临床观察研究设计.pdf

临床观察性研究中的临床观察性研究中的 若干方法学问题和实例分享若干方法学问题和实例分享 Min Yao, MD, MPH Clinical Scientist Boston University Medical Center Department of Surgery Boston, MA minyao99内容内容 ?临床观察性研究临床观察性研究: 真实世界真实世界(real world)研究研究 ?观察性研究中控制混淆因子的基本方法观察性研究中控制混淆因子的基本方法 概述概述 举例举例：多因素相关回归多因素相关回归 举例举例：倾向性得分配对法倾向性得分配对法 ?临床事件风险预测研究临床事件风险预测研究 ?卫生经济学研究卫生经济学研究 ? Science tells us what we can do; ? Guidelines tells we should do; ? Observational study tells what we are actually doing 临床医学研究的分类临床医学研究的分类 临临 床床 研研 究究 实验性研究实验性研究 Experimental studies 观察性研究观察性研究 Observational studies 临床试验临床试验 ? 横断面研究横断面研究 ? 队列研究队列研究 ? 病例对照研究病例对照研究 观察性研究的定义与目的观察性研究的定义与目的 ?定义定义 不对研究对象施加任何外部干预而获得的事 实材料的研究方法。record as it is 目的目的 了解在真实世界 (real world) 或者现实条件下疾病原因、 发病机理、分布、发生、诊断、治疗、预后等等。 临床观察性研究的三种常用方法临床观察性研究的三种常用方法 病例-对照研究(case-control study) 队列研究(cohort study) 横断面/现况研究 (cross-sectional study) 观察性临床研究的类型观察性临床研究的类型 病例-对照研究(Case-Control Study) 肺癌肺癌 非患者非患者 吸烟 非吸烟 时间时间 观察性临床研究的类型观察性临床研究的类型 队列研究队列研究(Cohort Study)(Cohort Study) 肺癌肺癌 非患者非患者 吸烟 非吸烟 时间时间 观察性临床研究的类型观察性临床研究的类型 横断面研究(Cross Sectional Study) 流行病调查/普查(survey) 时间时间 时 间 点 时 间 点 混杂因素的控制方法混杂因素的控制方法 Control for Confounding 1、随机化随机化 Randomization 2、限制限制 Restriction 3、配对配对Matching 4、分层分层 Stratification 5、多元回归分析法多元回归分析法 Multiple Regression 或者上述数个方法的联合应用或者上述数个方法的联合应用 混杂因素的控制方法可用在混杂因素的控制方法可用在 研究设计和数据统计阶段研究设计和数据统计阶段 研研 究究 设设 计计 数据数据 统计统计 研究实行研究实行 随机化随机化 配对配对 分层分层 限制限制 配对配对 分层分层 限制限制 多元分析回归多元分析回归 偏离真实结果的数据偏离真实结果的数据 混淆因素混淆因素 有偏倚的结论有偏倚的结论 混杂因素的控制方法之一混杂因素的控制方法之一 多元回归分析法多元回归分析法 一个医学事件的发生往往是多个因素（变量）共同作用 的结果： 如果以血压为例，在我们评价某个抗高血压药物对血压的影 响，必须同时考虑病人的年龄、性别、饮食、运动、是否有 糖尿病史、同时服用的其他药物等等诸多因素影响。 多元回归分析法是研究多个随机变量之间的相互依赖关系以 及内在规律性的统计学方法。 通过一系列复杂的数学运算，揭示各个变量与事件的关 系。 多元回归统计方法的选择多元回归统计方法的选择 条件条件方法方法 Continue 连续性指标连续性指标 血压下降血压下降，减少体重减少体重，胆固醇降低胆固醇降低 Multiple linear regression 多元线性模型多元线性模型 Dichotomous, 二分类指标二分类指标 有效率有效率、治愈率治愈率、死亡率死亡率 Multiple logistic regression 多元多元Logistic回归模型回归模型 Time-to-even 生存时间生存时间 开始治疗至事件发生开始治疗至事件发生(死亡死亡)的时间的时间 Cox proportional hazards model COX比例风险模型比例风险模型 A Pragmatic Retrospective Cohort Study Evaluating Clinical Outcomes in High Risk Patients with Chronic Lower Extremity Ulcers Treated with Negative Pressure Wound Therapy Min Yao, Matteo Fabbi, Hisae Hayashi, Nanjin Park, Khaled Attala, Michael A French and Vickie R. Driver Presenter: Min Yao MD, MPH Clinical Research Scientist Limb Preservation and Wound Care Department of Surgery Boston University Medical Center Boston, MA 02118 ?NPWT is used in the treatment of chronic, acute, sub-acute, traumatic, wounds with different etiology: diabetic, peripheral arterial disease, pressure, surgical