west china medical publishers
Keyword
  • Title
  • Author
  • Keyword
  • Abstract
Advance search
Advance search

Search

find Keyword "Survival data" 6 results
  • The Application of RevMan, Stata and R Software for Meta-analysis of Survival Data

    Meta-analysis of survival data is becoming more and more popular. The data could be extracted from the original literature, such as hazard ratio (HR) and its 95% confidence interval, the difference of actual frequency and theoretical frequency (O - E) and its standard deviation. The data can be used to calculate the combined HR using Review Manager (RevMan), Stata and R softwares. RevMan software is easy to learn, but there are some limitations. Stata and R software are powerful and flexible, and they are able to draw a variety of graphics, however, they need to be programmed to achieve.

    Release date: Export PDF Favorites Scan
  • The correct application of Stata and R software in meta-analysis for survival data

    In systematic reviews and meta-analyses, time-to-event outcomes were mostly analysed using hazard ratios (HR). It was neglected transformation of the data so that some wrong outcomes were gained. This study introduces how to use Stata and R software to calculate the HR correctly if the report presents HR and confidence intervals were gained.

    Release date:2017-10-16 11:25 Export PDF Favorites Scan
  • Accessibility and validation of survival data in survival curve for meta-analysis

    Hazard ratio (HR) is usually regarded as the effect size in survival studies. Meanwhile, it is supposed to be perfect for pooling results in the meta-analysis of survival data. However, it does not function usually due to absence of original data for pooling HR. As a compromise method, entering data from reading Kaplan-Meier curves and follow-up times into the calculation spreadsheet can also be used to obtain related survival data. But related study on the subject is scarce, and opinions are inconsistent. Accordingly, we conduct this study to further illustrate the procedure in details.

    Release date:2019-09-10 02:02 Export PDF Favorites Scan
  • Non-constant proportional hazards network meta-analysis: a case study in R software

    Network meta-analyses (NMA) of survival data often rely on the proportional hazards (PH) assumption, however, this assumption fails when survival curves intersect. With the emergence of innovative therapies such as immunotherapy, the importance of NMA based on non-proportional hazards (non-PH) in the current evidence-based medicine evaluation of oncology drugs has become increasingly prominent. Fractional polynomial (FP) models do not rely on the assumption of PH, which can flexibly capture the characteristics of survival curves, and the corresponding fitting effects are better than those of the PH models. This study introduced a complete workflow in R for NMA using FP models with non-PH.

    Release date: Export PDF Favorites Scan
  • A systematic method for extracting survival data from Kaplan-Meier curve

    Survival data include the occurrence and duration of an event. As most survival data are distributed irregularly, the Kaplan-Meier method is often used in survival analysis; however, studies usually only report the Kaplan-Meier curve and median survival time and do not provide the original survival data, which creates issues for subsequent secondary research. This study introduced a systematic method whereby image processing software and R software were used to process and extract survival data from published Kaplan-Meier curves. It also introduced the specific steps required to obtain survival data using an example to show the accuracy and feasibility of the extraction method and provided references for the effective secondary use of survival data.

    Release date: Export PDF Favorites Scan
  • Matching-adjusted indirect comparison for survival data analysis: implementation in R language

    With the increase in the number of single-arm clinical trials and lack of head-to-head clinical studies, the application of unadjusted indirect comparisons and network meta-analysis methods has been limited. Matching-adjusted indirect comparison (MAIC) is an alternative method to fully utilize individual patient data from one study and balance potential bias caused by baseline characteristics differences in different trials through propensity score matching with aggregated data reported in other studies, and complete the comparison of the efficacy between target interventions. This study introduced the concept and principles of MAIC. In addition, we demonstrated how to use the anchored MAIC method based on R language for survival data, which has been widely used in anti-cancer drug evaluation. This study aimed to provide an alternative method to inform evidence-based decisions.

    Release date: Export PDF Favorites Scan
1 pages Previous 1 Next

Format

Content