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E-quiz 3c


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Quantification of prognosis (disease experience)
Allows us to: Describe severity to assist in priority setting Provide information for persons with disease Provide a means of comparison across groups
Different perspective on natural history of disease
Biological onset Pathologic evidence Signs and symptoms Medical care sought Diagnosis made Treatment Outcome
Outcomes in describing prognosis
Death/Survival Recurrence of disease Yes/No or time until recurrence Time to impairment Time to recovery Specific quality of life measures
Expressing prognosis: Case Fatality Rate (CFR)
the proportion of persons with disease that die of that disease (within a specific time frame) most useful for acute diseases with relatively rapid onset of death not useful for chronic disease where other causes of death may intervene
Expressing prognosis: Person-years of survival
Useful method to capture information from persons observed for varying lengths of time assumes that each person year has equal risk of death frequently the measure of choice in cohort studies and clinical trials
Expressing prognosis: person years
Computed just like incidence density, but the numerator might be any of a range of outcomes: relapse recurrence of disease disability death
Expressing prognosis: 5-year survival
Most frequently used for measuring clinical outcomes for diseases such as cancer 5 year interval is arbitrary, not based on any biological data must observe subjects for the entire period to calculate the measure
Expressing prognosis: 5-year survival (part 2)
utility of the 5 year survival measure over time can diminish since screening and disease detection patterns can change dramatically sensitivity of mammograms versus breast self-exams at finding small tumors in breast cancer
Expressing prognosis: life table method
Uses the actual observed survival over time measures that survival in 1-year increments from treatment or some other initial event
Expressing prognosis: life table method 2
The life table method requires the computation of survival probabilities for each year the probability of surviving the entire interval is estimated by computing the product of the individual probabilities
Life table method
(look at slides for tables)
Life table method (issue)
This example does not deal with the problem of lost to follow-up
Assumptions in life table method
No or minimal changes in effectiveness of treatment or survivorship over calendar time (secular trends) No changes in diagnostic technology over study time period Survival in those lost to follow-up is not significantly different than those retained in the study
Expressing prognosis: Kaplan-Meier Method
This method uses the exact time of event of interest in calculating the step-wise survival curves not limited to yearly as in the life table example can compare curves for different groups – male/female for example
Expressing prognosis: mean survival time
The average length of time that the study group survives Easy to compute Influenced substantially by outliers – those with extreme values (short or long)
Expressing prognosis: median survival time
The length of time that half of the study group survives Less influenced by outliers than mean survival Time-efficient - only need to observe until half of group reaches endpoint
Expressing prognosis: relative survival rate
Compares observed survival with a disease or condition to that of the same group without the condition Analogous to the RR
Choice of prognosis measure
Determined in large part by: data availability purpose of analysis Assumptions and caveats for each measure must also factor into the decision process and final usage

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