and burn ?Mechanisms of action for NPWT ?Providing a moist, closed wound healing environment ?Drawing wound edges together ?Removing infectious materials and fluids ?Reducing edema ?Promoting tissue perfusion and formation, which Prepare wound bed for closure by secondary intention. BackgroundBackground Negative Pressure Wound Therapy (NPWT) Portal Unit Objectives 1.To evaluate efficacy of Negative Pressure Wound Therapy (NPWT) on wound healing in patients with chronic lower extremity ulcers and multiple co-morbidities across a real world continuum of care settings. 2.To determine the effectiveness of early advanced care for patients treated with and without NPWT Study DesignStudy Design This is a retrospective cohort study Subjects were divided into two groups based on exposure to NPWT: Treatment group: Wounds treated with NPWT Control group: Wounds not treated with NPWT Two groups are matched on wound size and surgical incision and drainage (I/D) Study Design Inclusion/Exclusion Criteria Lower extremity ulcers with and without NPWT were included in this study (2002-2010). Patients with following conditions were excluded from the study: HIV positive Active malignancy with chemotherapy Less than 18 years old More than 80 years old Traumatic wounds due to motor vehicle accidents Burn ulcers Sickle cell disease On steroid Data Source Boston University Medical Center Electronic Medical Record (EMR) Data Retrieved from Boston Medical Center Electronic Data Warehouse for the period of 2002 to 2010. Data CaptureData Capture Electronic data capture by database query Demographics Diagnostic code (ICD-9) Inpatient and outpatient procedures (CPT code) Hospitalizations (room and board) Clinic visits (room and board Meds Lab Manual data capture by chart review History of ulcer Start date/end date of index ulcers Size and grade of index ulcers Duration of NWPT application History of amputation . Clinic Data Capture and Conversion Electronic Data Collection Room and Board Med ICD-9/CPT Lab Radiology Microsoft Access SAS Manual Data Capture by Chart Review Ulcer onset Ulcer start date and end date Size and grade Duration of NWPT Statistical Analysis Table1- Patient Characteristics Is wound healing only affected by Negative Is wound healing only affected by Negative Pressure Wound Technology (NPWT) or not ?Pressure Wound Technology (NPWT) or not ? Age BMI Diabetes Chronic Kidney Disease Chronic Liver Disease Peripheral vascular disease Anemia Hypoalbuminemia Infection Duration of ulcers Stage of Ulcers History of Amputation Unadjusted