Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Introduction to clinical research

1,013 views

Published on

Anatomy and Physiology of Research

Published in: Health & Medicine, Business
  • Be the first to comment

Introduction to clinical research

  1. 1. Introduction to Clinical Research & Concept of Scientific Inquiry Tamer Hifnawy MD. Dr.PH Associate Professor Public Health & Community Medicine Faculty of Medicine – BSU- Egypt College of Dentistry Taibah University- KSA Vice Dean For Quality, Development & International Affairs Certified Trainer for International Research Ethics
  2. 2. What is research? • Research is a systematic investigation designed to discover or contribute to a body of generalisable knowledge.
  3. 3. It is important at this point to draw the line between “research” and “practice”.
  4. 4. Anatomy of Research What it’s made of?
  5. 5. Anatomy of Research: What it’s made of? • The structure of a research project is set out in its Protocol “ The written plan of the study” • Components of the Protocol: 1. Research Question 2. Background and significance 3. Design 4. Study Subjects 5. Variables 6. Statistical issues
  6. 6. Outline of the Study Protocol Element Purpose Research Question What question will the study address? Background and significance Why are these question important? Design: •Time frame •Epidemiological approach How the study is structured? Subjects •Selection Criteria •Sampling Design Who are the subjects and how will they be selected? Variables: •Predictors variable •Confounding Variables •Outcome Variable What measurement will be made Statistical issues: •Hypothesis •Sample size •Analytical approach How large is the study and how will it be analyzed?
  7. 7. Research question • The research question is the objective of the study. • The uncertainty of the investigator wants to resolve. • Often begin with a general concern that must be narrowed to a concrete , reasonable issue.
  8. 8. Research question Should people eat more fish? • This is a good place to start, but the question must be focused before planning efforts can begin. • Often this involves breaking the question into more specific components: • How often do Saudis eat fish? • Does eating fish lower the risk of cardiovascular diseases • Do fish oil supplements have the same effect on CVD as dietary fish? • Which Fish oil supplements don’t make people smell like fish?
  9. 9. Research question • A good research question should pass the “So What?” test. • Getting the answer should contribute usefully to our state of knowledge. • The Acronym FINER denotes five essential characteristics of a good research question:
  10. 10. Research question FINER • Feasible • Interesting • Novel • Ethical • Relevant
  11. 11. Background and Significance • Set the proposed study in context and gives its rationale • What is know about the topic in hand? • Why is the research question important? • What kind of answers will the study provide? • This section cites the previous researches that are relevant ( including investigator own work)
  12. 12. Design • The design of the study is a complex issue • Could be Interventional or Non- Interventional study (Observational Study). • No one approach is always better than the others, and each research question requires a judgment about which design is the most efficient way to get a satisfactory answer.
  13. 13. Design • The Randomized Control trials (RCTs)is often held up as the best design for establishing causality and the effectiveness of the intervention. • There are many situations for which an observational study is a better choice or the only feasible option. • The relatively low cost of case control studies and their suitability for rare outcomes make them attractive for some questions.
  14. 14. Design • A typical sequence for studying a topic begins with observational studies of a type that is often called descriptive. • These studies explore the lay of the land: – Describing distribution of a disease or a health related problem in a population – Usually followed by analytic studies that evaluate the associations to permit inferences about cause and effect relationship.
  15. 15. Study Subjects • Inclusion criteria • Exclusion Criteria • How to recruit enough people • Studying a random sample?
  16. 16. Variables • In an analytical study the investigator studies the associations among variables to predict outcomes (Predictor variable) and (Outcome variable). • Clinical trials examine the effect of an intervention ( a special kind of predictor variable that the investigator manipulate), this design allow to observe the effect on the outcome variable using randomization to control for the influence of confounding variables (other predictors of outcome)
  17. 17. What do we study in a medical research? • The outcome (Coronary Heart Disease). • The primary exposure (risk factor) (Blood Cholesterol). • Other exposures that may influence the outcome (Age, Sex).
  18. 18. Exposures and outcomes The definition of both the “exposure” and “outcome” depends on the question you are asking The primary exposure (s) is the one(s) included in your hypothesis
  19. 19. Question 1: Does smoking increase the risk of lung cancer? Smoking Lung Cancer exposure outcome
  20. 20. Question 2: Is consumption of aflatoxin associated with increased risk of liver cancer? Aflatoxin Liver Cancer exposure outcome
