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79 Cards in this Set

  • Front
  • Back

proportion

- division of 2 numbers


- numerator is included in the denominator


- quantities are of the same nature


- proportions range between 0-1, or x100 (percentage)

ratio

- division of 2 numbers


- numerator not included in the denominator


- can compare quantities of different nature

rate

- division of 2 numbers w/ time included in the denominator


- speed of occurrence of an event over time

cumulative incidence

- proportion of a population that acquire a disease in a period of time


- the probability of developing a disease




# of new cases during a period/population at the beginning of the period

prevalence

- proportion of a population that has a condition at a given point in time




# of cases observed at a given time/total # of individuals at a given time

period prevalence

# of existing cases + new cases developing during the study/total population

odds

probability that an event will happen/probability that an event won't happen

relationship between probability and odds

- probability and odds are more alike the lower the absolute P (risk)




Prob = Odds/1+Odds


Odds = Prob/1-Prob

risk difference

cumulative incidence of Group 1 - cumulative incidence of Group 2




(a/a+b) - (c/c+d)

risk ratio (relative risk)

cumulative incidence of Group 1/cumulative incidence of Group 2




(a/a+b) / (c/c+d)

odds ratio

odds of disease in Group 1/odds of disease in Group 2




(a/b)/(c/d)

hypothesis

statement of belief about population parameters

Type I error

occur with a probability of alpha




rejecting H0 when H0 is actually true




as the probability of Type I errors increases, the probability of Type II errors decreases

Type II error

occur with a probability of beta




failing to reject H0 when Ha is actually true (Ha is true but we say it's not)




as the probability of Type I errors increases, the probability of Type II errors decreases

confidence interval

probability that a repeated study/test will get results within the confidence interval

predictive value positive

probability that an individual with a positive test actually has the disease in question




disease +, test +/total test +




a/(a+b)

sensitivity

probability that a test will be positive when applied to an individual who actually has the disease




disease +, test +/total disease +




a/(a+c)

specificity

the probability that a test will be negative when applied to a disease free individual




disease -, test -/total disease -




d/(b+d)

false negative rate

probability that an animal with the disease will have a falsely negative test result




disease +, test -/total disease +




c/(a+c)

false reassurance rate

proportion of the negative test results that are actually false negative




disease +, test -/total test -




c/(c+d)




... OR 1-PVN

false positive rate

probability that an animal without the disease will have a false positive test result




disease -, test +/total disease -




b/(b+d)

false alarm rate

proportion of the positive test results that are actually false positives




disease -, test +/total test +




b/(a+b)




... OR 1-PVP

prior probability

prevalence of the disease or condition in the population under study




may be different in different populations

negative predictive value

probability that an individual with a negative test actually is free of the disease




non-disease indiv that test neg/total neg tests




d/(c+d)

accuracy

overall frequency of correct diagnostic classification, how close the answer is to the true value




depends on prevalence unless sensitivity and specificity are the same

critical value

study result that differentiates a positive finding from a negative finding

prevalence pool

subset of population in the given disease state




if an animal dies it is removed from the prevalence pool.




if an animal recovers it exits the prevalence pool.

risk or propability

probability that an event will happen/total cases

bias

systematic difference of results from the truth - not due to random error




prejudice in favor or against one thing, person, or group compared with another




selection, information and confounding bias

sources of bias

selection, misclassification, confounding

precision

reflects the variability of the results, quality of being repeatable




testing the same sample should give the same result




increase precision w/ a larger sample size or by reducing the variability of measurements

confounding factors

source of bias in a study




related to both the risk factor of interest and the outcome variable

pattern recognition (Gestalt)

looks like a duck, quacks like a duck...

method of exhaustion

collect and sift through results of history, physical exam, lab tests, etc...




expensive in time and lab costs, not effective

branching algorithms

false positives and false negatives can lead to an inaccurate final answer

hypothetico-deductive method

formulate short list of potential diagnoses




selectively accumulate data to reduce length of list




scramble/add to list until you get a good fit

power of the study

probability of detecting a predefined clinically significant difference




power = 1 - beta

significance level

alpha or type 1 error




probability of detecting a significant difference when treatments are equally effective




risk of a false positive




set at alpha = 0.05 as a standard

descriptive studies

- looking at a population and describing it




- also called hypothesis generating studies




- case report, case series, survey/census




- pros: use for generating hypotheses, informative for rare disease w/ few est. risk factors, characterize disorders


