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.
Citation： GAO Pei, WANG Yang, LUO Jianfeng, REN Yan, HU Ming, TANG Shaowen, HU Hao, SUN Xin, On behalf of China REal world data and studies ALliance (ChinaREAL). Technical guidance for statistical analysis to assess therapeutic outcomes using real-world data. Chinese Journal of Evidence-Based Medicine, 2019, 19(7): 787-793. doi: 10.7507/1672-2531.201904179 Copy