病例对照研究中对照的选择 Selection of controls in a case-control or case-base study

1. 病例对照研究中对照的选择Selection of controls in a case-control or case-base study Robert E. Fontaine, MD, MSc Bao-Ping Zhu, MD, MS US Expert Advisors to Chinese FETP

2. 选择对照的原则 Rules of control selection • 病例和对照来自于同一人群或人群组 Cases and controls come from the same base (population or population subset) • 病例和对照间除了要研究的暴露因素外其他越相似越好 Selection designed to increase similarity with cases for all characteristics except the exposures under investigation • 如果对照发生了疾病，那么也会变成病例的一部分 If controls had developed illness, they would have been detected as a case

3. 在工厂、学校、小村庄中选择对照 Selection of controls: factories, schools, institutions, small villages • 常常有各群组的名单 Lists are available by groups • 用单纯随机、系统抽样或其他方法来抽样 Random, systematic or other probability selection from lists. • 若各群组罹患率不同，应使病例和对照中各组人数平衡 If attack rates differ by group then balance cases to controls by group.

4. 某工厂疾病在各群组中的分布情况。如何选择对照？ A disease in a factory. How do you choose controls?

5. 某工厂疾病在各群组中的分布情况。如何选择对照？ A disease in a factory. How do you choose controls?

6. 在“开放”人群中选择对照 Control selection in an “open” population • 没有有用的名单 No useful list 例： • 大单位或工厂 Large institutions or factories • 城镇 Towns • 城市 Cities • 县 Counties • 省 Provinces, • 国家 Countries

7. 根据入选标准，从范围较小的人群中选择 We select from a more limited population defined by eligibility criteria • 将人群根据以下特征进行定义 Population + collection of persons defined by: • 时间 Time • 地点 Place • 人群 Person • 用入选标准，从这一假定人群中选择对照 Use eligibility criteria to select from this hypothetical population

8. 病例和对照应该用同样的入选标准 Eligibility criteria should be applied equally to cases and controls.

9. 例：在一个1,110,000人的城市中有150个病例。需要随机选择对照You have 150 cases in a city of 1,110,000 you select controls at random • 如何选择对照？How do you select controls? • 要考虑哪些问题？What must you think about? • 假定我们随机选择了150名对照 Let’s take a random selection of 150

10. 但是，分析时却发现病例和对照的分布如下 But you find in analysis the following distribution of cases and controls 为什么会发生这样的问题？Why??

11. 因为大多数暴露都发生在较小的人群组内 Much of the exposure was in a limited population. 所面临的问题与前面“封闭”人群中的情况相似 You will risk the same problem as in the earlier example in a closed population.

12. 假设病例的年龄分布不均衡 Imagine that the case have a limited age distribution.

13. 对人群中进行随机抽样作为对照组 A probability survey can be a control group • 优点 Strength • 较少偏倚 Less subject to biases • 抽样比例已知，因此可以计算率 Sampling fraction known – one can estimate risk • 缺点 Weakness • 代价昂贵 Expensive • 需要处理复杂抽样的分析问题 Need to handle complex survey analysis

14. 家庭内的对照效率很高 Household controls are very efficient • 对照与病例在许多因素上很类似 Similar to cases in many factors besides the disease • 病例和对照的暴露机会机会完全均等 Exposure opportunity nearly identical to cases • 每一个家庭可以看出是一个小的队列 Each household and be a small cohort • 实施较容易，常常配合良好 Logistics and cooperation are easy • 可以研究家庭外的暴露因素 Evaluates exposures outside the home • 缺点：可选择的人数有限，尤其是要对年龄或性别进行匹配时 Limited in numbers -- age or sex

15. 朋友、同学、同事对照很方便 Friends, schoolmates, and workmates are very convenient controls • 优点 Strength • 病例与对照间许多因素类似 Similar to cases in many factors besides the disease • 实施较容易，常常配合良好 Logistics and cooperation are easy • 缺点 Weakness • 被人提名作为对照，常常与暴露有关 Being named may be related to exposure • 病例影响对照的选择 Case influences control selection • 重复：同一个人可能被两个病例所提名 Overlap – same person named by ≥ 2 cases

16. 邻居对照：可以减少可能的地区偏倚 Selection of neighbors handles potential geographic biases • 比抽样较容易实施 Logistics more simple than surveys • 若入户调查病例家庭，这一方法很好 Good if visits to case-household are done • 邻居对照有很高的暴露机会 High probability of exposure opportunity • 在对照家庭内随机抽取对照 Best to use a random selection rule inside the neighborhood • 需要进行多次家访 Will require revisits

17. 医院或诊所病人作为对照Persons coming to the same hospital and clinic as a case are possible controls. • 易得到、易面访 Easy to obtain and interview • 如果对照也发病，比较容易发现 If they develop the disease the probability is high that they would be detected • 对照与病例之间暴露机会可能不同 Can have different exposures than study base • 可能会有其他的暴露、或因治疗而受到保护 Can have other exposures and protective effects from treatment and management

