Quantitative research methods in medicine dr. baxi


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Quantitative research methods in medicine dr. baxi

  1. 1. 8/4/2011 RKB
  2. 2. Why research: An academic necessity? Intention to answer a few questions to improve Patient care n management? Personal glory? Competitive edge that it may provide? Skeptic disagreement with so-called “established practices”? Common curiosity and natural tendency to challenge and be challenged? Ultimately, it should add to existing body of knowledge, explain the unexplained and add to Patient care and management 8/4/2011 RKB
  3. 3. In other words, We are attempting to unravel “truth” in the universe with the help of Truth in the study which is based on “findings in the study” which in turn depends on “plan” and “execution” of the study…… Hence, preparing properly and adequately at planning stage and executing right things in the right manner may eventually lead to truth in the study and may permit further extension to Truth in the universe! So, now you know that Designing ,implementing, and inferring –all these stages are prone to “Errors” and the art and science of minimizing the same and increasing external and internal validity is Quantitative Methods in research!! 8/4/2011 RKB
  4. 4. Ideal study should be: <ul><li>Accurate: 1) validity (internal and external) </li></ul><ul><li>2)precision </li></ul><ul><li>Free from bias and confounders </li></ul><ul><li>No sampling error (Type- I or alpha, Type-II or beta) </li></ul><ul><li>Confidence intervals (CI) </li></ul><ul><li>“ p” value significance. </li></ul>8/4/2011 RKB
  5. 5. Anatomy of Research   1. Define the problem   2. Specify the objectives   3.Select design or type of study   4.Select study population   5. Collect data   6. Analyze data   7. Determine conclusions 8/4/2011 RKB
  6. 6. Sequence and Cycles of Research 8/4/2011 RKB
  7. 7. Getting ready…. Relax, investigator needs to get acquainted only It may be appreciated that it is a good idea to understand basic, underlying principles What is required to be done and how to draw inference is to be learnt Most computer software efficiently do the sample selection and statistical testing, but fortunately, they do not think for you as of now…. One does not have to know how to manufacture an automobile-just knowing how to drive a car is good enough. Pl. do not underestimate those who go into the details of this science—some of the much smarter and younger brains have invested years which I am sure I can not justifiably pass on over a few minutes!!! Pl. do not interpret or infer what is not there or not tested 8/4/2011 RKB
  8. 8. Hypothesis Testing the same Reject Null Hypothesis Fail to reject Null Hypothesis Much similar to Guilt, Guilty, Acquital, conviction!!!! Truth in the population Result in Sample Assoc. YES Assoc. NO Reject Null H Correct Type I error Fail to reject NullH Type II error correct Where, the null hypothesis states that there is No association between Predictors and outcome 8/4/2011 RKB
  9. 9. Type I error thus give false positive study result. This can arise out of methodological faults or “pure chance” or both. Hence it can not be eliminated completely, but can be brought down to a “measurable” level. This only chance measure ,your PSM friends call α Alpha.! This is the level of statistical significance-the level of reasonable doubt one is willing to accept based on study results i.e. you agree to err to the tune of 5%. Type II error gives false negative study result. Actuality not picked up by the study .Like alpha, it is not entirely avoidable, hence try to bring it to measurable minimum. This is called β .(beta). (1- beta ) is the power of the study. In simple terms, if beta is set at 0.20,it means, researcher is willing to accept a 20% chance of missing what is true in the population. Conventional alpha is 0.05 and beta 0.20. Ideal α and β should be 00- only conceptual and not concrete!!!!! 8/4/2011 RKB
  10. 10. TYPE OF STUDIES   Observational   1. Correlation study   2. Case reports and case series   3. Cross sectional survey   4. Case-control study   5. Cohort study   Experimental or interventional   1. Community trials   2. Clinical trials – individuals 3. RCT  8/4/2011 RKB
  11. 11. For the study to be valid We need Precision and Accuracy Precision is the degree to which a variable is reproducible, with nearly the same value each time it is measured. At times precision is also called reliability and consistency Precision is a function of Random error i.e. “chance error” Generally due to Observer variability, Instrument variability or Subject variability Standardizing, automation, training and repetition will help reduce these errors Not entirely avoidable hence, learn to be vigorous, define in advance how much vigorous you intend to be and learn to measure the errors made! 8/4/2011 RKB
