-
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.
-
incidence?
- caculated by counting the number of onsets of dia-ease occuring during a period of time.
- e.g. heart attacks, death
-
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
-
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
-
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
-
Estimates of effect
- Difference in disease occurrence between an exposed group and an unexposed group
- initially describe them as- estimates of association
-
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
-
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
-
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
-
Random Sampling error
Every representative sample will be slightly different from every other sample- the bigger the sample the smaller the difference
-
Allocation/adjustment error
Many/all participants allocated to EG are recruited from different source then those in CG
-
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
-
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)
-
Allocation error as the cause of confounding
Error occurs because of how participants were allocated to EG and CG
-
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.
-
Concealent of allocation
Reallocating participants in EG and CG
-
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
-
Maintenance error
Some partircipants exposure status changes or some lost to follow up
-
blind to exposure
- may have greater influence from subjective factors . reduce=blind
- More objective measurements
-
intention-to-treat (or to expose)
Everyone allocated to EG or CG are included in the denominators in the analysis
-
on-treatment (exposure) analysis
Only those who remained on treatment are included on analysis
-
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
-
The randomness inherent in biological phenomena
biological varibility reduced by taking multiple measurements and then averaging results
-
Random allocation error
Exposure and comparison groups differ by chance alone
-
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
-
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
-
Width of 95% Cl for EGO, CGO, RR and RD decreases when
Number of events in study increases
-
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
-
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
|
|
