1. Epidemiology?
    Study of how much dis-ease occurs in different populations/groups and of the factors that determine differences (variation) in the occurence of dis-ease between these groups.
  2. incidence?
    • caculated by counting the number of onsets of dia-ease occuring during a period of time.
    • e.g. heart attacks, death
  3. prevalence?
    • Counting the number of people with dis-ease at one point in time and then dividing by the number of people in the study group at that point in time (N/D)
    • e.g. diabetes, raised blood pressure level,being over weight, ashma attacks
  4. High incidence and low prevalence & High incidence and high prevalence
    A population with a high incidence of dis-ease could have a low prevalence if the death rate or cure rate is so high e.g. cold

    A population with a low incidence of dis-ease could have a high prevalence of dia-ease, if almost no one dies of the disease or is cured. e.g. diabetes, HIV
  5. Point Prevalence& Period Prevalence
    Prevalence is usually point prevalence, as the presence of dis-ease is measured

    Period prevalence- looking back in time.  the total number of persons known to have had the conditions at any time during a specific period is divided by the number of people in the population being studied. (ashma attack defined as having 2 attacks)- only measured as 1
  6. Estimates of effect
    • Difference in disease occurrence between an exposed group and an unexposed group
    • initially describe them as- estimates of association
  7. RR, RRR, RRI
    • RR=1 no difference- no effect value (EG/CG) (CG/EG)
    • RRR= RR less than 1 known as relative risk reduction (1-RR)x100
    • RRI= RR greater than 1 known as relative risk increase (RR-1)x100
  8. ARR & ARI
    EGO-CGO= RD is a absolute risk reduction(ARR) if the risk is lower in the exposure group ot a absolute risk increase(ARI) if the risk is higher in the exposure group

    Mean differences(MD)- difference between 2 means
  9. External vadility error, Recruitment error, Selection bias (Sports NZ school example)
    Main objective of the study is to measure the characteristics of a specified eligible population, but the participants who are recruited are not representives of the eligibles
  10. Random Sampling error
    Every representative sample will be slightly different from every other sample- the bigger the sample the smaller the difference
  11. Allocation/adjustment error
    Many/all participants allocated to EG are recruited from different source then those in CG
  12. Non-response bias/selection bias
    If a substantial proportion of the eligible population do not agree to take part (non responders) and the non responders are different from the responders
  13. Non-randomised experiment
    Investigator chooses which participants will recieve the exposure. Study investigators may choose to treat particular people they think will benefit most from study treatment (EG differ from CG)
  14. Allocation error as the cause of confounding
    Error occurs because of how participants were allocated to EG and CG
  15. Baseline comparison
    Check for differences between EG and CG at the beginning of the study regardless of whether participants were allocated by randomisation or measurement.
  16. Concealent of allocation
    Reallocating participants in EG and CG
  17. Stratified analysis
    Dividing participants into older or younger age groups or 'strata' (2 triangles, analysing seperately) similar? Results in different strata combined Different? Reported seperately
  18. Maintenance error
    Some partircipants exposure status changes or some lost to follow up
  19. blind to exposure
    • may have greater influence from subjective factors . reduce=blind
    • More objective measurements
  20. intention-to-treat (or to expose)
    Everyone allocated to EG or CG are included in the denominators in the analysis
  21. on-treatment (exposure) analysis
    Only those who remained on treatment are included on analysis
  22. Random measurement/ assessment error
    Our ability to measure biological factors in exactly the same way every time we measure them is often poor, particularly if the measurement instrument requires a human operator- other factors that influence the operators ability to detect blood- reduce random error  
  23. The randomness inherent in biological phenomena
    biological varibility reduced by taking multiple measurements and then averaging results
  24. Random allocation error
    Exposure and comparison groups differ by chance alone
  25. Confidence interval
    There is about a 95% probability that the true value of EGO in the whole population from which the study participants were recruited, lies between 8.0 and 10.0
  26. Results of studies if they cross no effect line or not
    • Cl (confidence limit) cross no effect line= not statistically significant
    • Touches no effect line= boderline statistically significant
    • Below no effect line= statistically significant
  27. Width of 95% Cl for EGO, CGO, RR and RD decreases when
    Number of events in study increases
  28. The study results are not statistically significant
    95% Cls for EGO and CGO overlap, the 95% Cl for the RD and RR will usually cross the no-effect line. Not possible to determine if the true RD is positive or negative. Or is RR is more or less than 1
  29. 95% Cl for RR or RD crosses the no effect line
    there is too much random error to determine if there is a real difference between EGO and CGO
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