Person years and person months are types of measurement that take into account both the number of people in the study and the amount of time each person spends in the study. For example, a study that follows 1,000 people for one year would contain 1,000 person years of data. A study that follows 100 people for 10 years would also contain 1,000 person years of data.
The same amount of data would be collected, but it would be collected on fewer people being studied for a longer follow-up period.
Survival Analysis
Person years and person months are often used as a measurement of time in studies that analyze their data using Kaplan-Meier curves, which is also known as survival analysis.
Survival analysis allows scientists to estimate how long it takes for half of a population to undergo an event. It is called “survival analysis” because the technique was initially developed to look at how various factors affected the length of life. However, today survival analysis is used by researchers across a number of fields—from economics to medicine.
Survival analysis is more forgiving of certain types of data problems than other types of analysis, such as when people leave the study before the end of the research period. Using survival analysis means the time those people spent in the study will still count toward the results.
STI Studies Using Person Years
A number of research studies looking at sexually transmitted infections (STIs) have used person years as a component of their analyses. A few examples are:
A 2015 study looked at whether hepatitis C (HCV) infection increased the risk of deep vein thrombosis (DVT) and related health consequences. The study found that HCV infection did increase DVT risk but not the rate of pulmonary emboli (blood clots in the lungs). A 2014 study looked at how often people living with HIV (human immunodeficiency virus) are long-term nonprogressors (people with HIV who do not progress to AIDS). The study found that even if people make it to 10 years post infection without progressing, most of them will eventually progress to AIDS without treatment. A 2013 study demonstrated that women presenting for infertility treatment are less likely to be able to get pregnant, without in vitro fertilization (IVF), if they test positive for chlamydia than if they don’t.
Time is an important component in these studies. For instance, in the infertility study, it didn’t just matter if women got pregnant, it mattered how long it took them to get there.
