chapter 10 copyright kaplan university 2009. the drawing of conclusions by the use of quantitative...

Chapter 10Copyright Kaplan University 2009Copyright Kaplan University 2009

The drawing of conclusions by the use

of quantitative or qualitative information

Inductive Finding valid

answers from examination of the data

Deductive Finding valid

answers using mathematic applications against the data

Inductive Studying

relationship between two types of data

Ex: The rate of hypertension among smokers

Deductive Proving or

disproving of a hypothesis

Ex: Smoking causes high blood pressure

An equation may be written using the same formula and have different applications in math or statistics

Consider the equation: y = mx + b Mathematically: Formula for defining a

straight line in geometry Statistically: Formula for simple regression

analysis

Null Hypothesis States there is no

difference between the means of the two compared groups being studied

Alternative Hypothesis States that there is

a true difference between the means of the two compared groups being studied

This list simplifies the steps to testing a null hypothesis for Statistical Significance

Generally p (false positive) = .05 or 5% Leads to a 95% confidence interval of

arriving at the right hypothesis

Obtain a “p” value for the data Example:

Standard Deviation Confidence Intervals Mean, Mode and Median Student t-test (1 or 2 tailed)

Compare the p values (answers) to the alpha level

Does this answer satisfy the null or alternative hypothesis?

H0: Men who eat pizza three times a week will gain ten pounds over the three week period (Null Hypothesis)

H1: Men who eat pizza three times a week will not gain ten pounds over the three week period (Alternative Hypothesis)

Subjects PRE Wt POST WT Difference 1 128 138 10 2 100 110 10 3 110 120 10 4 145 41 -4 5 201 215 4 6 200 201 1 7 198 196 -2 8 157 157 0 9 300 289 -11 10 194 195 1 Mean 173 176

Do you accept or reject the null hypothesis

Do you accept or reject the alternative hypothesis

Most commonly used statistical test in medicine

Compares means of the variables of two research samples (groups)

May be used in research groups which differ i.e. Male/Female; dogs/cats

May be 1 or 2 tailed (which affects interpretation)

In regards to the “t” test and the use of “p” If “t” is large (means of samples) then “p” is

small (percentage of error) and the difference is regarded as real (i.e. believable)

If “p” is large (larger than 5%) then the difference is not real (unrealistic or unbelievable)

INTRODUCTION TO PREVENTATIVE MEDICINE

Promotes general health Prevention of disease

Application of epidemiological concepts Aid in prevention Aid in promotion

A state of complete physical, mental and social well-being, not merely the absence of disease or infirmity

-World Health Organization

Good Known as Eustress

Exercise Infant stimulation

Bad Known as distress

Mal-adaption Environmental

Mortality Data Life Expectancy Quality of Adjusted Life Years (QALY)

Latent: Also known as: “hidden” Offers a window of opportunity for early

detection Symptomatic:

Produces clinical manifestations that are measurable

Tertiary: Disease progression in the absence of

intervention

Primary: Eliminate the cause of disease Example: Vaccinations

Secondary: Interrupt the disease process prior to

symptoms occuring Example: Medication or Surgical intervention

Tertiary: Limiting physical and social consequences of

symptomatic disease Example: Rehabilitation/therapy

Nutritional Factors – How important is this factor?

How can nutritional issues be addressed within the scope of preventive medicine.

What is the difference between Environmental and Occupational health

promotion?

Explore routes of exposure to environmental hazards. How dangerous

are these?What are some sources?

Behavioral factors: How do we promote prevention here. Someone cite an example and let us discuss briefly?

HS 310: Epidemiology and StatisticsCopyright Kaplan University 2009

Sample Size: Used to determine time and amount of

funding needed for research Directly affects presence of statistical

significance Defines the realism of the proposed research

Need for paired data Will there be large/small variance in

variables of interest? Consideration of Beta and/or Alpha Errors Acceptance of 95% Confidence

Interval/5% Error 1 sided or 2-sided t-test Degree of difference desired

Calculation of Paired t-test w/Alpha Error onlyFormula: N = (zx)2 . (s)2

(d)2

Plug in the #:N = (1.96)2 . (15) 2

(10) 2

Work from Center: N = (3.84) (225)100

Can you solve from here, what is the answer?

8.64 = “9”

Consider the differences in the equationN = (zx)2 . 2 .(s)2

(d)2

Again work from center: N = (1.96)2 . (2) . (15)2

(10)2 Can you solve for “N”

17.28 or 18

Utilizing Page 200 again, Box 12-2 for the numbersN = (zx + zb) 2 . (2) . (s)2

(d) 2

N = (1.96 + 0.84) 2 . (2) . (15) 2

(10) 2

Solve for “N”

35.28 (nope)70.56 (NOPE)

72(remember you have to have the same

number in both categories so even though 70.56 is numerically correct you cannot divide 71 into 2 even groups.)

Also 36(2) = 72

A method of assigning subjects to the control or experimental group in such a way that the choice is in no way influenced. Example of ways to randomize:

Can you think of some?

Simple Random Allocation Use of random

numbers table

Randomization into groups of “2” Used to get 2

groups with same number of participants

Systematic Allocation Assign 1st person

to group 1, second person to group 2 and so on.

Stratified Allocation Used in clinical

research whereas patients are assigned to certain groups according to severity of their condition

Independence Rule: One probability is not influenced by the outcome of another probability

Product Rule: determination that the probability of two things being true

Addition Rule: Determination that the probability of one thing being true under all possibilities.

Multivariable Statistics Involves more than one variable These variables are called “multivariable

models”

Determination of interactions between variables

To develop prediction models in clinical settings

Adjust inter-group differences Useful in propensity matching and

scoring

ANOVA – Analysis of Variance Definition: Use to analyze results of

experimental studies or categorical independent variables

Two types: 1-way ANOVA (aka F-Test) Comparison of more than two means

simultaneously Involves estimating the independent variance in

one of two ways

Type I = Between groups Type II = within groups

N-way ANOVA Aka 2-way ANOVA Testing of two or more independent variables

ANCOVA: Analysis of Covariable Definition: Method of analyzing continuous

dependent variables MLR: Multiple Linear Regression

Definition: Method of analyzing dependent variables and all independent variables which are continous

Most common is the “stepwise linear regression”

Not used much in clinical medicine