1. Chapter 10 Copyright Kaplan University 2009

2. The drawing of conclusions by the use of quantitative or qualitative information

4.  Inductive  Finding valid answers from examination of the data  Deductive  Finding valid answers using mathematic applications against the data

5.  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

6.  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

7.  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

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

9.  Generally p (false positive) =.05 or 5%  Leads to a 95% confidence interval of arriving at the right hypothesis

10.  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?

11.  H 0 : Men who eat pizza three times a week will gain ten pounds over the three week period (Null Hypothesis)  H 1 : Men who eat pizza three times a week will not gain ten pounds over the three week period (Alternative Hypothesis)

12.  SubjectsPRE WtPOST WTDifference  112813810  210011010  311012010  414541-4  52012154  62002011  7198196-2  81571570  9300289-11  101941951  Mean173176

13.  Do you accept or reject the null hypothesis  Do you accept or reject the alternative hypothesis

15.  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)

16.  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)

17. INTRODUCTION TO PREVENTATIVE MEDICINE

18.  Promotes general health  Prevention of disease  Application of epidemiological concepts  Aid in prevention  Aid in promotion

19.  A state of complete physical, mental and social well-being, not merely the absence of disease or infirmity -World Health Organization

20.  Good  Known as Eustress  Exercise  Infant stimulation  Bad  Known as distress  Mal-adaption  Environmental

21.  Mortality Data  Life Expectancy  Quality of Adjusted Life Years (QALY)

22.  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

23.  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

25.  Nutritional Factors – How important is this factor?

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

28. What is the difference between Environmental and Occupational health promotion?

29. Explore routes of exposure to environmental hazards. How dangerous are these? What are some sources?

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

31. HS 310: Epidemiology and Statistics Copyright Kaplan University 2009

32.  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

33.  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

34.  Calculation of Paired t-test w/Alpha Error only Formula: 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?

35. 8.64 = “9”

36.  Consider the differences in the equation N = (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”

37. 17.28 or 18

38.  Utilizing Page 200 again, Box 12-2 for the numbers N = (z x + z b ) 2. (2). (s) 2 (d) 2 N = (1.96 + 0.84) 2. (2). (15) 2 (10) 2 Solve for “N”

39. 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

40.  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?

41.  Simple Random Allocation  Use of random numbers table  Randomization into groups of “2”  Used to get 2 groups with same number of participants

42.  Systematic Allocation  Assign 1 st 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

43.  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.

44.  Multivariable Statistics  Involves more than one variable  These variables are called “multivariable models”

45.  Determination of interactions between variables  To develop prediction models in clinical settings  Adjust inter-group differences  Useful in propensity matching and scoring

46.  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

47.  Type I = Between groups  Type II = within groups  N-way ANOVA  Aka 2-way ANOVA  Testing of two or more independent variables

48.  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