Evidence map›Paper›PMID 34074267›Full record

ArticleBMC medical research methodology2021

IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves.

Na Liu, Yanhong Zhou, J Jack Lee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 392 papers, 87 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
392citing papers in PubMed, 87 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

392 citing papers in PubMed, 87 syntheses or guidelines pooled it.

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332 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Na LiuDepartment of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, United States.
Yanhong ZhouDepartment of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, United States.
J Jack LeeDepartment of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, United States. jjlee@mdanderson.org.ORCID 0000-0001-5469-9214

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
The University of Texas MD Anderson Cancer Center SPORE in MelanomaP50CA221703 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI WARGO, JENNIFER A. · 2019 to 2023
$10.3M
Statistical Software for Adaptive Oncology Clinical TrialsR43CA150519 · NCI · CYTEL, INC · PI MEHTA, CYRUS R · 2010 to 2010
$185k
NCI NIH HHS CA016672NCI NIH HHS CA221703NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA221703
6 · The paper itself

Abstract

backgroundWhen 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.

resultsImproving 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.

conclusionsIPDfromKM 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.

Indexed as

SoftwareHumansKaplan-Meier EstimateProbabilityProportional Hazards ModelsReproducibility of ResultsIndividual patient data (IPD)Kaplan-Meier curveMeta-analysisR packageShiny applicationSurvival analysis

Identifiers

PMID34074267
PMCPMC8168323

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.