Evidence map›Paper›PMID 35369867›Full record

ArticleBMC medical research methodology2022

KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves.

Joseph J Zhao, Nicholas L Syn, Benjamin Kye Jyn Tan, Dominic Wei Ting Yap, Chong Boon Teo, Yiong Huak Chan, Raghav Sundar

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Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 4 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

23 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

7 authors.

Joseph J ZhaoYong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore.
Nicholas L SynYong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore.
Benjamin Kye Jyn TanYong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore.
Dominic Wei Ting YapYong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore.
Chong Boon TeoYong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore.
Yiong Huak ChanBiostatistics Unit, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. medcyh@nus.edu.sg.
Raghav SundarYong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore. mdcragh@nus.edu.sg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData from certain subgroups of clinical interest may not be presented in primary manuscripts or conference abstract presentations. In an effort to enable secondary data analyses, we propose a workflow to retrieve unreported subgroup survival data from published Kaplan-Meier (KM) plots.

methodsWe developed KMSubtraction, an R-package that retrieves patients from unreported subgroups by matching participants on KM plots of the overall cohort to participants on KM plots of a known subgroup with follow-up time. By excluding matched patients, the opposing unreported subgroup may be retrieved. Reproducibility and limits of error of the KMSubtraction workflow were assessed by comparing unmatched patients against the original survival data of subgroups from published datasets and simulations. Monte Carlo simulations were utilized to evaluate the limits of error of KMSubtraction.

resultsThe validation exercise found no material systematic error and demonstrates the robustness of KMSubtraction in deriving unreported subgroup survival data. Limits of error were small and negligible on marginal Cox proportional hazard models comparing reconstructed and original survival data of unreported subgroups. Extensive Monte Carlo simulations demonstrate that datasets with high reported subgroup proportion (r = 0.467, p < 0.001), small dataset size (r = - 0.374, p < 0.001) and high proportion of missing data in the unreported subgroup (r = 0.553, p < 0.001) were associated with uncertainty are likely to yield high limits of error with KMSubtraction.

conclusionKMSubtraction demonstrates robustness in deriving survival data from unreported subgroups. The limits of error of KMSubtraction derived from converged Monte Carlo simulations may guide the interpretation of reconstructed survival data of unreported subgroups.

Indexed as

PublicationsHumansKaplan-Meier EstimateMonte Carlo MethodProportional Hazards ModelsReproducibility of Results

Identifiers

PMID35369867
PMCPMC8978435

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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.