Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Department of Lymphoma/Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Bioinformatics. 2022 Sep 2;38(17):4252-4254. doi: 10.1093/bioinformatics/btac461.
In the era of big data, machine learning techniques are widely applied to every area in biomedical research including survival analysis. It is well recognized that censoring, which is a common missing issue in survival time data, hampers the direct usage of these machine learning techniques. Here, we present CondiS, a web toolkit with graphical user interface to help impute the survival times for censored observations and predict the survival times for future enrolled patients. CondiS imputes a censored survival time based on its distribution conditional on its observed part. When covariates are available, CondiS-X incorporates this information to further increase the imputation accuracy. Users can also upload data of newly enrolled patients and predict their survival times. As the first web-app tool with an imputation function for censored lifetime data, CondiS web can facilitate conducting survival analysis with machine learning approaches.
CondiS is an open-source application implemented with Shiny in R, available free at: https://biostatistics.mdanderson.org/shinyapps/CondiS/.
Supplementary data are available at Bioinformatics online.
在大数据时代，机器学习技术被广泛应用于生物医学研究的各个领域，包括生存分析。众所周知，删失是生存时间数据中常见的缺失问题，这限制了这些机器学习技术的直接使用。在这里，我们提出了 CondiS，这是一个带有图形用户界面的网络工具包，用于帮助对删失观察值进行生存时间推断，并预测未来入组患者的生存时间。CondiS 根据其观察部分的分布条件推断删失的生存时间。当存在协变量时，CondiS-X 会纳入这些信息以进一步提高推断准确性。用户还可以上传新入组患者的数据并预测他们的生存时间。作为第一个具有删失寿命数据推断功能的网络应用程序工具，CondiS 网络可以方便地使用机器学习方法进行生存分析。
CondiS 是一个用 R 中的 Shiny 实现的开源应用程序，可免费在 https://biostatistics.mdanderson.org/shinyapps/CondiS/ 上获得。
补充数据可在生物信息学在线获得。
