A Common Pitfall in Glaucoma Treatment Success Assessment
We read with interest the article by Ko et al.1 In their article, they have evaluated the surgical outcomes of the implantation of an additional Ahmed glaucoma valve into the eyes of patients with refractory glaucoma after previous Ahmed glaucoma valve implantation. A Kaplan-Meier survival analysis was performed to assess success rates. They have presented a survival analysis curve that yielded success rates of 87%, 70%, and 52% at 1, 2, and 3 years, respectively. Although the authors have attentively presented the number of patients under follow-up at sequential visits in another graph that addresses the IOP changes over time, the confidence interval (CI) for survival analysis is not readily accessible for readers of the article. In this letter, we would like to notify a common pitfall in glaucoma research.
The Kaplan-Meier analysis is widely utilized in glaucoma treatment success assessment. If follow-up is complete for every individual in a studied cohort, the estimation of the cumulative incidence of failure will simply result from dividing the number of failures occurring during the follow-up time by the initial population. However, in situations where there are individuals lost to follow-up (statistically called censored observations), special analytical approaches, collectively known as survival analysis, are required. The Kaplan-Meier method involves the calculation of the probability of each event at the time it occurs. The denominator for this calculation is the population at risk at the time of each event’s (eg, surgical failure in this study) occurrence. It is noteworthy that like other tests and methods in statistics, it is essential to estimate the statistical uncertainty around the measure of the cumulative incidence obtained in the study (the so-called point estimate) in relation to the (unknown) true value of the success/failure rate in the reference population (refractory glaucoma).2 In fact, not calculating (or reporting) the CI for the cumulative survival estimate is a major drawback of many articles that evaluate the success after glaucoma treatment. It is especially essential when interpreting the survivor function at the far right of a Kaplan-Meier survival curve, as there are fewer patients remaining in the study group, and the survival estimates are not as accurate. Furthermore, plotting CIs can sometimes be useful in comparing survival curves from different studies to assess whether a visualized difference between two given survival curves proves statistically significant.