18. 某特殊危险因素在病例和对照间的分布可能迥异 The control can differ from the base population by special risk factors 对照可能是人群中一个特殊群组。这种选择性对研究的影响无法预料 These controls are a very special subgroup of the study base. Their effect is not predictable

19. 其他“与疾病有关”的对照也有同样问题 Other “disease dependent” control selection have similar problems • 阴性实验室结果 Negative laboratory tests • 监测系统中的其他疾病病例 Other diseases detected in surveillance

20. 时间上的可比性：举例Time comparability: Example • 病例对照研究，调查医院中败血症的危险因素 Case-control study examining risk factors for septicaemia in a hospital • 怀疑中心静脉导管是危险因素 Central venous line is a suspected risk factor • 若某位被感染的医护人员是感染来源，则暴露会根据该医护人员是否上班发生时间变异 If an infected health care worker is a source of infection, the effect of exposure (central line) will vary over time (presence of the health care worker)

21. 食源性爆发常常发生如下情况 The following situation often arises from foodborne outbreaks

22. 若用同一组病例和对照对10月1日午餐进行研究，会发生什么情况？ What if you use the same cases and controls for a food for lunch on 1/10? • 这样，午餐中每一种食物，都有15%的病例和60%的对照没有吃过 For any individual food that I evaluate 15% of cases and 60% of controls were not present to eat it. • 他们没有暴露机会！ There was no exposure opportunity!

23. 对1月10日午餐B食物进行病例对照研究 Case-control study on Food B at lunch on 1/10 Food B If I include these unexposed cases and controls, the OR will be incorrect and high If I exclude them, I may not have enough controls.

24. 如果不小心，可能发生如下情况 This is what happens if one does not take care Food B If I include these unexposed cases and controls, the OR will be incorrect and high If I exclude them, I may not have enough controls.

25. 如果不小心，可能发生如下情况 This is what happens if one does not take care Food B If I include these unexposed cases and controls, the OR will be incorrect and high If I exclude them, I may not have enough controls.

26. 如果不小心，可能发生如下情况 This is what happens if one does not take care 3.5 Food B • 若把没有暴露机会（未吃1/10午餐）者加进去，可能高估ORIf we include these unexposed cases and controls, the OR will be incorrect and high • 若把他们去掉，对照数目可能不够 If we exclude them, I may not have enough controls.

27. 对左旋色氨酸进行序列研究 Sequential studies in assessment of L-tryptophan in the United States 嗜酸性粒细胞增多-肌痛综合征 ，1989 Eosinophilia-myalgia syndrome, USA, 1989 • 问题 Question: 原因是什么？ Cause of syndrome?研究人群 Study base:全人群，分年龄-性别Age-sex band of population • 问题 Question:所有品牌都有危险？All brands of tryptophan?研究人群Study base:左旋色氨酸使用者Tryptophan users • 问题 Question:X品牌所有批次都有问题？ All lots of brand X?研究人群 Study base: 使用X品牌者 Brand X tryptophan users

28. 需要为每名病例选择 >1名对照吗？Should you select > 1 control per case • 病例数比对照数重要 Case numbers are more important • 为每名病例选择≥ 1名对照可以增加统计效力 ≥ 1 control per case can increase statistical power • ≥ 4名对照时，统计效力增加得很有限 ≥ 4 does not improve power • 5-6名对照在病例数很少时可能有帮助 5 to 6 helps with very low (≤ 10) case numbers

29. ≥2个对照组常常不好 ≥2 control groups to test the same hypothesis is usually a poor idea. 可以考虑采用序列假设 Testing sequential hypotheses is better

30. 某病在一所学校爆发，研究者决定选择临近一所学校学生作对照。For a disease in a school, students from a nearby, similar school are used for controls. • 这种方法选择对照如何？Is this a good choice for a control group? • 应该如何选择？ What is the proper choice? • 如果这所学校所有学生都暴露怎么办？ Suppose every student was exposed? What do you do?

31. 猪链球菌感染病人的病例定义如下。如何选择对照？Streptococcus suis case definition, what is your control group? 临床诊断病人+接触病猪或死猪 Clinical disease + exposure to a sick or dying pig 研究问题：接触病猪或死猪的RR或OR是多少？ Question: What is the relative risk (OR) of disease for contact with a sick or dying pig?

32. 这种情况下的2X2表 Here is what your 2X2 table will look like

33. 猪链球菌感染病人的病例定义如下。如何选择对照？Streptococcus suis case definition, what is your control group? 临床诊断病人+接触病猪或死猪 Clinical disease + exposure to a sick or dying pig 研究问题：何种暴露是危险因素？ Question: What type of exposure is a risk factor for infection?

34. 病例和对照的暴露时间相同 Cases and controls are examined for the same referent exposure period • 病例在某一时间段内暴露 Cases were exposed during the referent exposure period • 病例和对照的暴露时间段必须相同 Referent exposure period must be identical for cases and controls • 时间长短相同 Same duration • 时间段相同 Same time frame

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36. Extra Slides

37. A disease in a school 40/class. How do you choose controls? Mean = 2 cases/class

38. Expected cases under random (Poisson) distribution per class – mean = 2/class