# Estimating Risk. Sukon Kanchanaraksa, PhD Johns Hopkins University

Save this PDF as:

Size: px
Start display at page:

## Transcription

2 stimating Risk Sukon Kanchanaraksa, PhD Johns Hopkins University

3 Section A Relative Risk

4 Risk Incidence of Disease (Attack Rate) = Absolute Risk 4

5 Attack Rates from Food-Borne Outbreak xercise Attack Rate (%) Food (1) Ate (2) ot Ate gg salad Macaroni Cottage cheese Tuna salad Ice cream Other

6 Attack Rates from Food-Borne Outbreak xercise Attack Rate (%) Difference of Attack Rates Food (1) Ate (2) ot Ate (1) (2) gg salad Macaroni Cottage cheese Tuna salad Ice cream Other

7 Attack Rates from Food-Borne Outbreak xercise Attack Rate (%) Difference of Attack Rates Ratio of Attack Rates Food (1) Ate (2) ot Ate (1) (2) (1)/(2) gg salad Macaroni Cottage cheese Tuna salad Ice cream Other

8 Approaches to the Measurement of xcess Risk Ratio of risks Risk in exposed Risk in non exposed Differences in risks (Risk in exposed) (Risk in non-exposed) 8

9 Relative Risk or Risk Ratio Relative risk (RR) = Risk in exposed Risk in non-exposed 9

10 Cohort Study First, identify Then follow to see whether Disease develops Disease does not develop Calculate and compare Totals xposed a b a+b ot exposed c d c+d Incidence of disease a a+b c c+d a a+b = Incidence in exposed c c+d = Incidence in not exposed 10

11 Cohort Study First, identify Then follow to see whether Disease develops Disease does not develop Calculate and compare Totals xposed a b a+b ot exposed a = Incidence in exposed a+b c = Incidence in not exposed c+d c d c+d Relative Risk = Incidence of disease a a+b c c+d a a+b c c+d 11

12 Cohort Study Then follow to see whether calculate First select Smoke cigarettes Do not smoke cigarettes Develop CHD Do not develop CHD Totals Incidence of disease Relative Risk = = =

13 Interpreting Relative Risk of a Disease If RR = 1 Risk in exposed = Risk in non-exposed o association If RR > 1 Risk in exposed > Risk in non-exposed Positive association;? causal If RR < 1 Risk in exposed < Risk in non-exposed egative association;? protective 13

14 Cross-Tabulation Table (Food-Borne Outbreak xercise) Attack Rates of Sore Throat gg Salad Ate Did not eat Tuna Salad Ate Did not eat 46/53 (87%) 8/12 (67%) 3/10 (30%) 3/10 (30%) 14

15 Cross-Tabulation Table (Food-Borne Outbreak xercise) Relative Risk of Sore Throat gg Salad Ate Did not eat Tuna Salad Ate Did not eat The baseline group for comparison is the no exposure group i.e., those who did not eat tuna salad and did not eat egg salad 15

16 xposure-disease Tables xpanded from the Cross- Tabulation Table (Food-Borne Outbreak xercise) Sore Throat Sore Throat Both Tuna Salad and gg Salad Yes o Total Ate Did not eat either RR = (46/53)/(3/10) =2.9 Tuna Salad Only Yes o Total Ate Did not eat either RR = (3/10)/(3/10) =1.0 Sore Throat gg Salad Only Yes o Total Ate Did not eat either RR = (8/12)/(3/10) =2.2 16

17 Relative Risk by Food Items o tuna salad Ate tuna salad 2 Relative Risk 1 +Tuna +gg o gg Salad Ate gg Salad 17

18 Relative Risk for MI and CHD Death in Men Aged in Relation to Cigarette Smoking 5 Cholesterol Levels Low 5 Blood Pressure < 130 mmhg 4 High* mmhg Relative Risk on-smoker Smoker on-smoker Smoker * High > 220 mg/100 cc Source: Doyle et al,

19 Relationship between Serum Cholesterol Levels and Risk of Coronary Heart Disease by Age and Sex Serum Men Women Cholesterol mg/dl Aged Aged Aged Aged Incidence Rates (per 1,000) < Source: Doyle et al,

20 Incidence Rates and RR of CHD in Relation to Serum Cholesterol Levels by Age and Sex Serum Men Women Cholesterol mg/dl Aged Aged Aged Aged Incidence Rates (per 1,000) < Relative Risk* < * RR of 1.0 set at level for males yrs of age with cholesterol level < 190 mg/dl. 20

21 Incidence Rates and RR of CHD in Relation to Serum Cholesterol Levels by Age and Sex Serum Men Women Cholesterol mg/dl Aged Aged Aged Aged Incidence Rates (per 1,000) < Relative Risk* < * RR of 1.0 set at level for males yrs of age with cholesterol level < 190 mg/dl. 21

