Methodology to standardize heterogeneous statistical data presentations for combining time-to-event oncologic outcomes

Abstract

Survival analysis following oncological treatments require specific analysis techniques to account for data considerations, such as failure to observe the time of event, patient withdrawal, loss to follow-up, and differential follow up. These techniques can include Kaplan-Meier and Cox proportional hazard analyses. However, studies do not always report overall survival (OS), disease-free survival (DFS), or cancer recurrence using hazard ratios, making the synthesis of such oncologic outcomes difficult. We propose a hierarchical utilization of methods to extract or estimate the hazard ratio to standardize time-to-event outcomes so that study inclusion into meta-analyses can be maximized. We also provide proof-of concept results from a statistical analysis that compares OS, DFS, and cancer recurrence for robotic surgery to open and non-robotic minimally invasive surgery. In our example, use of the proposed methodology would allow for the increase in data inclusion from 108 hazard ratios reported to 240 hazard ratios reported or estimated, resulting in an increase of 122%. While there are publications summarizing the motivation for these analyses, and comprehensive papers describing strategies to obtain estimates from published time-dependent analyses, we are not aware of a manuscript that describes a prospective framework for an analysis of this scale focusing on the inclusion of a maximum number of publications reporting on long-term oncologic outcomes incorporating various presentations of statistical data.

Fig 1. Decision tree for hazard ratio extraction: Flow chart to determine which hazard ratio estimate to use based on data provided in manuscript.

Fig 2. Simulations to verify correct implementation of Guyot algorithm: Visual illustration of simulations explored for validation of Guyot algorithm.

Details can be found in S2 Appendix.

Fig 3. Kaplan-Meier curve worked example: Example data for Method 3 Guyot algorithm and worked example.

Panel A is a graph that might appear in a publication. Panel B shows the “digitized” version with time and KM points, and panel C shows the re-constructed individual patient data using the digitization and n at risk as input. See S1 Appendix for full R code.