EBVM Flashcards
what are the 2 objectives of observational studies
- identify risk factors
- quantify the effect of risk factors on disease occurrence
what are 3 sources of data for analysis
- experimental studies
- observational studies
- other people’s data (secondary data)
in this type of study, a researcher will control one or more factors that may affect what they are studying
experimental studies
in this type of study, the research will record factors of interest and outcomes; however these studies don’t change anything
observational studies
this type of data describe a property of individuals such as hair color, eye color, religion or gender
categorical
this type of data can be put in an order but not measured like a number (ex: sad < ok < happy; BCS)
ordinal (ranked)
this is data that exists as whole numbers, such as counts of animals, age or number of times an animal was sick
discrete
this is data where any number could be measured, including decimals or fractions (weight, length, age, temp)
continuous
numbers are put in order and we find the numbers that are 1 quarter and 3 quarters along, the difference between them is this
interquartile range (IQR)
this is the mathematical measure of spread that look at how all the numbers differ from the overall mean
standard deviation (SD)
this is a way to express uncertainty in an estimate and show how precise it is; it is a measure of hoe much estimates of that size would vary
standard error
this is a precision or uncertainty statement that we add after an estimate; it gives a range which we are reasonably confident will contain the true population value/parameter
confidence interval
this is a hypothesis that we are setting out to disprove; normally one of no-difference or no-effect
null hypothesis
this is the probability/chance that the difference we have found between our samples would occur if our null hypothesis were true and there was no difference between our populations
P-value
if the p-value a researcher gets for their question is less than this critical value, they conclude that there is evidence to reject the null hypothesis
0.05
this error is made if we reject a true null hypothesis
type 1 error
this error is made if we fail to reject a false null hypothesis
type 2 error
a powerful study has a low chance of type 2 error; power is influenced by these 3 things..
- sample size
- difference between the populations
- individual variation
list Koch’s postulates; an organism is causal if…
- present in all cases of disease
- does not occur in other diseases
- can be isolated and cultured
- can induce disease in healthy animals
what are 5 difficulties with Koch’s postulates
- requires specificity of response (only 1 disease)
- isolation difficult
- reproduction of disease difficult
- only applicable to infectious diseases
- multifactorial complexes cannot be addressed
main measures to quantify disease are of these 2 things
morbidity and mortality
what is the cumulative incidence (CI) fraction?
CI = number of individuals that become diseased during a particular period / number of healthy individuals in the population at the beginning of that period
what is the incidence rate (I) fraction?
I = number of cases of disease that occur in a population during a particular period of time / the sum, over all individuals, of the length of time at risk of developing disease
what is the relationship between incidence and prevalence?
P ∝ I x D
how to calculate case fatality (CF)?
CF = number of deaths / number of diseased animals
how to calculate survival?
S = (number of cases - number of deaths) / number of cases
why can’t seronegative animals be included in geometric mean titres?
because the logarithm of zero is -∞
what is the geometric titre mean
antilog2 of arithmetic mean
these assays are based on ‘all-or-none’ response (ex agglutination)
quantal assays