HR * (95% CI)Adjusted HR* (95% CI) All ulcers2.25 (1.73-3.96)2.63 (1.87-3.70) Diabetic ulcers Wagner 1-21.85 (1.26-2.69)2.73 (1.61-4.62) Wagner 3-42.54 (1.77-3.65)2.74 (1.78-4.21) Type of Ulcers Diabetic ulcers2.38 (1.75-3.23)3.26 (2.21-4.83) Arterial ulcers2.33 (1.57-3.48)2.27 (1.56-3.78) Venous ulcers4.90 (1.72-13.59)6.31 (1.49-26.6) Pressure ulcers2.19 (1.03-4.66)1.72 (0.43-6.95) Initiation of NWPT after ulcer onset (month) Unadjusted HR (95% CI) Adjusted HR (95% CI) P value 0-33.11 (1.58-6.12)3.38 (1.68-6.82)0.05 Duodenum, n (%)25 (29.6)33 (50.7) Intestine+Colon, n (%)33 (52.5)30 (40) Pancreatic , n (%)5 (7.9)7 (9.3) Fistula tract length (cm), mean (std)5.8 (2.3)5.9 (3.0)0.027 Fistula output (ml/day), mean (std)76.3 (43.4)90.7 (57.3)0.025 Fistula duration (day), mean (std)44.8 (46.1)81.6 (73.7)0.002 Co-morbidities coronary heart disease, n (%)2 (3.7)1 (1.3)0.05 chronic kidney disease, n (%)0 (0)0 (0)0.05 liver cirrhosis, n (%)1 (1.6)1 (1.3)0.05 diabetes, n (%)11 (7.5)5 (6.7)0.05 anemia, n (%)42 (66.7)50 (66.7)0.05 cancer, n (%)12 (19.1)20 (26.7)0.05 smoking, n (%)21 (33.3)20 (26.7)0.05 achohole29 (46.0)22 (29.3)0.05 Baseline Lab ALT29.6 (20.2)39.5 (27.8)0.011 AST29.0 (20.2)33.8 (21.0)0.05 Hb109.9 (16.7)114.1 (14.8)0.05 Albumin36.4 (3.8)37.5 (3.5)0.05 pre Albumin221.6 (57.6)234.9 (83.1)0.003 Total Bilirubin22.6 (25.5)23.9 (31.5)0.05 C Reactive Protein14.3 (26.9)12.9 (15.1)0.001 Medication growth hormone, n (%)20 (31.8)37 (49.3)0.035 somatostatin, n (%)40 (63.5)65 (86.7)0.008 全部病例全部病例 组间不平衡的解决办法之一组间不平衡的解决办法之一 随机和配对随机和配对 组间不平衡组间不平衡： 混淆因素混淆因素结果偏差结果偏差 随机抽样随机抽样： 用于前瞻性临床试验用于前瞻性临床试验 配对配对：用于观察性研究用于观察性研究-回顾性研究回顾性研究 问题问题：多个因素同时配对时多个因素同时配对时， 会产生找不到会产生找不到“对对 象象”的问题的问题 倾向性得分倾向性得分 Propensity Score （PS） 随机研究随机研究：某个病人被分配到治疗组或对照组的概率是相同的某个病人被分配到治疗组或对照组的概率是相同的 （50%）。）。 非随机研究非随机研究：某个病人被分配到治疗组或对照组的概率是不相某个病人被分配到治疗组或对照组的概率是不相 同的同的， 是有倾向性是有倾向性 (propensity)的的。 倾向性得分倾向性得分(propensity score)是在确定病人的基线是在确定病人的基线 (baseline)特特 征后征后， 该病人进入治疗组概率值该病人进入治疗组概率值。 避免用多个变量同时配对时是找不到避免用多个变量同时配对时是找不到对象对象的问题的问题。 A和和B两个病人有相同的概率两个病人有相同的概率（P=0.25） 进入治疗组或进入治疗组或 对照组对照组 非常有幸地是非常有幸地是： A进入了治疗组进入了治疗组， B却进入了对照组却进入了对照组 把把A与与B配对配对， 就称之为就称之为： Propensity Score Matching 倾向性得分倾向性得分 Propensity Score PS Matching CABG Treatment PS PCI Control PS 10010.12020010.120 10020.22020020.220 10030.23420030.248 10040.24820040.276 10050.26220050.304 10060.27620060.332 10070.29020070.360 10080.30420080.388 10090.31820090.416 10100.33220100.444 10110.34620110.472 10120.36020120.500 10130.37420130.528 10140.38820140.556 10150.40220150.584 倾向性得分倾向性得分 Propensity Score PS Matching 8 8 倾向性得分倾向性得分 Propensity