  12. 12. Accuracy: It is the degree to which a variable represents what it is intended to represent It compares to a reference standard It increases the validity & It is prone to Systematic errors(cf. chance errors and precision),hence accuracy is a function of systematic error or “Bias” Like with precision, Here also, it could be observer bias, instrument bias and subject bias. Comparing with “Gold standard’ will assess accuracy….which generally is known as specificity and sensitivity….. 8/4/2011 RKB
  13. 13. STUDY DESIGNS IN APPLIED MEDICAL RESEARCH 8/4/2011 RKB Approach Type of study Examples Observational 1. Descriptive - Institutional surveys - Community surveys 2. Analytic -cross-sectional - Case-Control studies - Cohort studies Experimental Analytic - Lab experiments - Animal experiments - Clinical trials
  14. 14. Cross – Sectional Studies <ul><li>Definition : In a cross-sectional study the information is collected from each subject at one point in time. This is in contrast to a cohort study which collects information on new events over a period of time. The main outcome measure obtained from a cross-sectional study is prevalence. </li></ul>8/4/2011 RKB
  15. 15. Cross sectional study would permit simple analysis by categorizing data according to exposure status and outcome status. Thus, it will give Point prevalence Crude prevalence rate Prevalence among exposed and among NOT exposed & there by difference of the 2 above and the ratio of the 2 above. A simple chi square test will get us the strength of association Further, CI for Prev. rate ratio also can be calculated. Most software do it for you ! 8/4/2011 RKB
  16. 16. Cross sectional studies are onetime observations-a snap shot as they call it! Describes variables and its distribution Gives point prevalence At times, in a relationship/association one is not too sure of what is a predictor and what is an outcome! Risk factor; Disease Risk factor; No Disease No Risk factor; No Disease No Risk factor; Disease Sample Population 8/4/2011 RKB
  17. 17. CASE - CONTROL STUDY Yes No Yes No Select cases Select suitable controls Exposure to risk factor 8/4/2011 RKB
  18. 18. Without controls there can not be a case-control study, but with the wrong controls there can only be regrettable case –control studies. Controls should be comparable to case s except for the disease under study & frequency of the exposure under study..However potential for exposure should be same… Controls should come from same source population and should follow same selection criteria as cases Hospital controls generally not representative of the source population Though convenient, useful, better comparability, better recall they may be “inherently” different and may weaken internal validity. 8/4/2011 RKB
  19. 19. While selecting Controls ,pl. ask: Do controls come from the same source population as cases? Are they similar to cases as regards potential for past exposures? Are potentialities of confounders similar? Have similar exclusion criteria are applied to both cases and controls? Have they come from the same time period? 8/4/2011 RKB
  20. 20. Analysis of Case –Control study gives OR or Odds Ratio. As one would understand ,it can not give incidence, cumulative incidence or relative risk as we have not taken cases emerging prospectively , BUT, we have cases selected from a population where the rate of occurrence would be different from the proportion which is “selected” for the study. If incident cases are used, if selection of cases is Unbiased and (if) the outcome is Rare… OR can approximate RR. OR, chisquare and CI of OR can be calculated to refine the analysis. As it is the most common design employed, and because it is vulnerable to Bias and confounders, we shall touch upon both Bias and confounders a little later.. 8/4/2011 RKB
  21. 21. COHORT STUDY Screen population Disease absent Disease present Sample Risk factor present Risk factor absent Develop disease Do not develop Develop disease Do not develop Time Time / / / / 8/4/2011 RKB
  22. 22. Cohort Study: What matters is the sequencing of Exposure and outcome. Measured at one time, simultaneously is Cross-sectional, Outcome decided before exposure is case-control while in Cohort, exposure is necessarily determined before outcome. Even in a retrospective or historical or “Cohort –on –paper” ,in the time line, though outcome also has already occurred, examining exposure precedes the outcome. Hence, they are Longitudinal in nature. 8/4/2011 RKB