  21. 21. Exposures and outcomes Because you do not know which exposures are likely to be risk factors for the disease. i.e. you do not know which exposures are “primary” Because some exposures may ‘get in the way’ when trying to sort out a relationship between primary exposure and outcomes. i.e. they may act as confounding factors You will need to measure more than one exposure
  22. 22. Exposure Cholesterol level Outcome Coronary H.D. Confounder Age, Sex Is exposure to High Cholesterol a risk factor for CHD?
  23. 23. Is caffeine consumption during pregnancy associated with increased risk of low birth weight? Caffeine during pregnancy Low birth weight exposure outcome Smoking during pregnancy potential confounding factor
  24. 24. Exposures and outcomes An outcome in one study could be the exposure in another ! Low birth weight Hypertension exposure outcome
  25. 25. Quiz I am interested in finding out about the relationship between cancer of the colon and diet. Question: Which is the outcome ? Answer: Depends on the research question. • If question is: how does diet affect risk of developing cancer colon ? Outcome = cancer colon • If question is: how does having cancer colon affect diet? Outcome = diet
  26. 26. Statistical Issues • Sample size • Specifying a hypothesis: 50 to 60 years old women with CHD who take fish oil supplements will have a lower risk of myocardial infarction than those who do not. • Reasonable probability (P- value) • Power
  27. 27. Statistical Issues • Purely descriptive studies (What proportion of people with CHD use fish oil supplements?) • No statistical significance is required and this do not require a hypothesis, instead, the number of subject needed to produce a narrow confidence intervals (CI)for means, proportions or other descriptive statistics can be calculated.
  28. 28. Physiology of Research How it works?
  29. 29. Physiology of Research How it works? • The goal of clinical research is to draw inferences from findings in the study about the nature of the universe around. • Two major sets of inferences are involved in the interpreting a study. • Internal validity • External validity
  30. 30. Validity of a research • Internal validity: – The degree to which the investigator draws the correct conclusion about what actually happened in the study. • External validity (Generalizability): – The degree to which these conclusions can be appropriately applied to people and events outside the study.
  31. 31. Designing the study • Consider the simple descriptive question: What is the prevalence of regular use of fish oil supplements among people with CHD? • This question can not be answered with perfect accuracy because it would be impossible to study all patients with CHD; SO the investigator settles for a related question that can be answered by the study: Among a sample of CHD patients seen in the investigators' clinic and respond to a questionnaire what proportion report taking fish oil supplements?
  32. 32. Implementing the study • Sometimes we may get a wrong answer to a study question because the way the sample was actually drawn and the measurement made, differed in important ways from the way they were designed. • The difference between the study plan and the actual study can alter the answer to the research question
  33. 33. Causal inference • A special kind of validity problem arises in the studies that examine the association between a predictor and outcome variable in order to draw a causal inference IS smoking cause CHD?
  34. 34. The Errors of Research • No Study is free of errors, and the goal is to maximize the validity. • Two main type of errors that interfere with the research inferences: – Random errors – Systematic errors.
  35. 35. Random Errors • A wrong result due to chance. • Depends on the sample size. • Can be minimized by increasing the sample size.
  36. 36. Systematic Error • Is a wrong result due to bias • Bias: Source of variation that distort the study findings in one direction. • An example of a systematic measurement error is the underestimation of the prevalence of fish oil use due to lack of clarity in how the question is phrased.
  37. 37. Summary • The Anatomy of research is a set of tangible elements that make up the study plan: research question, its significance, study design, study subjects and measurement approach. • The Physiology of research is how the study works: internal and external validity, random (chance) and systematic error (Bias)
  38. 38. Conceiving The Research Question Introduction to Clinical Research & Concept of Scientific Inquiry
  39. 39. Conceiving the Research question • The research question is the uncertainty about something in the population that the investigator wants to resolve by making measurements on the study subjects.
  40. 40. Origin of Research Question • Mastering the literature • Being alert to new ideas and techniques – Attend conferences • Keeping the imagination Roaming – Careful observation of patients – Teaching (during presentation and lecture preparation) • Choosing a Mentor – Experience
  41. 41. Characteristic of a Good Research Question • FINER Criteria for a Good Research Question. –Feasible –Interesting –Novel –Ethical –Relevant
  42. 42. Feasible • Adequate number of subjects • Adequate technical expertise • Affordable in time and money • Manageable in scope. (You have to narrow the scope and focus only on the most important goals)