- cons: can't study cause/effect, can't assess disease frequency

case report

- descriptive study




- describes rare disease in an individual or population




- easy, low cost, low investigator control, no causal proof

case series

- descriptive study




- describes a series of similar cases




- easy, low cost, low investigator control, no causal proof

survey or census

- descriptive study




- describes characteristics of a population




- survey: collected from a sample of the population


- census: collected from all members of the population




- moderate difficulty, low cost, moderate investigator control, no causal proof

analytic studies

- hypothesis testing or experimental studies



- observational or experimental



- evaluation of diagnostic tests, reviews

observational studies

- analytic study




- no individual intervention




- treatment or exposures occur in "non-controlled" environment




- individual can be observed: concurrently, prospectively, retrospectively

prospective study

- observational, analytic study




- looks forward in time


- follows an exposure


- examines future events

retrospective study

- observational, analytic study




- looks back in time


- start with the disease


- examines events that have already occurred

cross-sectional studies

- observational, analytical study




- determine disease and exposure at a single time point




- often used to study conditions that are relatively common w/ long duration




- moderate difficulty, moderate cost, low investigator control, low causal proof

case-control studies

- observational, analytical study



- compare individuals w/ known disease status to find differences in exposure



- data collected retrospectively




- moderate difficulty, moderate cost, moderate investigator control, moderate causal proof

cohort studies

- experimental, analytical study




- compare individuals w/ known risk factor to individuals w/o the risk factor




- evaluate risk of disease over a period of time




- data usually collected prospectively




- difficult, high cost, high investigator control, high causal proof

experimental studies

- investigator "controls" the exposure




- randomly assigned to groups




- clinical trials most well known experimental design




- ultimate step in causal hypothesis testing

randomized controlled trial

- experimental, analytical study




- subjects randomly assigned to group - treatment, control groups




- prospective




- difficult, expensive, high investigator control, high causal proof - GOLD STANDARD OF PROOF

validity

the quality of being logically or factually sound

internal validity

study population must be representative of the target population




maximize internal validity by:


- decreasing bias


- sampling of target population


- allocation to group

external validity

ability to make inferences to populations beyond the target population




can't be externally valid if not internally valid

selection bias

- error occurs selecting individuals for a study




- study population not representative of target population




- minimize non-response to surveys, proper selection of comparison group

selecting a comparison group

- pre-determined inclusion and exclusion criteria




- observational study: case and exposure defns, random or other representative sample of comparison group




- experimental study: random allocation to treatment/control group, sequential allocation

simple random sampling

every animal has an equal chance of selection

systematic sampling

every kth animal is selected

cluster sampling

simple random sample of groups

stratified sampling

population divided into mutually exclusive groups; random selection w/in each group

information bias

- bias in measurement or gathering of data - i.e. data collected by a biased observer, recall bias, measurement, misclassification




- have standardize protocol for data collection, use objective outcomes, use accurate and precise tests, use tests w/ high sensitivity and specificity, "blind" the person measuring

misclassification

incorrect assignment of exposure and/or disease status




non-differential or differential

non-differential misclassification

frequency of classification errors is the same between groups


- exposure misclassification is the same regardless of disease status


OR


- outcome misclassification is the same regardless of exposure status

differential misclassification

frequency of classification errors is not the same between groups

- exposure misclassification differs based on disease status


OR


- disease misclassification differs based on exposure status

confounding bias

"to mix up something w/ something else so that the individual elements become difficult to distinguish"




- factor assoc. w/ both the exposure and the outcome - bias occurs when this isn't accounted for in design and/or analysis




- restrict study to one level of confounder, collect data on potential confounders and account for it in analysis, randomize clinical trials, match on confounders in observational trials, stratify by level of confounder, account for using multivariable methods

health management considerations

- animal welfare


- food safety


- herd economics


- legality


- strategic and tactical planning


- change


- needs to be part of general management

properties of data

lag


momentum


bias


dispersion

lag

data is only available about an event after some time period has passed

momentum

a parameter of interest changes slowly in response to real underlying change

dispersion

the distribution of values for a parameter is such that the usual means of reporting may miss important features of herd behavior

qualitative data

valuable in decision making and monitoring

cost of disease (dairy cow)

cost of milk not produced - cost of feed not eaten = marginal cost of milk not produced

value of discarded milk

milk discarded due to treatment is assumed safe to use to feed calves




value of this milk is 1/2 the value of normal sale value

cost of a not pregnant cow

fixed cost of $3.00/day assumed for an open cow

labor cost of a sick cow

estimate an extra $16/hr labor of treating/handling sick cows

veterinary costs of a sick cow

estimated at $100/hr for vet treatment of a sick cow

culling lame cows

risk for culling increases two-fold with lameness




varies slightly depending on etiology of lameness (i.e. digital dermatitis, foot rot, abscess, ulcer, complicated ulcer)

minimization of disease losses

- reduce disease consequences: early detection & treatment, rational interventions


- reduce diseases incidence

biological risk factors for neonatal diarrhea

- calving area hygiene


- calving assistance protocols


- colostrum delivery


- colostrum quality


- calf housing


- nutrition & feeding practices