A non-linear optimisation method to extract summary statistics from Kaplan-Meier survival plots using the published P value.
Background: Meta-analyses of studies evaluating survival (time-to-event) outcomes are a powerful technique to assess the strength of evidence for a given disease or treatment. However, these studies rely on the adequate reporting of summary statistics in the source articles to facilitate further ana...
| Published in: | BMC Medical Research Methodology Vol. 20; no. 1 |
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| Main Authors: | , , , |
| Format: | research Journal Article |
| Published: |
BioMed Central
10/30/2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146732981&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146732981 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 10/30/2020 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 146732981 146732981 NLM33126853 146732981 10.1186/s12874-020-01092-x NLM33126853 146732981 ppct: 1 formats: tig: atl: A non-linear optimisation method to extract summary statistics from Kaplan-Meier survival plots using the published P value. aug: au: Irvine, Andrew F. Waise, Sara Green, Edward W. Stuart, Beth affil: Faculty of Medicine, University of Southampton, Southampton, UK sug: subj: Study Design Survival Analysis Kaplan-Meier Estimator Human Cox Proportional Hazards Model Prognosis Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: Background: Meta-analyses of studies evaluating survival (time-to-event) outcomes are a powerful technique to assess the strength of evidence for a given disease or treatment. However, these studies rely on the adequate reporting of summary statistics in the source articles to facilitate further analysis. Unfortunately, many studies, especially within the field of prognostic research do not report such statistics, making secondary analyses challenging. Consequently, methods have been developed to infer missing statistics from the commonly published Kaplan-Meier (KM) plots but are liable to error especially when the published number at risk is not included.Methods: We therefore developed a method using non-linear optimisation (nlopt) that only requires the KM plot and the commonly published P value to better estimate the underlying censoring pattern. We use this information to then calculate the natural logarithm of the hazard ratio (ln (HR)) and its variance (var) ln (HR), statistics important for meta-analyses.Results: We compared this method to the Parmar method which also does not require the number at risk to be published. In a validation set consisting of 13 KM studies, a statistically significant improvement in calculating ln (HR) when using an exact P value was obtained (mean absolute error 0.014 vs 0.077, P = 0.003). Thus, when the true HR has a value of 1.5, inference of the HR using the proposed method would set limits between 1.49/1.52, an improvement of the 1.39/1.62 limits obtained using the Parmar method. We also used Monte Carlo simulations to establish recommendations for the number and positioning of points required for the method.Conclusion: The proposed non-linear optimisation method is an improvement on the existing method when only a KM plot and P value are included and as such will enhance the accuracy of meta-analyses performed for studies analysing time-to-event outcomes. The nlopt source code is available, as is a simple-to-use web implementation of the method. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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