Score PS Matching PCI Control CABG Treatment Control PS Treatment PS 似乎是回顾性地按进入治疗组似乎是回顾性地按进入治疗组（或对照组或对照组）的概率的积分重的概率的积分重 新挑选病人新挑选病人- 使病人在基线变量使病人在基线变量（例如例如：年龄年龄、性别性别、BMI、 瘘管的位置瘘管的位置、瘘管类型瘘管类型、瘘管存在时间瘘管存在时间、每天流量每天流量、糖尿病糖尿病、 感染感染、营养状况等等众多因素营养状况等等众多因素) 上进入治疗组上进入治疗组（或对照组或对照组） 的概率值相等或近似的概率值相等或近似 倾向性得分配对倾向性得分配对 PS PS 的计算方法的计算方法 proc logistic data=fistula_healing descending; model treat_group= age gender BMI fistula_age fistula_length fistula_output fistula_location pre_alb DM2SSI ; output out = propensity_scores pred = prob_treat_group; run; 常规非手术常规非手术 63 cases 富含血小板血浆富含血小板血浆 纤维胶纤维胶 75 cases 常规非手术常规非手术 PS 28 cases PRPFG PS 28 cases 基于倾向性得分配对基于倾向性得分配对 Propensity Score Matching PS-matched cohort (Patients Matched on Propensity Score) control (n=28)Treatment (n=28)P-value Demographics Age, mean (std)50.548.40.05 Gender0.05 Male, n (%)21 (75.0)64(85.3) Female, n (%)16 (25.0)6 (14.7) BMI, mean (std)20.9 (2.9)20.5 (3.1)0.05 Fistula Fistula location0.05 Duodenum, n (%)12 (42.9)12 (42.9) Intestine+Colon, n (%)14 (50.0)14 (50.0) Pancreatic , n (%)2 (7.1)2 (7.1) Fistula tract length (cm), mean (std)6.2 (2.1)6.3 (3.7)0.05 Fistula output (ml/day), mean (std)84.9 (47.7)89.3 (54.2)0.05 Fistula duration (day), mean (std)49.6 (52.8)46.5 (48.0)0.05 Co-morbidities coronary heart disease, n (%)0 (0)1 (3.57)0.05 chronic kidney disease, n (%)0 (0)0 (0)0.05 liver cirrhosis, n (%)1 (3.6)1 (3.6)0.05 diabetes, n (%)4 (14.3)2 (7.1)0.05 anemia, n (%)22(78.5)20 (71.4)0.05 cancer, n (%)10 (35.7)12 (42.9)0.05 smoking, n (%)8 (28.6)7 (25.0)0.05 achohole7 (25.0)8 (28.57)0.05 Baseline Lab ALT28.6 (19.5)29.7 (16.4)0.05 AST28.4 (18.9)31.7 (16.5)0.05 Hb109.9 (19.2)111 (15.7)0.05 Albumin37.0 (3.7)37.7 (3.6)0.05 pre Albumin225.2 (72.9)221.6 (85.4)0.05 Total Bilirubin23.7 (24.5)24.5 (30.6)0.05 C Reactive Protein15.7 (13.1)13.6 (14.9)0.05 Medication growth hormone, n (%)8 (28.6)9 (32.1)0.05 somatostatin, n (%)23 (82.1)23 (82.1)0.05 PS-配对病例配对病例 Initial Cohort (Total Patients Enrolled) PS-matched cohort (Patients Matched on Propensity Score) Control (n=63) Treatment (n=75)P-value control (n=28) Treatmen t (n=28) P-value Demographics Age, mean (std) 46.6 (13.6)59.2 (15.6) 0.00150.548.40.05 Gender0.050.05 Male, n (%)47 (74.6)64 (85.3) 21 (75.0)64(85.3) Female, n (%)16 (25.4)11 (14.7) 16 (25.0)6 (14.7) BMI, mean (std) 22.3 (13.3)20.5 (3.1) 0.01120.9 (2.9)20.5 (3.1)0.05 Fistula Fistula location0.050.05 Duodenum, n (%)25 (29.6)33 (50.7)12 (42.9)12 (42.9) Intestine+Colon, n (%)33 (52.5)30 (40)14 (50.0)14 (50.0) Pancreatic , n (%)5 (7.9)7 (9.3)2 (7.1)2 (7.1) Fistula tract