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  1. How to Write Introduction Section? Dr. Gholamreza Khalili Department of Epidemiology & Biostatistics School of public health Tehran University of Medical Sciences

  2. Introduction • Before you begin, answer the basic questions: • What do I have to say? • Is it worth saying? • What is the right format? • What is the audience? • What is the right journal?

  3. Introduction General, concise description of problem background to the work previous research Where that work is deficient how your research will be better State the hypothesis About 3 to 4 paragraphs

  4. Paragraph1: What we know Paragraph2: What we don’t know Paragraph3: Why we did this study

  5. Introduction Existing state of knowledge Gaps in knowledge which research will fill. State what you Intend to do & the purpose of article Give pertinent references Summarize the rationale for study or observation Define specialized terms or abbreviations you want to use

  6. Inverted pyramid The structure should funnel down from a broad perspective to a specific aim Oxidative stress plays an important role in.... When LDL particles are oxidized ... Antioxidants are important... ...Paraoxonase...

  7. Don’t make it a review article Don not include methods, results and discussion Don’t put down every all previous studies & their data gaps Don’t explain pathophysiology irrelevant to your study Introduction

  8. Introduction • Tell why you have undertaken the study • Clarify what your work adds • Follow the best advice • Keep it short • Make sure you are aware of earlier studies • Tell about importance of your study • Don’t baffle your readers • Give the study design • Think about using journalistic tricks

  9. Introduction • To write an effective introduction you must: • Know your audience • Keep it short • Tell readers why you have done the study • Explain why it is important • Convince readers that it is better than others • Try to hook them!

  10. Types of study designs Dr. Gholamreza Khalili Department of Epidemiology & Biostatistics School of public health Tehran University of Medical Sciences

  11. Types of Studies • Descriptive Studies • Observational Analytic Studies • Cross Sectional studies • Ecologic studies • Case Control studies • Cohort studies • Experimental Studies • Randomized controlled trials

  12. Hierarchy of Study Types Analytic • Descriptive • Case report • Case series • Survey • Observational • Cross sectional • Ecologic • Case-control • Cohort studies • Experimental • Randomized controlled trials • Field Trials • Community Trials Strength of evidence for causality between a risk factor and outcome

  13. Descriptive studies • Getting a “lay of the land” • Surveys (NHIS, MCBS) • Describing a novel phenomena • Case reports or case series

  14. Descriptive studies • Cannot establish causal relationships • Still play an important role in describing trends and generating hypotheses about novel associations • The start of HIV/AIDS research • Squamous cell carcinoma in sexual partner of Kaposi sarcoma patient. Lancet. 1982 Jan 30;1(8266):286. • New outbreak of oral tumors, malignancies and infectious diseases strikes young male homosexuals. CDA J. 1982 Mar;10(3):39-42. • AIDS in the "gay" areas of San Francisco. Lancet. 1983 Apr 23;1(8330):923-4.

  15. Analytic Studies • Attempt to establish a causal link between a predictor/risk factor and an outcome. • You are doing an analytic study if you have any of the following words in your research question: • greater than, less than, causes, leads to, compared with, more likely than, associated with, related to, similar to, correlated with

  16. Hierarchy of Study Types Analytic • Descriptive • Case report • Case series • Survey • Observational • Cross sectional • Ecologic • Case-control • Cohort studies • Experimental • Randomized controlled trials • Field Trials • Community Trials Strength of evidence for causality between a risk factor and outcome

  17. Research Question Is the regular consumption of Red Bull associated with improved academic performance among U.S. medical students?

  18. Background • Alford C, Cox H, Wescott R. The effects of red bull energy drink on human performance and mood. Amino Acids. 2001;21(2):139-50. • Warburton DM, Bersellini E, Sweeney E. An evaluation of a caffeinated taurine drink on mood, memory and information processing in healthy volunteers without caffeine abstinence. Psychopharmacology (Berl). 2001 Nov;158(3):322-8. • Seidl R, Peyrl A, Nicham R, Hauser E. A taurine and caffeine-containing drink stimulates cognitive performance and well-being. Amino Acids. 2000;19(3-4):635-42. • Horne JA, Reyner LA. Beneficial effects of an "energy drink" given to sleepy drivers. Amino Acids. 2001;20(1):83-9. • Kennedy DO, Scholey AB. A glucose-caffeine 'energy drink' ameliorates subjective and performancedeficits during prolonged cognitive demand. Appetite. 2004 Jun;42(3):331-3.

  19. Great idea, but how do you get started…. • Interesting, novel, and relevant, but… • You only have 25,000 dollars to start investigating this question. • What is feasible?

  20. Study Design #1 • Cross-sectional study of UCSF medical students taking USMLE Step 2 • Questionnaire administered when registering for USMLE 2 • Primary predictor: self-report of >3 cans Red Bull per week for the previous year • Covariates: Age, sex, undergraduate university, place of birth • Outcome: Score on USMLE Step 2

  21. Cross-sectional study: structure Red Bull consumption USMLE Score time

  22. Cross-sectional Study: • Descriptive value: • How many UCSF medical students drink Red Bull? • What is the age and sex distribution of UCSF medical students who drink Red Bull? • Analytic value: • Is there an association between regular Red Bull consumption and test scores among UCSF med students?

