BIOSTATISTICS Topic: Probability 郭士逢 輔大生科系 2007 Note: These slides are made for teaching purpose only, with contents from the textbook, Biostatistics for the biological and health science, by M.M. Triola and M.F. Triola), and supplemental materials published by Pearson Education, Inc. in 2006

Definition Event –A collection of outcomes of a procedure Simple event –An outcome or an event that can not be further broken down Sample space –For a procedure consist of all possible simple events

Example - Gender of baby Procedure: 1 birth –Event: female –Sample space: {male, female} Procedure: 3 births –Event: 2 females and a male –Sample space: {fff, ffm, fmf, fmm, mff, mfm, mmf, mmm}

Notation of probabilities P denotes a probability A, B, C denote specific event P(A) denotes the probability of event A occuring

Defined the probability of an event Relative frequency approximation Classical approach for equally likely outcomes Subjective probabilities

Relative frequency approximation Conduct and observe a procedure and count the number of time that event A actually occurs Base on the result, P(A)s estimated as simulation

Classical approach For a given procedure has n different simple events, assuming each of those simple events has an equal chance of occurring

Subjective probabilities probability of event A s estimated by using knowledge of relevant circumstance

Law of large number As a procedure is repeated again and again, the relative frequency probability of an event tends to approach the actual probability

Rules of probability The probability of an impossible event is 0 The probability of an event that is certain to occur is 1 For any event, the probability of A is between 0 and 1 inclusive

Complementary event Consist of all outcomes in which event A does not occur, and denoted by Example,105 out of 205 newborn babies are boys, then P(not boy) = P(girl) =100/205 = 0.488

Rounding off probabilities Express probability in fraction or decimal rounding off to 3 significant digits

Compound event Any event combining two or more simple events

Addition rule P(A or B) = P(in a single trial, event A occur s or event B occurs or they both occur) P(A+B) = P(A) + P(B) – P(A and B) Adding n such a way that every outcomes s counted only once

Definition Event A and B are disjoint or mutually exclusive, if they can not occur at the same time

Rules of complementary events

Multiplication rule P(A and B)=P(event A occurs in a first trial and event B in a second trial)

Conditional probability P(B|A) represents the probability of event B occurring after event has already occurred

Definition Event A and B are independent, if occurrence of one does not affect the probability of the occurrence of the other

Formal multiplication rule P(A and B) = P(A). P(B|A)

Probability of at least one “At least one” is equivalent to “one or more” The complement of getting at least one of a particular is get no item of that type

Conditional probability Conditional probability of an event is the probability with additional information that some other event has already occurred

Bayes’ theorem Dealing with sequential events Revise a probability value base on additional information that s later obtained

Definitions A prior probability is an initial probability obtained before any additional information A posterior probability is a probability that has been revised by using additional information that is later obtained

Definitions Absolute risk reduction = | P(event occurring in treatment group ) – P(event occurring in control group ) | From table 3-4 Absolute risk reduction =

Definitions Relative risk is the ratio P t / P c –Pt is the proportion of the characteristic in treatment group –Pc is the proportion in control group Using table 3-4 Absolute risk reduction = P t / P c =

Definitions Number needed to treat = 1 / absolute risk reduction Rounded up to next larger whole number

Definitions Actual odds against event A = –Expressed in the form of m:n Actual odds in favor of event A = –Expressed in the form of n:m

Definitions Odds ratio = Using table 3-4, odds ratio = ad / bc

Relative risk versus odds ratio Prospective study: relative risk, odds ratio Retrospective study: odds ratio only

Definitions Rate = –a = frequency count of the number of people that event occurred –b = total number of people exposed to the risk of the event occurring –k = multiplier number

Counting rule For a sequence of 2 events, first event can occur m ways and the second can occur n ways, the events together can occur a total of m*n ways

Factorial Denotes the product of decreasing positive whole number Example, 4! = 4*3*2*1 =24

Permutation rule Select r items from n available items If there is n items with some items are identical to others, number of permutation is

Combination rule Number of combination of r items selected from n different items