The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.

Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted, focusing on the critical first year. Participants are divided into two groups: one followed for a year and another for two years. Survival rates are estimated using the actuarial (or life-table) method, which divides the study period into intervals—typically one year.

To illustrate, suppose the first group starts with 1,000 patients, of whom 240 die within the first year. The second group also begins with 1,000 patients, with 200 deaths in the first year and 16 in the second year. The one-year survival rate is calculated by subtracting the total number of deaths from the initial cohort size and dividing the result by the starting population:

One-year survival rate = (2000−240−200)/2000=0.78 or 78%

This approach can also be applied to studies of long-term outcomes, such as evaluating the efficacy of a cancer treatment over years. The actuarial method accommodates patients lost to follow-up or who die from unrelated causes, allowing for a robust analysis of long-term effects.

For two-year survival rates, the calculation becomes more nuanced. It uses conditional probability, considering only those individuals observed for the full two years and who survived the first year. The two-year survival rate must not exceed the one-year rate and is determined by multiplying the probability of surviving the first year by the probability of surviving the second year, conditional on surviving the first year:

Two-year survival rate=0.78×0.98=0.7644 or 76.44%

This sophisticated method, while powerful, faces challenges such as incomplete follow-up data and accurate recording of deaths. Despite these limitations, it remains particularly effective in large-scale population studies where tracking individual participants is impractical. By accounting for censoring and interval-based survival probabilities, the actuarial approach provides a reliable framework for survival analysis in diverse research contexts.