22 Section B Odds Ratio

23 Interpreting Odds Odds is often known as the ratio of money that may be won versus the amount of money bet In statistics, an odds of an event is the ratio of: The probability that the event WILL occur to the probability that the event will OT occur For example, in 100 births, the probability of a delivery being a boy is 51% and being a girl is 49% The odds of a delivery being a boy is 51/49 = 1.04 In simpler term, an odds of an event can be calculated as: umber of events divided by number of non-events 23

24 Calculating Risk in a Cohort Study Develop Disease Do ot Develop Disease xposed a b on-exposed c d The probability that an exposed person develops disease = a a + b The probability that a non-exposed person develops disease = c c + d 24

25 Applying Concept of Odds Let s borrow the concept of odds and apply it to disease and non-disease So, the odds of having the disease is the ratio of the probability that the disease will occur to the probability that the disease will not occur Or, the odds of having the disease can be calculated as the number of people with the disease divided by the number of people without the disease [ote: in the exposure-disease 2x2 table, the odds of having a disease in the exposed group is the same as the odds that an exposed person develops the disease] 25

26 Calculating Odds in a Cohort Study Develop Disease Do ot Develop Disease xposed a b on-exposed c d The odds that an exposed person develops disease = a b The odds that a non-exposed person develops disease = c d 26

27 Calculating Odds in a Cohort Study Odds ratio is the ratio of the odds of disease in the exposed to the odds of disease in the non-exposed OR = Develop Disease Do ot Develop Disease xposed a b on-exposed c d odds that an exposed person develops the disease odds that a non - exposed person develops the disease = a b c d 27

28 Disease Odds Ratio in a Cohort Study OR = a b c d = a b x d c = ad bc 28

29 Calculating Odds Ratio in a Case-Control Study History of xposure o History of xposure Case a c Control b d The odds that a case was exposed = The odds that a control was exposed = a c b d 29

30 Calculating Odds Ratio in a Case-Control Study History of xposure o History of xposure Case a c Control b d Odds ratio (OR) is the ratio of the odds that a case was exposed to the odds that a control was exposed a odds that a case was exposed OR = = c odds that a control was exposed b d 30

31 xposure Odds Ratio in a Case-Control Study OR = a c b d = a c x d b = ad bc 31

32 Odds Ratio versus Relative Risk Odds ratio can be calculated in a cohort study and in a casecontrol study The exposure odds ratio is equal to the disease odds ratio Relative risk can only be calculated in a cohort study 32

33 When Is Odds Ratio a Good stimate of Relative Risk? When the cases studied are representative of all people with the disease in the population from which the cases were drawn, with regards to history of the exposure When the controls studied are representative of all people without the disease in the population from which the cases were drawn, with regards to history of exposure When the disease being studied is not a frequent one 33

34 When Is Odds Ratio a Good stimate of Relative Risk? If the incidence of the disease is low, then: a+b ~ b c+d ~ d Therefore: RR = ~ a/(a+b) c/(c+ d) a/b c/d = ad bc = OR 34

35 Comparing OR to RR: Disease Is Infrequent Develop Disease Do not Develop Disease xposed ,000 on- xposed ,000 Relative Risk = Odds Ratio = 200/10, /10, x x 9800 = 2 =

36 Comparing OR to RR: Disease Is OT Infrequent Develop Disease Do not Develop Disease xposed on- xposed Relative Risk = 50/75 50/25 50 x 75 Odds Ratio = 50 x 25 = 2 = 3 36

37 Interpreting Odds Ratio of a Disease If OR = 1 xposure is not related to disease o association; independent If OR > 1 xposure is positively related to disease Positive association;? causal If OR < 1 xposure is negatively related to disease egative association;? protective 37

38 Section C Odds Ratio in Unmatched and Matched Case-Control

39 Unmatched Case-Control Study: xample CAS COTROL Assume a study of 10 cases and 10 unmatched controls, with these findings = xposed = ot exposed 39

40 Unmatched Case-Control Study: xample CAS COTROL Thus, 6 of 10 cases were exposed, and 3 of 10 controls were exposed. In a 2x2 table, we have the following: Case Control xposed 6 3 ot xposed 4 7 = xposed = ot exposed 40

41 Unmatched Case-Control Study: xample CAS COTROL Case Control xposed 6 3 ot xposed 4 7 OR = ad bc = 6 x 7 3 x 4 = 3.5 = xposed = ot exposed 41

42 Quick Pause In a hypothetical 2x2 table with the following rows and columns, is the OR calculated correctly? Control Case xposed 8 3 ot xposed 4 7 OR = ad bc = 8 x 7 3 x 4 =

