The Methods of Comparative Effectiveness Research

被引:108
作者
Sox, Harold C. [1 ]
Goodman, Steven N. [2 ]
机构
[1] Dartmouth Med Sch, Dartmouth Inst Hlth Policy & Clin Practice, Dept Med, Hanover, NH 03755 USA
[2] Stanford Univ, Sch Med, Dept Med & Hlth Res & Policy, Palo Alto, CA 94305 USA
来源
ANNUAL REVIEW OF PUBLIC HEALTH, VOL 33 | 2012年 / 33卷
关键词
propensity score; instrumental variable; sensitivity analysis; treatment-response heterogeneity; clinical prediction rule; CLINICAL-PREDICTION RULES; MEDICAL DECISION-MAKING; CONVENIENT APPROXIMATION; CARDIOVASCULAR RISK; COST-EFFECTIVENESS; RANDOMIZED-TRIALS; LIFE EXPECTANCY; HEALTH; CARE; HETEROGENEITY;
D O I
10.1146/annurev-publhealth-031811-124610
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
This review describes methods used in comparative effectiveness research (CER). The aim of CER is to improve decisions that affect medical care at the levels of both policy and the individual. The key elements of CER are (a) head-to-head comparisons of active treatments, (b) study populations typical of day-to-day clinical practice, and (c) a focus on evidence to inform care tailored to the characteristics of individual patients. These requirements will stress the principal methods of CER: observational research, randomized trials, and decision analysis. Observational studies are especially vulnerable because they use data that directly reflect the decisions made in usual practice. CER will challenge researchers and policy makers to think deeply about how to extract more actionable information from the vast enterprise of the daily practice of medicine. Fortunately, the methods are largely applicable to research in the public health system, which should therefore benefit from the intense interest in CER.
引用
收藏
页码:425 / 445
页数:21
相关论文
共 79 条
[21]   Developing an evidence-based Guide to Community Preventive Services -: Methods [J].
Briss, PA ;
Zaza, S ;
Pappaioanou, M ;
Fielding, J ;
Wright-De Agüero, L ;
Truman, BI ;
Hopkins, DP ;
Mullen, PD ;
Thompson, RS ;
Woolf, SH ;
Carande-Kulis, VG ;
Anderson, L ;
Hinman, AR ;
McQueen, DV ;
Teutsch, SM ;
Harris, JR .
AMERICAN JOURNAL OF PREVENTIVE MEDICINE, 2000, 18 (01) :35-43
[22]   Instrumental variable methods in comparative safety and effectiveness research [J].
Brookhart, M. Alan ;
Rassen, Jeremy A. ;
Schneeweiss, Sebastian .
PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2010, 19 (06) :537-554
[23]   Confounding Control in Healthcare Database Research Challenges and Potential Approaches [J].
Brookhart, M. Alan ;
Sturmer, Til ;
Glynn, Robert J. ;
Rassen, Jeremy ;
Schneeweiss, Sebastian .
MEDICAL CARE, 2010, 48 (06) :S114-S120
[24]  
Centor R M, 1981, Med Decis Making, V1, P239, DOI 10.1177/0272989X8100100304
[25]   The effect of including C-reactive protein in cardiovascular risk prediction models for women [J].
Cook, Nancy R. ;
Buring, Julie E. ;
Ridker, Paul M. .
ANNALS OF INTERNAL MEDICINE, 2006, 145 (01) :21-29
[26]   Advances in Measuring the Effect of Individual Predictors of Cardiovascular Risk: The Role of Reclassification Measures [J].
Cook, Nancy R. ;
Ridker, Paul M. .
ANNALS OF INTERNAL MEDICINE, 2009, 150 (11) :795-W143
[27]   Joint Effects of Common Genetic Variants on the Risk for Type 2 Diabetes in U. S. Men and Women of European Ancestry [J].
Cornelis, Marilyn C. ;
Qi, Lu ;
Zhang, Cuilin ;
Kraft, Peter ;
Manson, JoAnn ;
Cai, Tianxi ;
Hunter, David J. ;
Hu, Frank B. .
ANNALS OF INTERNAL MEDICINE, 2009, 150 (08) :541-W98
[28]   EFFECT OF BYPASS-SURGERY ON SURVIVAL IN PATIENTS IN LOW-RISK AND HIGH-RISK SUBGROUPS DELINEATED BY THE USE OF SIMPLE CLINICAL-VARIABLES [J].
DETRE, K ;
PEDUZZI, P ;
MURPHY, M ;
HULTGREN, H ;
THOMSEN, J ;
OBERMAN, A ;
TAKARO, T .
CIRCULATION, 1981, 63 (06) :1329-1338
[29]   BAYESIAN SUBSET ANALYSIS [J].
DIXON, DO ;
SIMON, R .
BIOMETRICS, 1991, 47 (03) :871-881
[30]  
Eddy D.M., 1980, Screening for cancer: theory, analysis, and design