应用实例分析临床流行病学研究 胎源性疾病 (dohad).ppt

应用实例分析应用实例分析 -临床流行病学研究临床流行病学研究 胎源性疾病胎源性疾病（DOHAD)学习目标学习目标n通过应用实例分析通过应用实例分析，加深对流行病学重要基本概念的理解加深对流行病学重要基本概念的理解n通过应用实例分析通过应用实例分析，提高对流行病学研究重要方法的应用能力提高对流行病学研究重要方法的应用能力内容内容n实例分析实例分析 一个经典的有关胎源性疾病的临床流行病学研究报告一个经典的有关胎源性疾病的临床流行病学研究报告n实例分析实例分析 -设计一个有关胎源性疾病的临床流行病学研究项目设计一个有关胎源性疾病的临床流行病学研究项目胎源性疾病胎源性疾病 /发育源性疾病发育源性疾病(DOHAD)nThe suscepatible ity to many chronic diseases in adulthood can be traced back to exposures during early life(during fetal and early postnatal life)成年期的许多慢性疾病的易感性成年期的许多慢性疾病的易感性可以追溯到生命早期的暴露因素（胎儿期和出生后早期）可以追溯到生命早期的暴露因素（胎儿期和出生后早期）n无致病基因突变无致病基因突变流行病学流行病学 -重要作用之一重要作用之一 发现新联系发现新联系 （DOHAD）*Odds ratio for two hour glucose concentration of 7.8 mmol/l adjusted for current body mass index.(X2 for trend=15.4;p7.8 mmol/l adjusted for current body mass index.出生体重（出生体重（lb)糖耐量异常糖耐量异常 n/N（%）Adjusted*ORCrude ORCrudeRR低于 5.5 lb(2.5kg)8/20(40%)6.6?5.5-6.4 (2.9 kg)16/47(34%)4.8?6.5-7.4 (3.4 kg)32/104(31%)4.6?7.5-8.4 (3.9 kg)26/117(22%)2.6?8.5-9.4 (4.2 kg)7/54(13%)1.4?9.5+lb (4.3 kg+)4/28(14%)1.01.01.0Answers to Questions（1）What is the study design 研究设计是？研究设计是？Retrospective cohort study 回顾性队列研究回顾性队列研究Why OR?why not RR?Either good for reasoning,the latter is more accurate in defining the relative risk disparity.为什么用比值为什么用比值比（比（OR）？而不是相对风险度）？而不是相对风险度（RR？推理任一均可，推理任一均可，RR在定义相在定义相对风险差距更准确。对风险差距更准确。Can we have crude OR and crude RR?Yes,in cohort studies or RCT,you can calculate RR.我们可以计算粗我们可以计算粗OR（未调整比值比）（未调整比值比）,粗粗RR（未调整相对风险度（未调整相对风险度）吗？）吗？是的，在队列研究或试验，可以计是的，在队列研究或试验，可以计算出算出RR。Why adjusted OR?Why not adjusted RR?Either is good for reasoning,it is easier to calculate the adjusted OR.为什么是调整为什么是调整OR？而不是调整而不是调整RR？推理任一均可，调整？推理任一均可，调整OR更容易计算更容易计算.For cohort study data,you can use log binomial model,to obtain the adjusted RR.队列研究的数据，您可以使用 log 二项式模型，得到调整后的RR。How to calculate adjusted RR,in this study?在本研究中，在本研究中，如何计算调整后的如何计算调整后的RR？Answers to Questions（2）Do we need adjustments in OR or RR?Most times,yes.我们需要我们需要调整吗？大多数时候，是的。调整吗？大多数时候，是的。Is adjustments always necessary?No,sometimes unnecessary.调整是必要的吗？不，有时不必要。调整是必要的吗？不，有时不必要。What the difference Between OR and RR?OR does not always represent RR.OR can be calculated in any study designs,RRs can not be calculated directly in case control studies.OR和和RR有有什么区别？什么区别？OR 有时不能代表有时不能代表RR。可以在任何临床流行病学研究设计。可以在任何临床流行病学研究设计中计算中计算OR。在病例对照研究不能直接计算。在病例对照研究不能直接计算RR。Whats the difference between Crude OR vs.Adjusted OR?The adjusted OR more often(but not always)represents the true association。未调整。未调整OR或与调整或与调整OR区别是什么？调整区别是什么？调整OR更经常更经常（但并不总是）代表真正的联系。（但并不总是）代表真正的联系。Whats the difference between Crude RR vs.Adjusted RR?调整调整RR更经常（但并不总是）代表真正的联系。更经常（但并不总是）代表真正的联系。真理？假象？真理？假象？Causal Inference Considerations 因果推理思考因果推理思考Information bias?信息偏倚信息偏倚 less likelyConfounding factors？混杂因素？混杂因素 possiblyConsistency of association?联系的一致性联系的一致性 yesStrength of association?关联强度关联强度 OKDose-response relationship?剂量剂量-反应关系反应关系 yesTemporally order consistent?时间一致性时间一致性 yesDeterministic/probabilistic?决定性决定性 noNecessary?必要性必要性 noSufficient?充分充分 noSpecificity?特异性特异性 noBiological plausibility?生物合理性生物合理性 yesSurrogate risk factor?替代风险因素替代风险因素 may beAnimal model experiment?实验动物模型实验动物模型 yes Birth weight 出生体重出生体重CrudeORAdjusted*OR Crude ORsIs Current BMI a confounder?*adjusted for current body mass index.Confounder or Effect Mediator?混杂因素混杂因素,或影响介质或影响介质?Glucose tolerance 糖耐量 Blood pressure 血压 Current BMI现体重指数现体重指数Birth weight 出生体重 Confounders 混杂因素 (e.g.ethnicity 如种族)When you inappropriate adjust for a factor in the causal pathway,you could produce a false association,or exaggerated association 当你不适当的调整一个在因果通路途径上的因素，你可能会产生一个虚假的关联，或夸张的关联。It may be inappropriate to adjust for current BMI in estimating the effect of birth weight on current glucose tolerance or blood pressure.调整现在的体重指数以估计出生体重对目前的糖耐量或血压的影响可能是不合适的。Reversal paradox 逆转谜题逆转谜题 Why evidence for the fetal origins of adult disease might be a statistical artifact:the reversal paradox for the relation between birth weight and blood pressure in later life.Tu YK,West R,Ellison GT,Gilthorpe MS.Am J Epidemiol;161(1):27-32.Some researchers have recently questioned the validity of associations between birth weight and health in later life.They argue that these associations might be due in part to inappropriate statistical adjustment for variables on the causal pathway(such as current body size),which creates an artifactual statistical effect known as the reversal paradox.Computer simulations were conducted for three hypothetical relations between birth weight and adult blood pressure.The authors examined the effect of statistically adjusting for different correlations between current weight and birth weight and between current weight and adult blood pressure to assess their impact on associations between birth weight and blood pressure.When there was no genuine relation between birth weight and blood pressure,adjustment for current weight created an inverse association whose size depended on the magnitude of the positive correlations between current weight and birth weight and between current weight and bl