43 Quick Pause Control Case xposed 8 3 ot xposed 4 7 Incorrect! OR = ad bc = 8 x 7 3 x 4 = 4.7 Why? 43

44 Odds Ratio in a Case-Control Study OR = a c b d = a c x d b = ad bc = (# cases exposed) x (# controls not exposed) (# cases not exposed) x (# controls exposed) The numerator is the product of cases exposed and controls not exposed. 44

45 Case-Control Study: xample Cases CHD Controls (without disease) Smoked cigarettes Did not smoke cigarettes Total % Smoking cigarettes = 56% = 44% OR = ad bc = 112 x x 88 =

46 Matched Case-Control Study In a matched case-control study, one or more controls are selected to match to a case on certain characteristics, such as age, race, and gender When one control is matched to a case, the case and the matched control form a matched pair 46

47 Concordant and Discordant Pairs We can define two types of matched pairs by the similarity or difference of the exposure of the case and control in each pair Concordant pairs are: 1. Pairs in which both the case and the control were exposed, and 2. Pairs in which neither the case nor the control was exposed Discordant pairs are: 3. Pairs in which the case was exposed but the control was not, and 4. Pairs in which the control was exposed and the case was not 47

48 2x2 Table in a Matched Case-Control Study Discordant xposed Controls ot xposed Cases xposed ot xposed Concordant 48

49 2x2 Table in a Matched Case-Control Study aa = number of matched pairs 2 x aa subjects in this cell xposed Controls ot xposed Cases xposed aa bb ot xposed cc dd Total number of subjects = 2 x (aa+bb+cc+dd) 49

50 OR from 2x2 Table in a Matched Case-Control Study Odds ratio (matched) = bb cc xposed Controls ot xposed Cases xposed aa bb ot xposed cc dd ote: bb is not the product of b and b (not b x b); it is the number of pairs 50

51 Matched Case-Control Study: xample CAS COTROL Assume a study of 10 cases and 10 controls in which each control was matched to a case resulting in 10 pairs. = xposed = ot exposed 51

52 Matched Case-Control Study: xample CAS COTROL Cases xposed Controls ot xposed xposed 2 4 ot xposed 1 3 Matched OR = 4 1 = 4 = xposed = ot exposed 52

53 Review: Matched Case-Control Study Cases xposed Controls ot xposed xposed 2 4 ot xposed 1 3 Q1. How many pairs? Q2. How many subjects? Q3. What are the discordant pairs? Q4. Which is the bb cell? Q5. What is the bb cell? 53

54 Review: Unmatching a Matched 2x2 Table Matched CC Controls xposed ot xposed Cases xposed 2 4 ot xposed 1 3 Disease Unmatched Yes o 2x2 xposure xposed ot xposed 54

55 Section D Attributable Risk

56 Attributable Risk Attributable risk (AR) is a measure of excess risk that is attributed to the exposure Attributable risk in the exposed group equals the difference between the incidence in the exposed group and the incidence in the non-exposed (baseline) group 56

57 Attack Rates from Food-Borne Outbreak xercise Attack Rate (%) Difference of Attack Rates Food (1) Ate (2) ot Ate (1) (2) gg salad Macaroni Cottage cheese Tuna salad Ice cream Other

58 Risk in xposed and on-xposed Groups Background Risk xposed group on-exposed group 58

59 Risk in xposed and on-xposed Groups Incidence due to exposure Attributable risk Incidence not due to exposure Background Risk xposed group on-exposed group 59

60 Risk in xposed and on-xposed Groups 1. Incidence attributable to exposure (attributable risk) = ( Incidence in ) ( Incidence in ) exposed group non-exposed group 60

61 Risk in xposed and on-xposed Groups 1. Incidence attributable to exposure (attributable risk) = ( Incidence in ) ( Incidence in ) exposed group non-exposed group 2. Proportion of incidence attributable to exposure (proportional attributable risk) = ( Incidence in ) ( Incidence in ) exposed group non-exposed group Incidence in exposed group 61

62 xample: Cohort Study Develop CHD Do not develop CHD Totals Incidence of disease Smoke cigarettes Do not smoke cigarettes per 1, per 1,000 62

63 Attributable Risk in Smokers 1. The incidence in smokers which is attributable to their smoking = ( Incidence in ) ( Incidence in ) smokers non-smokers = = 10.6/1,000/year 63

64 Proportion Attributable Risk in Smokers 2. The proportion of the total incidence in the smokers which is attributable to their smoking = ( Incidence in ) ( Incidence in ) smokers non-smokers Incidence in smokers = = = = 37.9% 64

65 Risk in the Total Population Population is a mix of exposed and non-exposed groups 65

66 Attributable Risk in the Total Population 3. Incidence attributable to exposure = ( Incidence in ) ( Incidence in ) total population non-exposed group 66