  23. Cross-sectional Study: Pluses + Prevalence (not incidence) + Fast/Inexpensive - no waiting! + No loss to follow up + Associations can be studied

  24. Measures of association Risk ratio (relative risk) A A +B C C +D

  25. Cross-sectional study: minuses - Cannot determine causality Red Bull consumption USMLE Score time

  26. Cross-sectional study: minuses - Cannot determine causality • ACE inhibitor use and hospitalization rates among those with heart failure time

  27. Cross-sectional study: minuses - Cannot determine causality - Cannot study rare outcomes

  28. What if you are interested in the rare outcome? • The association between regular Red Bull consumption and… • A perfect score on the USMLE – Step 2 • Graduating top 1% of the medical school class • Acceptance into a highly selective residency ANSWER: A Case-Control study

  29. Study Design #2 • A case-control study • Cases: med students accepted to residency in “highly selective specialty X”. • Controls: med students who applied but were not accepted. • Predictor: self-reported regular Red Bull consumption • Additional covariates (age, sex, medical school, undergraduate institution)

  30. Case control studies • Investigator works “backward” (from outcome to predictor) • Sample chosen on the basis of outcome (cases), plus comparison group (controls)

  31. C C C C B B B B Not Exposed to A Exposed to A Not Exposed to A Exposed to A No Disease Disease Study multiple exposures in a Case-control Study

  32. Case-control study structure present ACTUAL CASES students who matched in “highly selective specialty X” Red Bull consumption YES Red Bull consumption NO ACTUAL CONTROLS students who failed to match in “highly selective specialty X” time

  33. Case control studies • Determines the strength of the association between each predictor variable and the presence or absence of disease • Cannot yield estimates of incidence or prevalence of disease in the population (why?) • Odds Ratio is statistics

  34. Case-control Study: pluses + Rare outcome/Long latent period + Inexpensive and efficient: may be only feasible option + Establishes association (Odds ratio) + Useful for generating hypotheses (multiple risk factors can be explored)

  35. Case-control study-minuses • Causality still difficult to establish • Selection bias (appropriate controls) • Recall bias: sampling (retrospective) • Cannot tell about incidence or prevalence

  36. Where are we? • Preliminary results from our cross-sectional and case-control study suggest an association between Red Bull consumption and improved academic performance among medical students • What’s missing? - strengthening evidence for a causal link between Red Bull consumption and academic performance • Use results from our previous studies to apply for funding for a prospective cohort study!

  37. Study design #3 • Prospective cohort study of UCSF medical students Class of 2009 • All entering medical students surveyed regarding consumption and variety of other potential covariates • Survey updated annually to record changes in Red Bull consumption • Outcomes: USMLE Step 1 score, USMLE Step 2 score, match in first choice residency

  38. Cohort studies • A cohort (follow-up, longitudinal) study is a comparative, observational study in which subjects are grouped by their exposure status, i.e., whether or not the subject was exposed to a suspected risk factor • The subjects, exposed and unexposed to the risk factor, are followed forward in time to determine if one or more new outcomes (diseases) occur • Subjects should not have outcome variable on entry • The rates of disease incidence among the exposed and unexposed groups are determined and compared.

  39. Not Exposed Exposed Do not Develop Disease A Develop Disease A Do not Develop Disease A Develop Disease A B B B B C C C C Study multiple outcomes in a cohort Study

  40. Elements of a cohort study • Selection of sample from population • Measures predictor variables in sample • Follow population for period of time • Measure outcome variable • Famous cohort studies • Framingham • Nurses’ Health Study • Physicians’ Health Study • Olmsted County, Minnesota

  41. Prospective cohort study structure The present The future Top USMLE scorers Everyone else time

  42. Strengths of cohort studies • Know that predictor variable was present before outcome variable occurred (some evidence of causality) • Directly measure incidence of a disease outcome • Can study multiple outcomes of a single exposure (RR is measure of association)

  43. Weaknesses of cohort studies • Expensive and inefficient for studying rare outcomes • Often need long follow-up period or a very large population • Loss to follow-up can affect validity of findings

  44. Other types of cohort studies • Retrospective cohort • Identification of cohort, measurement of predictor variables, follow-up and measurement of outcomes have all occurred in the past • Much less costly than prospective cohorts • Investigator has minimal control over study design

  45. Other types of cohort studies • Nested case-control study • Case-control study embedded in a cohort study • Controls are drawn randomly from study sample • Case cohort Study

  46. Hierarchy of Study Types Analytic • Descriptive • Case report • Case series • Survey • Observational • Cross sectional • Ecologic • Case-control • Cohort studies • Experimental • Randomized controlled trials • Field Trials • Community Trials Strength of evidence for causality between a risk factor and outcome

  47. What distinguishes observational studies from experiments? • Ability to control for confounding Confounder Predictor Outcome • Examples: • sex (men are more likely to drink red bull and men are more likely to match in neurosurgery) • Undergraduate institution (students from northwest school are more likely to drink red bull and also more likely to score higher on • USMLE)

  48. But we measured all of the potential confounders……. • In a prospective cohort study you can (maybe) measure all potential known confounders, but… • You can’t control for unmeasured confounders

  49. Study design # 4 • Randomized controlled trial of daily Red Bull consumption among entering UCSF medical students Class 2009 • Randomized to daily consumption of Red Bull vs. daily consumption of placebo • Outcomes: USMLE Step 1 score, USMLE Step 2 score, match in first choice residency

  50. Randomized controlled trials • Investigator controls the predictor variable (intervention or treatment) • Major advantage over observational studies is ability to demonstrate causality • Randomization controls unmeasured confounding • Only for mature research questions