  43. 43. Interesting • It is wise to confirm that you are not the only one who finds a questions interesting. • Speak with mentors and outside experts before devoting substantial energy to develop plan or grant proposal that peers and funding agencies may consider dull.
  44. 44. Novel • Contribute to new information • Reviewing literatures Ethical • Research Ethics Committee (REC)/ Institutional Review Board (IRB) • Following the international codes of Ethics. • Autonomy/ Justice/ beneficence
  45. 45. Relevant • Among the Characteristics of a good research question, none is more important than its relevance. • A good way to decide about relevance is to imagine the various outcome that are likely to occur. • Discuss with experts in the field.
  46. 46. HYPOTHESIS TESTING
  47. 47. HYPOTHESIS • In statistics, is a claim or statement about some population parameter (basically, a good guess). • The hypothesis may or may not be true (there’s only one way to find out)…. • That’s what we are trying to figure out.
  48. 48. Research Hypothesis o The research process begins with a hypothesis about the relationship between two occurrences. E.g.,  People who smoke are more likely to get lung cancer than people who do not  Post-menopausal women treated with Hormone replacement therapy (HRT) are less likely to have MI than women who are not.
  49. 49. Research Hypothesis • An assumed answer to the study question. • The study has either to: »Prove the hypothesis »Disprove the hypothesis • Research hypotheses depend on the current state of knowledge and technology in a specific field of study.
  50. 50. Research Hypothesis o After formulation of the research hypothesis in scientific methodology, the research hypothesis is not tested directly. o Instead we start with an assumption that there is no difference or association between the variables compared. This is called the null hypothesis (H0 ). o The null hypothesis is thus the contrary to the research hypothesis (also termed alternative hypothesis H1 ).
  51. 51. The NULL Hypothesis • Null = Zero (in German language) • No difference between compared groups (in statistical science)
  52. 52. The NULL Hypothesis • Assumes that the difference you observe is due to chance (H0). • When the significance test result shows that (P≤ 0.05) this means: 1. The difference is significant 2. Reject the null hypothesis 3. Accept the alternative hypothesis (H1) 4. The observed difference is not due to chance 5. Find out what the difference signifies
  53. 53. Research Hypothesis o In scientific methodology, even if a difference or an association is found, it should be assumed that it is due to chance, until it is proven, by statistical analysis, that it is unlikely to be explained by chance. o The research hypothesis is accepted by exclusion if the statistical test rejects the null hypothesis.
  54. 54. Difference Observed Difference Height Signifies;  Age difference  Sex difference  Hormonal effect  Nutritional effect  Genetic effect Is it normal biological variation (CHANCE)?
  55. 55. HYPOTHESIS TESTING AND PROBABILITY • Most statistical analyses in medicine involves hypothesis testing, i.e. the probability, (P) that: –The null hypothesis (H0) is A=B –The alternative hypothesis (H1) is A B
  56. 56. HYPOTHESIS TESTING AND PROBABILITY • The P value: –“the probability of obtaining the value of the observed statistic (e.g. a difference between the means of two groups, or a difference between two proportions) or ones of more extreme value, if the null hypothesis is true”. –i.e. the probability that a difference or an association as large as the one observed could have occurred by chance alone.
  57. 57. P – VALUE • So, we examine the probability of the observed difference (and all the ones that are more extreme). • If this probability P ≤ 0.05 (the Type I probability) we conclude that the null hypothesis is false and it should be rejected. • So, a P value ≤ 0.05 leads to rejection of the null hypothesis, while a P value > 0.05 indicates that probability of type I error is high and we can not reject the null hypothesis.
  58. 58. Poor Man 500,000 $ 1000 $ 1,000,000 $
  59. 59. Null hypothesis and alternative hypothesis There is no difference between the means of two compared groups (this shown difference would be expected to occur by chance) Null hypothesis Alternative hypothesis There is a difference between the means of the two groups
  60. 60. Alpha error (Type I error): • P value (traditionally levels of 0.05 are used for statistical significance) • It indicates the probability of rejecting the statistical hypothesis tested when in fact, that hypothesis is true. ART
  61. 61. Beta error (Type II error): • the probability that the test will accept the hypothesis tested when in fact, it is false. It measures the power of the test. =(1-B error) • Power of the test: probability of rejecting the null hypothesis when it is false. BAF
  62. 62. EXAMPLE It may be concluded on the basis of the results that a new treatment is better when in fact it is not better than the standard treatment Type I error Randomized control trial of drugs
  63. 63. On the other hand, a new treatment, that is actually effective may be concluded to be ineffective Type II error
  64. 64. Type I error (P-value) Level of significance of the test
  65. 65. P < 0.05 ?? P<0.001 P>0.05 P<0.01P<0.05 P<0.0001
  66. 66. Type II error Power of the test
  67. 67. Remember: • Be sure of the distribution of your data before doing any statistical analysis to choose the right statistical test. P -value Statistical significance

×