67 Attributable Risk in the Total Population 3. Incidence attributable to exposure =( Incidence in ) ( Incidence in ) total population non-exposed group 4. Proportion of incidence attributable to exposure = ( Incidence in ) ( Incidence in ) total population non-exposed group Incidence in total population 67

68 Attributable Risk in the Total Population 3. Incidence attributable to smoking in the total population = ( Incidence in ) ( Incidence in ) total population non-exposed group 68

69 Attributable Risk in the Total Population If the incidence in the total population is unknown, it can be calculated if we know: Incidence among smokers Incidence among nonsmokers Proportion of the total population that smokes 69

70 Attributable Risk in the Total Population We know that: The incidence in smokers = 28.0/1,000/year The incidence in nonsmokers = 17.4/1,000/year From another source, we learn that: The proportion of smokers in the population is 44% So, we know that: The proportion of nonsmokers in the population is 56% 70

71 Attributable Risk in the Total Population Incidence in total population = ( Incidence) Percent smokers in in smokers population ( ) ( )+ Incidence in nonsmokers Percent non-smokers in population ( ) (28.0/1000) (.44) + (17.4/1000) (.56) = 22.1/1000/year 71

72 Attributable Risk in the Total Population 3. Incidence attributable to smoking = ( Incidence in total population ) ( Incidence in ) non-smokers (22.1/1000/year) (17.4/1000/year) = 4.7/1000/year 72

73 Attributable Risk in the Total Population 4. Proportion of incidence attributable to exposure =( Incidence in ) ( Incidence in ) total population non-smokers Incidence in total population = 21.3% 73

74 Lung Cancer, CHD Mortality in Male British Physicians Age-Adjusted Death Rates/100,000 Smokers on-smokers RR AR %AR Lung cancer % CHD % %AR = Proportion attributable risk Source: Doll and Peto (1976). BMJ, 2:

75 Lung Cancer, CHD Mortality in Male British Physicians Age-Adjusted Death Rates/100,000 Smokers on-smokers RR AR %AR Lung cancer % CHD % %AR = Proportion attributable risk Source: Doll and Peto (1976). BMJ, 2:

### Measurement in Epidemiology: Frequency, Association, and Impact

Measurement in Epidemiology: Frequency, Association, and Impact Mayfong Mayxay M.D., Ph.D. (Trop Med) GFMER - WHO - UNFPA - LAO PDR Training Course in Reproductive Health Research Vientiane, 12 October

### Section 3 Part 2. Describing Relationships Between Two Nominal Characteristics. PubH 6414 Section 3 Part 2 1

Section 3 Part 2 Describing Relationships Between Two Nominal Characteristics PubH 6414 Section 3 Part 2 1 Measuring relationship between two variables The Relative Risk and the Odds Ratio are measures

### Case-control studies. Alfredo Morabia

Case-control studies Alfredo Morabia Division d épidémiologie Clinique, Département de médecine communautaire, HUG Alfredo.Morabia@hcuge.ch www.epidemiologie.ch Outline Case-control study Relation to cohort

### P (B) In statistics, the Bayes theorem is often used in the following way: P (Data Unknown)P (Unknown) P (Data)

22S:101 Biostatistics: J. Huang 1 Bayes Theorem For two events A and B, if we know the conditional probability P (B A) and the probability P (A), then the Bayes theorem tells that we can compute the conditional

### Statistics. Annex 7. Calculating rates

Annex 7 Statistics Calculating rates Rates are the most common way of measuring disease frequency in a population and are calculated as: number of new cases of disease in population at risk number of persons

### Determinants of mortality

International Task Force for Prevention Of Coronary Heart Disease Coronary heart disease and stroke: Risk factors and global risk Slide Kit 4 (Prospective Cardiovascular Münster Heart Study) Determinants

### The American Cancer Society Cancer Prevention Study I: 12-Year Followup

Chapter 3 The American Cancer Society Cancer Prevention Study I: 12-Year Followup of 1 Million Men and Women David M. Burns, Thomas G. Shanks, Won Choi, Michael J. Thun, Clark W. Heath, Jr., and Lawrence

### Exercise Answers. Exercise 3.1 1. B 2. C 3. A 4. B 5. A

Exercise Answers Exercise 3.1 1. B 2. C 3. A 4. B 5. A Exercise 3.2 1. A; denominator is size of population at start of study, numerator is number of deaths among that population. 2. B; denominator is

### COHORT STUDIES. Concept of a cohort : A group of individuals that are all similar in some trait and move forward together as a unit.

OCW Epidemiology and Biostatistics, 2010 Alice Tang Tufts University School of Medicine October 5, 2010 COHORT STUDIES Learning objectives for this session: 1) Know when it is appropriate/feasible to use

### 6. Applications of Probability in Epidemiology

BioEpi540W 6. Applications of Probability in Epidemiology Page 1 of 17 6. Applications of Probability in Epidemiology Topics 1. Probability in Diagnostic Testing.. 3 a. Prevalence.. 3 b. Incidence. 3 c.

