EPI-820 Evidence-Based Medicine. LECTURE 8: PROGNOSIS Mat Reeves BVSc, PhD. Objectives:. 1. Review definitions. 2. Understand concept of natural history and inception cohort studies. 3. Define commonly used measures of prognosis. 4. Understand origins of bias in follow-up studies.
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LECTURE 8: PROGNOSIS
Mat Reeves BVSc, PhD
about the future”
Improved = 75
Not improved = 75
N = 50
Improved = 40
Not improved = 10
N = 100
Improved = 35
Not improved = 65
Number of individuals with disease at t0
Random assignment ensures that known and unknown confounders are equally distributed between exposure groups (this is rarely feasible however, unless a specific RCT designed to evaluate some aspect of prognosis is being conducted).
If a strong confounding factor is known - such as age or sex - limit the range of the characteristics of patients in the study.
Match exposure groups on the basis of important prognostic variables - such as stage of disease, age or sex.
Compare event rates within subgroups (strata) with otherwise similar probability of outcomes e.g., sex or age-groups specific rates.
+Table. Methods for Controlling Selection Bias (from Fletcher)
Mathematically adjust crude rates for a characteristic known to be an important prognostic factor e.g., age adjustment.
Use mathematical models to adjust risk estimates for several prognostic variables (Cox Regression).
Describe how the results could differ by changing the values of known prognostic factors over plausible ranges. Best/worst case analysis is an example.
+Table. Adjustment Procedures to Control Selection Bias