IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves
Abstract Background When applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to IPD. We aim to pr...
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doaj-aed4e8ec588b41f4b8686379281ea4842021-06-06T11:03:07ZengBMCBMC Medical Research Methodology1471-22882021-06-0121112210.1186/s12874-021-01308-8IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curvesNa Liu0Yanhong Zhou1J. Jack Lee2Department of Biostatistics, The University of Texas, MD Anderson Cancer CenterDepartment of Biostatistics, The University of Texas, MD Anderson Cancer CenterDepartment of Biostatistics, The University of Texas, MD Anderson Cancer CenterAbstract Background When applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to IPD. We aim to propose a straightforward and robust approach to obtain IPD from published survival curves with a user-friendly software platform. Results Improving upon existing methods, we propose an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we developed the R package IPDfromKM and an accompanying web-based Shiny application. Both the R package and Shiny application have an “all-in-one” feature such that users can use them to extract raw data coordinates from published K-M curves, reconstruct IPD from the extracted data coordinates, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the basis of the reconstructed IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real-world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, and hazard ratios. Conclusions IPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD and advance the use of secondary data to facilitate informed decision making in medical research.https://doi.org/10.1186/s12874-021-01308-8Individual patient data (IPD)Kaplan-Meier curveMeta-analysisR packageShiny applicationSurvival analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Na Liu Yanhong Zhou J. Jack Lee |
spellingShingle |
Na Liu Yanhong Zhou J. Jack Lee IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves BMC Medical Research Methodology Individual patient data (IPD) Kaplan-Meier curve Meta-analysis R package Shiny application Survival analysis |
author_facet |
Na Liu Yanhong Zhou J. Jack Lee |
author_sort |
Na Liu |
title |
IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves |
title_short |
IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves |
title_full |
IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves |
title_fullStr |
IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves |
title_full_unstemmed |
IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves |
title_sort |
ipdfromkm: reconstruct individual patient data from published kaplan-meier survival curves |
publisher |
BMC |
series |
BMC Medical Research Methodology |
issn |
1471-2288 |
publishDate |
2021-06-01 |
description |
Abstract Background When applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to IPD. We aim to propose a straightforward and robust approach to obtain IPD from published survival curves with a user-friendly software platform. Results Improving upon existing methods, we propose an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we developed the R package IPDfromKM and an accompanying web-based Shiny application. Both the R package and Shiny application have an “all-in-one” feature such that users can use them to extract raw data coordinates from published K-M curves, reconstruct IPD from the extracted data coordinates, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the basis of the reconstructed IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real-world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, and hazard ratios. Conclusions IPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD and advance the use of secondary data to facilitate informed decision making in medical research. |
topic |
Individual patient data (IPD) Kaplan-Meier curve Meta-analysis R package Shiny application Survival analysis |
url |
https://doi.org/10.1186/s12874-021-01308-8 |
work_keys_str_mv |
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