### MEASURING ASSOCIATIONS IN EPIDEMIOLOGIC STUDIES. Dr. Ian Gardner University of California Davis

MEASURING ASSOCIATIONS IN EPIDEMIOLOGIC STUDIES Dr. Ian Gardner University of California Davis Measures of association Association means relationship Various type of measures of how numerically strong

### Lesson 14 14 Outline Outline

Lesson 14 Confidence Intervals of Odds Ratio and Relative Risk Lesson 14 Outline Lesson 14 covers Confidence Interval of an Odds Ratio Review of Odds Ratio Sampling distribution of OR on natural log scale

### Chapter 7: Effect Modification

A short introduction to epidemiology Chapter 7: Effect Modification Neil Pearce Centre for Public Health Research Massey University Wellington, New Zealand Chapter 8 Effect modification Concepts of interaction

### African Americans & Cardiovascular Diseases

Statistical Fact Sheet 2013 Update African Americans & Cardiovascular Diseases Cardiovascular Disease (CVD) (ICD/10 codes I00-I99, Q20-Q28) (ICD/9 codes 390-459, 745-747) Among non-hispanic blacks age

### Bayes Theorem & Diagnostic Tests Screening Tests

Bayes heorem & Screening ests Bayes heorem & Diagnostic ests Screening ests Some Questions If you test positive for HIV, what is the probability that you have HIV? If you have a positive mammogram, what

### Launch of the MOH Clinical Practice Guidelines on Screening for Cardiovascular Disease and Risk Factors. 23rd April 2011

Launch of the MOH Clinical Practice Guidelines on Screening for Cardiovascular Disease and Risk Factors 23rd April 2011 Global Risk Assessment Dr Low Lip Ping Low Cardiology Clinic Global Risk Assessment

### Underwriting Critical Illness Insurance: A model for coronary heart disease and stroke

Underwriting Critical Illness Insurance: A model for coronary heart disease and stroke Presented to the 6th International Congress on Insurance: Mathematics and Economics. July 2002. Lisbon, Portugal.

### Bivariate Analysis. Comparisons of proportions: Chi Square Test (X 2 test) Variable 1. Variable 2 2 LEVELS >2 LEVELS CONTINUOUS

Bivariate Analysis Variable 1 2 LEVELS >2 LEVELS CONTINUOUS Variable 2 2 LEVELS X 2 chi square test >2 LEVELS X 2 chi square test CONTINUOUS t-test X 2 chi square test X 2 chi square test ANOVA (F-test)

### 1. What is the probability a passenger died given they were female? 2. What is the probability a passenger died given they were male?

RELATIVE RISK AND ODDS RATIOS Other summaries that are often computed when investigating the relationship between two categorical variables are the relative risk ratio and the odds ratio. EXAMPLE: Consider

### Identification of Non-Smokers for the Workers Compensation Board of Manitoba Lung Cancer and Fire Fighting Policy

Identification of Non-Smokers for the Workers Compensation Board of Manitoba Lung Cancer and Fire Fighting Policy Submitted by Allen Kraut, MD, FRCPC Associate Professor Departments of Internal Medicine

### The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board.

The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board. Case Control Study Mark A. Kaelin Department of Health Professions

### UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Chapter 10 Chi-Square Test Relative Risk/Odds Ratios

UCLA STAT 3 Introduction to Statistical Methods for the Life and Health Sciences Instructor: Ivo Dinov, Asst. Prof. of Statistics and Neurology Chapter 0 Chi-Square Test Relative Risk/Odds Ratios Teaching

### Mortality Assessment Technology: A New Tool for Life Insurance Underwriting

Mortality Assessment Technology: A New Tool for Life Insurance Underwriting Guizhou Hu, MD, PhD BioSignia, Inc, Durham, North Carolina Abstract The ability to more accurately predict chronic disease morbidity

### Cholesterol Treatment Trialists (CTT) Collaboration. Slide deck

Cholesterol Treatment Trialists (CTT) Collaboration Slide deck CTT Collaboration: Background* History: Founded in 1993 (prior to publication of 4S trial in 1994) Original protocol published in 1995 Trial

Goals of This Course Be able to understand a study design (very basic concept) Be able to understand statistical concepts in a medical paper Be able to perform a data analysis Understanding: PECO study

### EPIDEMIOLOGY EXAM I REVIEW, FALL 2009

Topic 1. Case study: Hepatitis outbreak. 1. Characterizing outbreaks by time. Point source. Calculating likely exposure period... (latest reported case longest incubation time) (first reported case shortest