length (cm), mean (std)5.8 (2.3)5.9 (3.0)0.0276.2 (2.1)6.3 (3.7)0.05 Fistula output (ml/day), mean (std)76.3 (43.4)90.7 (57.3)0.02584.9 (47.7)89.3 (54.2)0.05 Fistula duration (day), mean (std)44.8 (46.1)81.6 (73.7)0.00249.6 (52.8)46.5 (48.0)0.05 Co-morbidities coronary heart disease, n (%)2 (3.7)1 (1.3)0.050 (0)1 (3.57)0.05 chronic kidney disease, n (%)0 (0)0 (0)0.050 (0)0 (0)0.05 liver cirrhosis, n (%)1 (1.6)1 (1.3)0.051 (3.6)1 (3.6)0.05 diabetes, n (%)11 (7.5)5 (6.7)0.054 (14.3)2 (7.1)0.05 anemia, n (%)42 (66.7)50 (66.7)0.0522(78.5)20 (71.4)0.05 cancer, n (%)12 (19.1)20 (26.7)0.0510 (35.7)12 (42.9)0.05 smoking, n (%)21 (33.3)20 (26.7)0.058 (28.6)7 (25.0)0.05 achohole29 (46.0)22 (29.3)0.057 (25.0)8 (28.57)0.05 Baseline Lab ALT29.6 (20.2)39.5 (27.8)0.01128.6 (19.5)29.7 (16.4)0.05 AST29.0 (20.2)33.8 (21.0)0.0528.4 (18.9)31.7 (16.5)0.05 Hb109.9 (16.7)114.1 (14.8)0.05109.9 (19.2)111 (15.7)0.05 Albumin36.4 (3.8)37.5 (3.5)0.0537.0 (3.7)37.7 (3.6)0.05 pre Albumin221.6 (57.6)234.9 (83.1)0.003225.2 (72.9)221.6 (85.4)0.05 Total Bilirubin22.6 (25.5)23.9 (31.5)0.0523.7 (24.5)24.5 (30.6)0.05 C Reactive Protein14.3 (26.9)12.9 (15.1)0.00115.7 (13.1)13.6 (14.9)0.05 Medication growth hormone, n (%)20 (31.8)37 (49.3)0.0358 (28.6)9 (32.1)0.05 somatostatin, n (%)40 (63.5)65 (86.7)0.00823 (82.1)23 (82.1)0.05 Cumulative incidence of fistula closure within 28 days Cumulative incidence of fistula closure within 28 days between two groups (logbetween two groups (log- -rank test P0.0001) rank test P1000room temperature ( (h) 1 12h ) * + , 1 1=Yes2=No - . 1 1=passive2=activenon-fored air / 0 1 2 3 (C0)36.5 Continue 4 5 6 7 3 8 (C0)26 Continue 预计出现术中低体温的概率0.64774688 Exp0.543812914 Dr Huang-PUMCH Risk Index for predication of Intraoperative Hypothermia ? 9 : 建立预测模型的方法学建立预测模型的方法学 ?两个独立获得的取自于同一人群的样本两个独立获得的取自于同一人群的样本 （全国全国 vs. 北京北京） ?通过经验通过经验、文献和统计方法筛选出与事件发生显著相关的因素文献和统计方法筛选出与事件发生显著相关的因素 ?利用其中一个样本建立回归方程利用其中一个样本建立回归方程， 方程内自变量的由导致事件发生的方程内自变量的由导致事件发生的 危险因素组成危险因素组成 ?将第二个样本的危险因素代入该回归方程将第二个样本的危险因素代入该回归方程， 计算得到事件发生的预测计算得到事件发生的预测 值值 ?将预测值与实际值比较将预测值与实际值比较，判断它们两者的吻合程度判断它们两者的吻合程度 (goodness of fit) ?C statistics 的范围的范围 0.5 to 1.0 ?C=0.5: 最差预测最差预测 ?C=0.5-0.7 一般一般 ?C=0.7-0.8 好好 ?C=0.8-1.0 （不包括不包括1.0) 非常好非常好 ?C=1.0: 最佳预测最佳预测 Health Technologies: 4 hurdles over 10+ years journey Olympic Game: 10 hurdles for 110 metersVS Evaluating Health TechnologiesEvaluating Health Technologies Safety: Does it harm? Efficacy: Can it work well under ideal circumstances (clinical trial setting)? Effectiveness: Does it work well under real-world circumstances (clinical practice setting)? Eff