Lecture 5: The Natural History of Disease: Ways to Express Prognosis Reading: Gordis - Chapter 5 Lilienfeld and Stolley - Chapter 10, pp. 218-220

Introduction How can we characterize the natural history of disease in quantitative terms That is: what is the prognosis? Problems in defining disease Determine when the disease begins Histological confirmation Determine stage of disease

Introduction Screening tests and diagnostic tests characterize people as sick or well Once diagnosed as sick – the question is: How sick and what duration cure or death

Introduction Quantification is important because: Knowing severity is useful in setting priorities for clinical services and public health programs Patients want to know the prognosis Baseline prognosis is useful when evaluating new therapies

Prognosis Prognosis can be expressed in terms of deaths from the disease or survivors with the disease. Ways to express prognosis: Case-fatality rate Five-year survival Observed survival rate Life table analysis Kaplan-Meier method Median survival time

Case-fatality rate Case-fatality rate = Number of people who die from the disease Number of people with the disease Given that a person has the disease what is their risk of dying from that disease Different than mortality rate (how?) Case-fatality often used for acute diseases of short duration In chronic disease, death may occur many years after diagnosis and the possibility of death from other causes becomes more likely.

Case-fatality rate: example 1000 new recruits get infected with disease X over a 15 day period 10 die within 5 days of diagnosis Case-fatality rate: 10/1000 = 1%

Person-years of follow-up Incidence rate using person-time for denominator Because with chronic diseases the diagnosis are not clustered around a single event (like an industrial exposure) Follow-up may differ and these differences can be “adjusted” by using person-time in the denominator

Person-years of follow-up Assumptions with incidence rate: Prognosis is the same over the entire follow-up period That is: Following 5 people for 2 years will give the same information as following 2 people for 5 years

Person-years: example

Five-year survival Percent of patients who are alive 5 years after diagnosis. Nothing magical about 5 years Most deaths from cancer occur during this period (historically) Convenient However, changes in screening may affect the time of diagnosis

Five-year survival Comparing 5-year survival among groups is only informative if the individuals began at a similar stage of disease The interval between diagnosis and death may be increased not because of better treatment but because of earlier diagnosis Lead time bias

Five-year survival What if we want to examine the effects of a therapy that was introduced 2 years ago. Do we wait for 5 years so we can use the 5-year survival rate? We use life table analysis

From person-years example In the previous example: 10.53 cases /100 PY / over 5 years or 2.1 cases / 100 PY / per year? 13 cases / 100 PY / over 5 years or 2.6 cases / 100 PY / per year? 8.55 cases / 1000 PY / over 5 years or 1.7 cases / 100 PY / per year?

Life table analysis Previous to now – when we used follow-up time we were describing the RATE at which disease occurred. How do we assess the RISK of disease development using follow-up time? Without making the assumption that risk is the same across all strata of time

Life table analysis Calculate the probabilities (risks) of surviving different lengths of time Using all of the data available If follow-up is complete: the easiest way is using the cumulative incidence Follow-up is usually NOT complete Therefore: LIFE TABLES and Kaplan Meier

Life table analysis without withdrew Cumulative proportion surviving = Pr(survival time t) = Pr(survival time t | survival time t-1) x Pr(survival time t-1) So: 0.81 = 0.9 x 0.9 0.73 =0.901 x 0.81 0.66 = 0.904 x 0.73

Life table analysis with withdrew Withdrew or loss to follow-up Effective number at risk = alive at beginning – ½ x withdrew So 375 = 375 – 0 175.5 = 197 – ½ x 43 …

Kaplan-Meier method In the life table analysis, we predetermine the intervals (e.g., 1 year). Kaplan-Meier method identifies the exact point in time when each death occurred Each death determines the interval

Kaplan-Meier method: example Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 died loss to follow-up died died loss to follow-up died 4 10 14 24 100 80 60 40 20 Percent surviving 4 10 14 24

Life table analysis Assumptions in using Life Tables No secular (temporal) change in the effectiveness of treatment or in survivorship over calendar time Survival experience of those lost to follow-up is the same as the experience of those who are followed

Median survival time The length of time that half of the study population survives Two advantages over mean survival Less affected by extremes (outliers) Can be calculated before the end To observe the mean survival – we need to observe all of the events

Generalizability of survival data The cohort must be at a similar stage of disease Patient data from clinics or hospitals may not be generalizable to all patients in the general population Referral patients may not represent all sick individuals