### Epidemiology-Biostatistics Exam Exam 2, 2001 PRINT YOUR LEGAL NAME:

Epidemiology-Biostatistics Exam Exam 2, 2001 PRINT YOUR LEGAL NAME: Instructions: This exam is 30% of your course grade. The maximum number of points for the course is 1,000; hence this exam is worth 300

### Longevity Risk in the United Kingdom

Institut für Finanz- und Aktuarwissenschaften, Universität Ulm Longevity Risk in the United Kingdom Stephen Richards 20 th July 2005 Copyright c Stephen Richards. All rights reserved. Electronic versions

### Confounding in Epidemiology

The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board. Confounding in Epidemiology Mona Baumgarten Department of Epidemiology

### Simple Sensitivity Analyses for Matched Samples. CRSP 500 Spring 2008 Thomas E. Love, Ph. D., Instructor. Goal of a Formal Sensitivity Analysis

Goal of a Formal Sensitivity Analysis To replace a general qualitative statement that applies in all observational studies the association we observe between treatment and outcome does not imply causation

### Andrews Publications Tobacco Litigation 2000 THEORIES FOR THE REDUCTION OF DAMAGES

THEORIES FOR THE REDUCTION OF DAMAGES By Steven Wright Brita J. Forssberg SYNERGISM Effect of cigarette smoking is greater than that of asbestos. Synergism Synergism Lung cancer incidence rates, expressed

### When Does it Make Sense to Perform a Meta-Analysis?

CHAPTER 40 When Does it Make Sense to Perform a Meta-Analysis? Introduction Are the studies similar enough to combine? Can I combine studies with different designs? How many studies are enough to carry

### Canadian Individual Critical Illness Insurance Morbidity Experience

Morbidity Study Canadian Individual Critical Illness Insurance Morbidity Experience Between Policy Anniversaries in 2002 and 2007 Using Expected CIA Incidence Tables from July 2012 Individual Living Benefits

### Mind on Statistics. Chapter 4

Mind on Statistics Chapter 4 Sections 4.1 Questions 1 to 4: The table below shows the counts by gender and highest degree attained for 498 respondents in the General Social Survey. Highest Degree Gender

### Logistic Regression: Use & Interpretation of Odds Ratio (OR)

Logistic Regression: Use & Interpretation of Odds Ratio (OR) Fu-Lin Wang, B.Med.,MPH, PhD Epidemiologist Adjunct Assistant Professor Fu-lin.wang@gov.ab.ca Tel. (780)422-1825 Surveillance & Assessment Branch,

### Dealing with confounding in the analysis

Chapter 14 Dealing with confounding in the analysis In the previous chapter we discussed briefly how confounding could be dealt with at both the design stage of a study and during the analysis of the results.

### Estimation of the Number of Lung Cancer Cases Attributable to Asbestos Exposure

Estimation of the Number of Lung Cancer Cases Attributable to Asbestos Exposure BC Asbestos Statistics Approximately 55,000 BC men and women exposed in 1971 in high exposed industries Significant exposure

### Absolute cardiovascular disease risk assessment

Quick reference guide for health professionals Absolute cardiovascular disease risk assessment This quick reference guide is a summary of the key steps involved in assessing absolute cardiovascular risk

### What is meant by "randomization"? (Select the one best answer.)

Preview: Post-class quiz 5 - Clinical Trials Question 1 What is meant by "randomization"? (Select the one best answer.) Question 2 A. Selection of subjects at random. B. Randomization is a method of allocating

### Journal Club: Niacin in Patients with Low HDL Cholesterol Levels Receiving Intensive Statin Therapy by the AIM-HIGH Investigators

Journal Club: Niacin in Patients with Low HDL Cholesterol Levels Receiving Intensive Statin Therapy by the AIM-HIGH Investigators Shaikha Al Naimi Doctor of Pharmacy Student College of Pharmacy Qatar University

### Course Notes Frequency and Effect Measures

EPI-546: Fundamentals of Epidemiology and Biostatistics Course Notes Frequency and Effect Measures Mat Reeves BVSc, PhD Outline: I. Quantifying uncertainty (Probability and Odds) II. Measures of Disease

### The concept of probability is fundamental in statistical analysis. Theory of probability underpins most of the methods used in statistics.

