基于真实世界数据评价治疗结局研究的统计分析技术规范
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摘要
在医疗卫生领域,利用真实世界数据生成真实世界证据的研究已经在世界范围内引起了广泛关注,基于真实世界数据评价治疗结局是其重要的组成部分。然而,目前针对如何在此类研究中设计并实施合理的统计分析,还存在一定的疑惑。为此,作为中国真实世界数据与研究联盟(ChinaREAL)工作组制定的真实世界研究系列技术规范4,本技术规范详述了真实世界数据评价治疗结局研究中进行统计分析时需要注意的事项。在本技术规范中,首先强调预先设计统计分析计划的重要性,推荐基于研究型数据库评价治疗结局研究的统计分析计划的主要内容。其次,阐述了在此类研究中关于研究样本量计算的注意事项及如何合理解读统计分析中的P值等问题。再次,针对此类研究中常见的人群选择偏倚、信息偏倚以及混杂偏倚,推荐相应的统计分析策略,涵盖了目前应用广泛的多变量回归模型以及新兴的因果推断模型,对基于既有数据库的研究中普遍存在的缺失数据给出了相应的指导方针。最后,明确了此类研究统计报告中应包含的核心内容。
Research of generating real-world evidence using real world data has attracted considerable attention globally. Outcome research of treatment based on existing health and medical data or registries has become one of the most important topics. However, there exists certain confusions in this line of research on how to design and implement appropriate statistical analysis. Therefore, in the fourth chapter of the series technical guidance to develop real world evidence by China REal world data and studies Alliance(ChinaREAL), we aim to provide an guidance on statistical analysis in the study to assess therapeutic outcomes based on existing health and medical data or registries.In this chapter,we first emphasize the significance of pre-specified statistical analysis plan, recommending key components of the statistical analysis plan. We then summarize the issue of sample size calculation in this content and clarify the interpretation of statistical p-value. Secondly, we recommend procedures to be considered to tackle the issue related to the selection bias, information bias and most importantly, confounding bias. We discuss the multivariable regression analysis as well as the popular causal inference models. We also suggest that careful consideration should be made to deal with missing data in real-world databases. Finally, we list core content of the statistical report.
Research of generating real-world evidence using real world data has attracted considerable attention globally. Outcome research of treatment based on existing health and medical data or registries has become one of the most important topics. However, there exists certain confusions in this line of research on how to design and implement appropriate statistical analysis. Therefore, in the fourth chapter of the series technical guidance to develop real world evidence by China REal world data and studies Alliance(ChinaREAL), we aim to provide an guidance on statistical analysis in the study to assess therapeutic outcomes based on existing health and medical data or registries.In this chapter,we first emphasize the significance of pre-specified statistical analysis plan, recommending key components of the statistical analysis plan. We then summarize the issue of sample size calculation in this content and clarify the interpretation of statistical p-value. Secondly, we recommend procedures to be considered to tackle the issue related to the selection bias, information bias and most importantly, confounding bias. We discuss the multivariable regression analysis as well as the popular causal inference models. We also suggest that careful consideration should be made to deal with missing data in real-world databases. Finally, we list core content of the statistical report.
引文
1Gamble C, Krishan A, Stocken D, et al. Guidelines for the content of statistical analysis plans in clinical trials. JAMA, 2017, 318(23):2337-2343.
2 Wasserstein RL, Lazar NA. The ASA's statement on p-values:context, process, and purpose. American Statistician, 2016, 70(2):129-131.
3詹思延.流行病学(第8版).北京:人民卫生出版社, 2017.
4Wood AM, White I, Thompson SG, et al. Regression dilution methods for meta-analysis:assessing long-term variability in plasma fibrinogen among 27 247 adults in 15 prospective studies.Int J Epidemiol, 2006, 35(6):1570-1578.
5 AHRQ methods for effective health care. In:Velentgas P, Dreyer NA, Nourjah P, Smith SR, Torchia MM, editors. Developing a protocol for observational comparative effectiveness research:a user's guide. Rockville(MD):Agency for Healthcare Research and Quality(US), 2013.
6Hernán MA, Robins JM(2019). Causal inference. Boca Raton:Chapman&Hall/CRC, forthcoming.
7 D'Agostino RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.Stat Med, 1998, 17(19):2265-2281.
8Yue LQ. Regulatory considerations in the design of comparative observational studies using propensity scores. J Biopharm Stat,2012, 22(6):1272-1279.
9 Kurth T, Walker AM, Glynn RJ, et al. Results of multivariable logistic regression, propensity matching, propensity adjustment,and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol, 2006, 163(3):262-270.
10Little RJ, D'Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med, 2012,367(14):1355-1360.
11 National Research Council Panel on handling missing data in clinical trial. The prevention and treatment of missing data in clinical trials. Washington(DC):National Academies Press(US)Copyright 2010 by the National Academy of Sciences. All rights reserved, 2010.
12von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology(STROBE)statement:guidelines for reporting observational studies. Lancet,2007, 370(9596):1453-1457.
13 聂晓路,彭晓霞.使用常规收集卫生数据开展观察性研究的报告规范-RECORD规范.中国循证医学杂志, 2017, 17(4):475-487.
2 Wasserstein RL, Lazar NA. The ASA's statement on p-values:context, process, and purpose. American Statistician, 2016, 70(2):129-131.
3詹思延.流行病学(第8版).北京:人民卫生出版社, 2017.
4Wood AM, White I, Thompson SG, et al. Regression dilution methods for meta-analysis:assessing long-term variability in plasma fibrinogen among 27 247 adults in 15 prospective studies.Int J Epidemiol, 2006, 35(6):1570-1578.
5 AHRQ methods for effective health care. In:Velentgas P, Dreyer NA, Nourjah P, Smith SR, Torchia MM, editors. Developing a protocol for observational comparative effectiveness research:a user's guide. Rockville(MD):Agency for Healthcare Research and Quality(US), 2013.
6Hernán MA, Robins JM(2019). Causal inference. Boca Raton:Chapman&Hall/CRC, forthcoming.
7 D'Agostino RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.Stat Med, 1998, 17(19):2265-2281.
8Yue LQ. Regulatory considerations in the design of comparative observational studies using propensity scores. J Biopharm Stat,2012, 22(6):1272-1279.
9 Kurth T, Walker AM, Glynn RJ, et al. Results of multivariable logistic regression, propensity matching, propensity adjustment,and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol, 2006, 163(3):262-270.
10Little RJ, D'Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med, 2012,367(14):1355-1360.
11 National Research Council Panel on handling missing data in clinical trial. The prevention and treatment of missing data in clinical trials. Washington(DC):National Academies Press(US)Copyright 2010 by the National Academy of Sciences. All rights reserved, 2010.
12von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology(STROBE)statement:guidelines for reporting observational studies. Lancet,2007, 370(9596):1453-1457.
13 聂晓路,彭晓霞.使用常规收集卫生数据开展观察性研究的报告规范-RECORD规范.中国循证医学杂志, 2017, 17(4):475-487.
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