Thursday, May 14, 2020

Research Shorts: Calculating Absolute Effects for Time-to-Event Outcomes

Time-to-event (TTE) data provide information about whether a specific event occurs as well as the amount of time that passes before its occurrence. As such, TTE analyses can be particularly useful in the development of guidelines in fields such as oncology, where various diagnosis and treatment options can change the time-course of a disease and its consequences. A methodological systematic review of cancer-related systematic reviews, however, found that review authors often struggled to appropriately apply TTE data in terms of their absolute effect. A 2019 paper by Skoetz and colleagues provides guidance for applying these types of data to calculate absolute effects in the development of systematic reviews and guidelines.

Direct calculation of absolute effect
If the TTE data come from studies with a fixed length of follow-up period and individual participant data, a timepoint at which all participant data are available should be used to create a 2x2 table, and absolute effect calculated accordingly.  Most of the time, however, absolute effect will not be directly calculable. This is the case with studies that have staggered participant entry and variable length of follow-up, and no time-points at which all individual participant data are captured. In this scenario, an absolute effect can be estimated from the pooled hazard ratio and an assumed baseline risk can be used, or a regular risk difference calculated if events are rare.

Indirect calculation of absolute effect
To estimate baseline risk in the calculation of an absolute effect sizing using a hazard ratio, it is important to use the best estimate of the baseline risk of the population at hand. While data reported in individual clinical trials may be used, consider that they may be either artificially inflated (by means of enrolling patients at higher-than-average risk) or reduced from the true population risk (by means of excluding patients with comorbidities). Thus, it is preferable to obtain a baseline risk estimate through large-scale observational studies conducted in the population of interest with a low risk of bias. Using this type of data to estimate baseline risk is also more likely to result in a higher certainty of effect, depending on the size of the study.

If these options are not suitable, data from the survival curves of control groups within studies at low risk of bias may be used. If possible, utilize data from a middle time-point.

No matter how absolute effects are calculated, it is important to clearly and transparently report this information, including:
  • reporting how the baseline risks were estimated
  • using the same the numbers consistently – e.g, whether reporting number of patients with events or those who remain event-free
  • uniformly choosing one specific time point based on the studies used.
Because absolute effects are more easily understood and used within shared decision-making, these estimates should be provided within the abstract as well as the Summary of Findings table or Evidence Profile.

This figure provides an example of how absolute risk based on time-to-event data can be meaningfully communicated in a patient-facing graphic.

The paper provides further guidance on determining the certainty of evidence using TTE data, calculating absolute absolute effects for events such as mortality, providing graphical representations of the absolute effect, and calculating corresponding numbers needed to treat and median survival times to further aid decision-making.

Skoetz, N., Goldkuhle, M/, van Dalen, E.C. et al. GRADE guidelines 27: How to calculate absolute effects for time-to-event outcomes in summary of findings tables and Evidence Profiles. J Clin Epidemiol 118 (2020); 124-131.

Manuscript available from publisher’s website here.