Elementary probability theory The concept of probability is fundamental in statistical analysis. Theory of probability underpins most of the methods used in statistics. 1.1 Experiments, outcomes and sample

### The Role of Insurance in Providing Access to Cardiac Care in Maryland. Samuel L. Brown, Ph.D. University of Baltimore College of Public Affairs

The Role of Insurance in Providing Access to Cardiac Care in Maryland Samuel L. Brown, Ph.D. University of Baltimore College of Public Affairs Heart Disease Heart Disease is the leading cause of death

### Size of a study. Chapter 15

Size of a study 15.1 Introduction It is important to ensure at the design stage that the proposed number of subjects to be recruited into any study will be appropriate to answer the main objective(s) of

### TESTS FOR CATEGORICAL DATA ONE-SAMPLE TEST FOR A BINOMIAL PROPORTION. H0: p = p0 vs. H0: p p0

TESTS FOR CATEGORICAL DATA ONE-SAMPLE TEST FOR A BINOMIAL PROPORTION H0: p = p0 vs. H0: p p0 Bernoulli trials: 0, 1, 0, 0, 1,... - independent trials Pr{x=1}=p Number of successes in a series of n trials

### Basic Health Statistics. Porcupine Health Unit

Basic Health Statistics Porcupine Health Unit 2012 . Basic Health Statistics Porcupine Health Unit 2012 . Table of Contents Demographics...7 a. Population Size...7 Figure 1: Percentage change in the Population

### Prognostic Outcome Studies

Prognostic Outcome Studies Edwin Chan PhD Singapore Clinical Research Institute Singapore Branch, Australasian Cochrane Centre Duke-NUS Graduate Medical School Contents Objectives Incidence studies (Prognosis)

### Guide to Biostatistics

MedPage Tools Guide to Biostatistics Study Designs Here is a compilation of important epidemiologic and common biostatistical terms used in medical research. You can use it as a reference guide when reading

### Incorrect Analyses of Radiation and Mesothelioma in the U.S. Transuranium and Uranium Registries Joey Zhou, Ph.D.

Incorrect Analyses of Radiation and Mesothelioma in the U.S. Transuranium and Uranium Registries Joey Zhou, Ph.D. At the Annual Meeting of the Health Physics Society July 15, 2014 in Baltimore A recently

### Article in Review. Citations to date: 67!

Journal Club Week 1 Doctor, I have migraine with aura, am I more likely to die of cardiovascular disease?!!!!!!!!!!!!!! Samantha Warhurst! Med3000 Student! ! Article in Review Gudmundsson, L. S., Scher,

### HeartScore Web - based version users guide TABLE OF CONTENTS. 1. Preamble... 2. 2. Benefits of using HeartScore... 2. 3. Accessing HeartScore...

TABLE OF CONTENTS 1. Preamble... 2 2. Benefits of using HeartScore... 2 3. Accessing HeartScore... 2 4. HeartScore Web Based Homepage... 3 5. Patient Card... 4 6. Create a new examination... 6 7. Examination

### Measures of disease frequency

Measures of disease frequency Madhukar Pai, MD, PhD McGill University, Montreal Email: madhukar.pai@mcgill.ca 1 Overview Big picture Measures of Disease frequency Measures of Association (i.e. Effect)

### Chronic Diseases. Between 2005 and 2007 there were no significant changes in any of the adult behaviors reported through the BRFSS.

Chronic Diseases Chronic diseases and conditions, such as heart disease, cancer, diabetes and obesity, are the leading causes of death and disability in the United States. Chronic diseases account for

### Evidence-based Public Health

CHAPTER 2 Evidence-based Public Health LEARNING OBJECTIVES By the end of this chapter the student will be able to: explain the steps in the evidence-based public health process. describe a public health

### Coronary Heart Disease

Coronary Heart Disease Disease Information Packets Slide Sets Public Health Services, Community Health Statistics 8/2010 What is Coronary Heart Disease? Coronary heart disease (CHD) is the most common

### Outline of Topics. Statistical Methods I. Types of Data. Descriptive Statistics

Statistical Methods I Tamekia L. Jones, Ph.D. (tjones@cog.ufl.edu) Research Assistant Professor Children s Oncology Group Statistics & Data Center Department of Biostatistics Colleges of Medicine and Public

### Predictors of Adherence to Inhaled Medications among Veterans with COPD. John Huetsch

Predictors of Adherence to Inhaled Medications among Veterans with COPD John Huetsch Background Medication nonadherence is a problem common to the treatment of many chronic diseases Potential obstacles

### Application of Odds Ratio and Logistic Models in Epidemiology and Health Research

Application of Odds Ratio and Logistic Models in Epidemiology and Health Research By: Min Qi Wang, PhD; James M. Eddy, DEd; Eugene C. Fitzhugh, PhD Wang, M.Q., Eddy, J.M., & Fitzhugh, E.C.* (1995). Application

### Study designs in medical research

Study designs in medical research 1 Study design is the procedure under which a study is carried out 2 Two main categories Observation: Identify subjects, then Observe and record characteristics Experiment

### Two Correlated Proportions (McNemar Test)

Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with

### RR833. The joint effect of asbestos exposure and smoking on the risk of lung cancer mortality for asbestos workers (1971-2005)

Health and Safety Executive The joint effect of asbestos exposure and smoking on the risk of lung cancer mortality for asbestos workers (1971-2005) Prepared by the Health and Safety Laboratory for the

