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# Week 3 30002

KSU

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## Popular in Introductory biostatistics

## Popular in Public Health

This 3 page Class Notes was uploaded by Cole Wojdacz on Tuesday February 2, 2016. The Class Notes belongs to 30002 at Kent State University taught by Dr. Eng in Summer 2015. Since its upload, it has received 12 views. For similar materials see Introductory biostatistics in Public Health at Kent State University.

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Date Created: 02/02/16

Basics of Probability Definition of Probability Probability: reflects the likelihood than an outcome will occur o 0≤ probablity≤1 number wit houtcome o probability= N o P(outcomeoccurs)+P (outcomedoesn t occu)=1 N o P( )= a N P() is read as "the probability of…" A represents any event of interest A-hat is the compliment of A or "not observing A" N A is the number of events that meet the specified criterion N is the total number of events The Addition Rule If two or more events are mutually exclusive, we can find the probability that one of them happens by adding each of their probabilities Calculating Probabilities Contingency Tables Contingency Table: a convenient means of summarizing data Frequency Contingency Table: summarizes the numbers of observations in a data set that manifest some specified set of characteristics Probability Contingency Table: summarizes the proportions of observations in a data set that manifest some specified set of characteristics Probability Notation A ): the probability of observing event A P¿ P(AB) : the probability of observing event A and event B P A∪B ) : the probability of observing event A or event B P A B ): the probability of observing event A given that you have already observed event B Conditional Probability: probability of an outcome in a specific sub-population A vertical line (|) = given o This makes it a conditional probability Independence Independence: the outcome of one event doesn’t change the probabilities of the outcome of the other event Diagnostic Testing The main goal is to accurately detect the presence of disease Ideal diagnostic tests always correctly detect disease o They should also be simple, quick, cheap, reliable, safe, and painless o Many diagnostic tests do not meet these ideals Evaluating diagnostic tests by using the test result as a predictor variable, and the actual disease rates as the outcome variable Definitions Sensitivity: the probability of a positive test given the person has the disease o P(+¿ D) Specificity: the probability of a negative test given that the person does not have the disease −¿D−¿ ¯ o P¿ Positive and Negative Predictive Values Positive: the probability that a person who tests positive for a disease has that disease o D PPV=P¿ Negative: the probability that a person who tests negative for a disease does not have that disease o D−¿−¿ NPV=P¿ Risk Ratio and Odds Ratio Prevalence: the proportion of participants with a disease at a particular point in time P(D) Prevalence= number of personswithdisease number of personsexamined atbaseline Risk Ratio/Relative Risk (RR): formed by dividing the probability of disease in some group exposed to a potential risk factor by the probability of disease in some group not so exposed D | P¿ P D |E) RR= ¿ Probability of disease in the exposed group: a o P(D|E = a+b Probability of disease in the unexposed group: D o | P¿ Odds: the probability that the event will occur divided by the probability that the event will not occur Odds of disease for the exposed group P D |E) o Odds= P (−¿E ) Odds of disease for the unexposed group D | E−¿¯ o ¿ D−¿E−¿ ¯ P¿ Odds=¿ Odds Ratio (OR): the odds of disease for an exposed group divided by the odds of disease for an unexposed group D | P(D−¿E )P¿ ¯ ¿= P(D|E)P D−¿E−bar ) ¿ a Odd E =b E−¿= c d Od d¿

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