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Transcript of 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