息偏倚小
2)病例与对照来自同一队列,可比性好
3)可计算发病率。统计和检验效率高于病例对照研究
4)样本量小于队列研究,节约人、物力,
5)符合因果推论要求,论证强度高。

有关巢式病例对照研究的问答

A1 为什么要使用NCCS?

  答:应用NCCS的首要原因是它只需收集那些被选为NCCS研究对象而不是全队列的完整资料,从而减少了资料收集所花费的人力物力。队列研究在确定暴露因素与疾病的因果关联上,能为人们提供直接的证据,比病例对照研究更具有说服力。但是开展慢性病病因调查的大规模队列研究存在许多困难令人望而却步。假如有一个大规模的队列研究,且要收集并分析研究对象的血、尿等生化标本。这时,流行病学家和生物统计学家们都期望借助一种新的设计来减少工作量,大大降低科研经费,并能方便地在微机上实施数据处理和统计分析,而其研究结果在阐述暴露因素与疾病联系上与全队列研究设计所能说明的问题没有两样。NCCS设计和病例队列研究设计(case cohort design)就能使之 梦想成真。在膳食调查中,个人饮食可以根据对不同时期 摄入食物的详细膳食调查来评估。 运用计算机分析这些记录编码和描述非常吃力而且代价很高。如果应用NCCS方法,只需对随访过程中发病的病例,以及为每一例病人所选取的对照进行编码,可以省去大量的工作。当对生物 学标本进行昂贵的实验室分析时,队列中每一个对象的资料都可以收集并保存起来,但只分 析进入NCCS的病例和对照的资料,省力省钱而且可与由整个队列人群估计的一样精确。
其次,随着时间的推移,研究工作的开展和深入,一项队列研究很可能要增加原设计中没有 的某一暴露或混杂因素的内容,NCCS能妥善解决这一问题。例如,假设常规的健康服务监测资料显示不同组别病人的死亡率不同,但是,因为缺乏重要混杂因素的资料,解释上述结果时不能排除是否因为混杂所致。这时,设计一个NCCS,仅需收集选入NCCS的研究对象更详细的医学档案资料就能测定出混杂因素的作用,并能对其进行控制。
最后,应用NCCS能避免那些与时间关联自变量(time-dependent explanatory variables)的 计算问题。NCCS设计建立在对各危险集随机抽样样本的基础之上,而队列中处在与结局事件 (event)或称失效(failure),如发病,发生时间相同时的非病例组成的危险集(risk sets) 是Cox回归模型分析中的基础概念。

A2 危险集是什么? 如何对危险集中的个体抽样?

  答:图1显示在对11位研究对象的随访过程中,有4人发生结局事件(发病或死亡 )。相应于这4个结局事件,就有4个危险集(risk sets)。它包括在发生每一个结局事件的瞬间,所有仍处于随访中的研究对象,也就是图中所示所有观察时间线跨过相应的垂直虚线的研究对象。进行NCCS 时,每出现一例病人就要从相应的危险集中选择对照,即对该危险集中所有非病例研究对象进行随机抽样。对一个危险集的随机抽样,必须要独立于对其它危险集的随机抽样,也要独立于该危险集中各成员以后出现的结局事件或截尾数据(censoring)。

A3 图1中4个危险集 的样本量各是多少?如何为每一例病人选择一个对照?

  答:第一个危险集包含10个研究对象(1个病人,9个非病例),其它三个危险集 分别包括9、7和4个研究对象。第一例病人的对照从第一个危险集9个未患病者中随机选取一 个。另外三个病人的对照分别从其余危险集8、6、3个非病例中随机选取一个。

A4 为每一个病例选 多少对照较合适?

  答:如果对照的数量是病例的m倍,病例对照研究相对于队列研究的精确度等于 :
严格说来,这个公式适用于暴露的效果小,而且不控制任何混杂的情况。但它 也常常用来对其他情况进行粗略指导。因为当m>5后,与1相差无几 。为每个病例选取5个或最多10个对照,而不是整个危险人群,几乎不损失研究的精确性。

A5 同一研究对象能 否被NCCS多次选为对照?

  答:简单地说,同一研究对象可以被NCCS多次选为对照,甚至后来它也可以成为一个病人。在发病率极低的队列研究中,同一研究对象被多次选用的机会较少。为避免同一研究对象被多次使用而对随机抽样采取的任何干预措施都违背对各危险集独立抽样的要求。

A6 如何对病例对照 进行配比?

  答:在NCCS设计中,病例仍然是全队列中的所有病例,而对照则是在相应失效时间上的危险集内选出的很少一部分非病例。除了这种时间配比外,较常考虑的配比因素是性别、年龄,此外还要根据具体情况对混杂因素进行配比。例如,对肺癌的职业研究中,吸烟是一个很强的混杂因素,这要求职业人群之间的比较应该控制吸烟。假如一个病例选五个对照,NCCS产生的研究总体中吸烟者和非吸烟者的比例可能与全队列的大不相同。因为吸烟者中的病例要比非吸烟者中的多得多,很可能出现这种情况,在吸烟者中,每一位病例找不出五个对照,而在非吸烟者中每一病例有远多于五个的对照。在这种情况下,最好能在病例和对照中对吸烟习惯进行配比。当然,这样做的先决条件是掌握整个队列的吸烟资料。对病例和对照的吸烟习惯进行配比,就不能推算吸烟的效果,但当吸烟成为研究中一个混杂因子时,我们没必要顾虑于此。

A7 NCCS资料分析常用的软件和 可采用的统计方法有哪些?