### Coronary Heart Disease (CHD) Brief

Coronary Heart Disease (CHD) Brief What is Coronary Heart Disease? Coronary Heart Disease (CHD), also called coronary artery disease 1, is the most common heart condition in the United States. It occurs

### Spirometry, COPD and lung cancer

Spirometry, COPD and lung cancer Associate Professor Robert Young BMedSc, MBChB, DPhil (Oxon), FRACP, FRCP University of Auckland, New Zealand Spirometry for those with smoking and dust exposures Risk

### WorkSmart WorkSmart myhealth WorkSmart A1C LDL TrigLyCeriDes CoTinine ToTAL hdl ToTAL ChoLesTeroL ChoLesTeroL-hDL ratio

is designed for the workplace looking for a single, comprehensive set of tests that establish baseline laboratory values for wellness and disease management programs. The Test Package describes the normal

### South African Cholesterol Guidelines Compared

South African Cholesterol Guidelines Compared Jacqueline van Schoor, Amayeza Info Centre While infectious diseases are currently the leading cause of death in South Africa, cardiovascular disease (CVD)

### High Cholesterol (Hyperlipidemia)

High Cholesterol (Hyperlipidemia) My cholesterol medication is: The directions are: In recent years, powerful new drugs have been developed to reduce cholesterol in your blood, thereby reducing your risk

### Health Passport. Your Journey to Wellness. Health Fair ID#

Health Passport Your Journey to Wellness Health Fair ID# Body Mass Index What is BMI? Your body mass index, or BMI, shows the amount of fat in your body. BMI is calculated using height, weight, and waist

### Effect measure modification & Interaction. Madhukar Pai, MD, PhD McGill University madhukar.pai@mcgill.ca

Effect measure modification & Interaction Madhukar Pai, MD, PhD McGill University madhukar.pai@mcgill.ca 1 Interaction + Effect Modification = Frustration Introduction to effect modification leaves some

### Attributable Risk Applications in Epidemiology

The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board. Attributable Risk Applications in Epidemiology Mark A. Kaelin Department

### Today s lecture. Lecture 6: Dichotomous Variables & Chi-square tests. Proportions and 2 2 tables. How do we compare these proportions

Today s lecture Lecture 6: Dichotomous Variables & Chi-square tests Sandy Eckel seckel@jhsph.edu Dichotomous Variables Comparing two proportions using 2 2 tables Study Designs Relative risks, odds ratios

### Now we ve weighed up your application for our protection products, it s only fair we talk you through our assessment process. More than anything, we

how we assess your application UNDERWRITING EXPLAINED. Now we ve weighed up your application for our protection products, it s only fair we talk you through our assessment process. More than anything,

### MANAGEMENT OF LIPID DISORDERS: IMPLICATIONS OF THE NEW GUIDELINES

MANAGEMENT OF LIPID DISORDERS: IMPLICATIONS OF THE NEW GUIDELINES Robert B. Baron MD MS Professor and Associate Dean UCSF School of Medicine Declaration of full disclosure: No conflict of interest EXPLAINING

### Radon in Homes and Lung Cancer Risk. Sarah C Darby CTSU, University of Oxford

Radon in Homes and Lung Cancer Risk Sarah C Darby CTSU, University of Oxford Plan of talk 1. Introduction to radon and lung cancer 2. Estimating the risk in homes 3. Reducing radon-related deaths Orion

### New Cholesterol Guidelines: Carte Blanche for Statin Overuse Rita F. Redberg, MD, MSc Professor of Medicine

New Cholesterol Guidelines: Carte Blanche for Statin Overuse Rita F. Redberg, MD, MSc Professor of Medicine Disclosures & Relevant Relationships I have nothing to disclose No financial conflicts Editor,

### Best Class Criteria LIFE UNDERWRITING

LIFE UNDERWRITING Best Class Criteria The following criteria contain key information that can help you estimate whether your life insurance clients will qualify for one of our best risk classes Super Preferred,

### Nutrition and Health Info-Sheet F o r H e a l t h P r o f e s s i o n a l s

Nutrition and Health Info-Sheet F o r H e a l t h P r o f e s s i o n a l s Produced by Erin Digitale, PhD Cristy Hathaway, BS Sheri Zidenberg-Cherr, PhD Karrie Heneman, PhD UC Cooperative Extension Center

### Quitting Smoking. Nancy Mesiha, MD, FACC, MACM PD, CV Fellowship Program St John Hospital & Medical center Cardiology Associates of Michigan

Quitting Smoking Nancy Mesiha, MD, FACC, MACM PD, CV Fellowship Program St John Hospital & Medical center Cardiology Associates of Michigan Risk Factors for PAD Reduced Increased Smoking Diabetes Hypertension