Median follow-up is often used to assess the quality of follow-up in longitudinal studies. Its relevance to evaluate the quality of the produced estimations seems obvious. However, there is no clear decision rule on its use. We evaluated the quality of the Kaplan-Meier estimator of the survival rate according to different values of the median follow-up in order to propose an objective decision rule. We performed a sensitivity analysis via Monte Carlo simulations based on a reference sample, according to the literature. The log(–log) confidence interval of the Kaplan-Meier estimator of survival rate was evaluated in terms of coverage probability and length. The Monte Carlo simulation was performed for 10,000 replications considering 170 values for the median follow-up. We could observe a high relationship between the quality of the estimation and the median follow-up. Heavy censorship leads to an overestimation of the survival rate and anti-conservative confidence intervals. From a threshold value of the median follow-up, the values of the criteria reach a plateau and good estimations of the survival rate are achieved. More precisely, simulations show that reliable Kaplan-Meier estimations cannot be obtained when the median follow-up is less than two thirds of the estimation time. This provides factual elements to evaluate results for long longitudinal studies or to assess ongoing cohorts.
Serge Somda, Yacouba Ouedraogo, Eric Dabone, Eve Leconte, and Thomas Filleron, “Assessing the sensitivity of the Kaplan-Meier confidence interval according to the median follow-up”, JP Journal of Biostatistics, vol. 18, n. 1, January 2021, pp. 67–85.
JP Journal of Biostatistics, vol. 18, n. 1, January 2021, pp. 67–85