  答:NCCS 符合配比病例对照研究设计方案,因此可采用相应成熟的 统计方法,如M-H法、条件logistic回归模型。另外,NCCS在统计方法上也有一些发展,如Cox比例危险回归模型的应用,RR的伪似然函数的估计。常用的统计软件均可进行分析,如SAS、EPILOG、EGRET、SPSS等。

英文释义:
Nested case-control study
--From Wikipedia, the free encyclopedia

In a nested case-control study, cases of a disease that occur in a defined cohort are identified and, for each, a specified number of matched controls is selected from among those in the cohort who have not developed the disease by the time of disease occurrence in the case. For many research questions, the nested case-control design potentially offers impressive reductions in costs and efforts of data collection and analysis compared with the full cohort approach, with relatively minor loss in statistical efficiency . The nested case-control design is particularly advantageous for studies of biologic precursors of disease. To advance its prevention research agenda, NIH might be encouraged to maintain a registry of new and existing cohorts, with an inventory of data collected for each; to foster the development of specimen banks; and to serve as a clearinghouse for information about optimal storage conditions for various types of pecimens. Compared with case-control studies, nested case-control studies can reduce 'recall bias' and temporal ambiguity, and compared with cohort studies can reduce cost and save time.

The drawback of nested case-control studies is non-diseased persons from whom the controls are selected may not be fully representative of the original cohort, due to death or failure to follow-up cases.

Similar terms--Panel study:
A case-control study in which cases and controls are drawn from within a prospective study. All cases who developed the outcome of interest during the follow-up are selected and compared with a subgroup of the non-cases. Exposure is defined prior to disease development based on data collected at baseline or on assays conducted in biological samples collected at baseline.

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    2020-10-14 ms6000001003714308

    图片在哪里可以看到呢

    0

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    2017-09-30 12034f64m75(暂无昵称)

    图在哪里啊?

    0

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    2014-03-02 fasd

    safda

    0

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    2013-01-10 aliceclz
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    2012-10-28 westjerry

    正愁这个问题呢,不错

    0

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    2013-04-13 amyloid
  9. [GetPortalCommentsPageByObjectIdResponse(id=891925, encodeId=f594891925bb, content=图片在哪里可以看到呢, beContent=null, objectType=article, channel=null, level=null, likeNumber=65, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=8f815425628, createdName=ms6000001003714308, createdTime=Wed Oct 14 07:56:58 CST 2020, time=2020-10-14, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=249059, encodeId=affc2490594b, content=图在哪里啊?, beContent=null, objectType=article, channel=null, level=null, likeNumber=84, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=d86c2162397, createdName=12034f64m75(暂无昵称), createdTime=Sat Sep 30 08:53:35 CST 2017, time=2017-09-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=8007, encodeId=3600800ef5, content=safda, beContent=null, objectType=article, channel=null, level=null, likeNumber=134, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=fasd, createdTime=Sun Mar 02 23:03:00 CST 2014, time=2014-03-02, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1992526, encodeId=d52b1992526e3, content=<a href='/topic/show?id=f1c74e84819' target=_blank style='color:#2F92EE;'>#巢式病例对照研究#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=30, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=47848, encryptionId=f1c74e84819, topicName=巢式病例对照研究)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=a0802500050, createdName=肉我只吃一口, createdTime=Sat Jun 01 14:19:00 CST 2013, time=2013-06-01, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1984119, encodeId=cef719841191f, content=<a href='/topic/show?id=77dee0776b6' target=_blank style='color:#2F92EE;'>#病例对照研究#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=70776, encryptionId=77dee0776b6, topicName=病例对照研究)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=a35a460, createdName=fengyi812, createdTime=Sat Apr 20 09:19:00 CST 2013, time=2013-04-20, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1702909, encodeId=5bba1e02909cb, content=<a href='/topic/show?id=72351e25437' target=_blank style='color:#2F92EE;'>#TED#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=37, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=17254, encryptionId=72351e25437, topicName=TED)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=76e230462388, createdName=aliceclz, createdTime=Thu Jan 10 07:19:00 CST 2013, time=2013-01-10, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=4875, encodeId=355f48e526, content=正愁这个问题呢,不错, beContent=null, objectType=article, channel=null, level=null, likeNumber=161, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=0cb769754, createdName=westjerry, createdTime=Sun Oct 28 23:11:00 CST 2012, time=2012-10-28, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1775254, encodeId=62ee1e7525454, content=<a href='/topic/show?id=937d46650bf' target=_blank style='color:#2F92EE;'>#对照#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=26, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=46650, encryptionId=937d46650bf, topicName=对照)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=8ae138776909, createdName=amyloid, createdTime=Sat Apr 13 14:19:00 CST 2013, time=2013-04-13, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=4851, encodeId=ac8b4851c3, content=又了解了一个研究方法,希望有更多的好内容拿来分享, beContent=null, objectType=article, channel=null, level=null, likeNumber=147, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=de4b41064, createdName=xjmuyq75, createdTime=Wed Oct 24 00:43:00 CST 2012, time=2012-10-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=2050324, encodeId=2a3e20503249c, content=<a href='/topic/show?id=95f8e042dd' target=_blank style='color:#2F92EE;'>#EST#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=32, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=7042, encryptionId=95f8e042dd, topicName=EST)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=e9a6357, createdName=xiangyuzhou971, createdTime=Sun Jan 27 11:19:00 CST 2013, time=2013-01-27, status=1, ipAttribution=)]
    2012-10-24 xjmuyq75

    又了解了一个研究方法,希望有更多的好内容拿来分享

    0

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