  23. 23. In establishing cause and effect relationship, an absolute must is “Temporal relation” Cohort study, best meets with this essentiality. Permits calculation of Incidence among exposed and non-exposed Permits calculating RR Permits examining multiple outcomes Since exposure is measured before outcome ,a developing outcome will have no opportunity to influence exposure. Some issues: Large sample size requirement Long follow up Measurement bias Selection bias Lost to follow up misclassification 8/4/2011 RKB
  25. 25. <ul><li>Intervention studies </li></ul><ul><li>Randomized controlled trials </li></ul><ul><li>Clinical trials </li></ul>8/4/2011 RKB
  26. 26. <ul><li>Key steps in randomized controlled trial </li></ul><ul><li>Clear information </li></ul><ul><li>Specific objectives of trail </li></ul><ul><li>Define the reference population </li></ul><ul><li>Select the study population </li></ul><ul><li>Select suitable subjects </li></ul><ul><li>Obtain informed consent </li></ul><ul><li>Collect baseline data </li></ul>8/4/2011 RKB
  27. 27. <ul><li>Randomly allocate the subjects to the (new) intervention or to the standard or placebo treatment (controls) </li></ul><ul><li>Follow up all subjects in both groups (minimizing and monitoring defaulters and subjects lost to follow up). </li></ul><ul><li>Make an assessment of defined outcome(s) continuously, intermittently, or at the end of trial (“blind” if appropriate) </li></ul><ul><li>Analysis – comparison of outcomes between intervention and control groups. </li></ul>8/4/2011 RKB
  28. 28. <ul><li>Interpretation – Magnitude of effect </li></ul><ul><li>- Alternative explanations of effect </li></ul><ul><li>e.g. bias in composition or follow-up of </li></ul><ul><li>groups </li></ul><ul><li>- Policy implications </li></ul><ul><li>Feedback results to the participants </li></ul><ul><li>Communicate key results to the relevant official bodies (eg Ministry of Health, non-governmental organizations), and the general public </li></ul>8/4/2011 RKB
  29. 29. 8/4/2011 RKB
  30. 30. For an incidental factor to be confounder: It must be associated with the exposure It must be an independent risk factor for the outcome It must NOT be intermediate in the chain of exposure to outcome It must be present in both study aswellas comparison group 8/4/2011 RKB
  31. 31. Bias <ul><li>Non random systematic error which occurs during study design/conduct/analysis/interpretation is called BIAS. </li></ul><ul><li>The study must be designed and conducted in such a manner that that every possibility of introducing a bias is anticipated and steps are taken to minimize its occurrence </li></ul><ul><li>If indeed the study has elements of bias, it can not be rectified at the stage of analysis (unlike confounding) </li></ul>8/4/2011 RKB
  32. 32. Types of bias <ul><li>Selection bias : mostly during study design stage. </li></ul><ul><li>A particular problem in case control and retrospective cohort studies where both exposure and disease have occurred at the time of selection of individuals for the study. </li></ul><ul><li>Eg: 1)Berksons’ Bias, 2)prevalence incidence bias, 3)healthy worker effect, 4)volunteer bias, 5)response bias, 6)loss to follow up bias. </li></ul>8/4/2011 RKB
  33. 33. Bias: <ul><li>Measurement bias : mostly during data collection phase. </li></ul><ul><li>eg: 1) Recall bias, 2) interviewer bias, 3)Diagnostic suspicion bias, 4) Exposure suspicion bias. </li></ul>8/4/2011 RKB
  34. 34. Confounding <ul><li>A confounder is a factor that is associated with the exposure and independently affects the risk of developing the disease. </li></ul><ul><li>It distorts the estimate of true relationship between the exposure and disease: it may result in association being observed when none in fact exists; or no association being observed when a true relationship does exist. </li></ul>8/4/2011 RKB
  35. 35. Confounding: example <ul><li>An observed association between the consumption of coffee and the risk of MI could be due, at least in part, to the effect of cigarette smoking, since coffee drinking is associated with smoking , and independent of coffee drinking, smoking is a risk factor for MI </li></ul><ul><li>The potential or true confounders are not always as obvious as seen here in this example. </li></ul>8/4/2011 RKB
  36. 36. Positive and negative confounding <ul><li>Tobacco smoking would be a positive confounder in association between coffee drinking and CAD </li></ul><ul><li>The association between physical activity and CAD would be negatively confounded by gender, since women have lower risk of CAD and they also exercise less than men. </li></ul>8/4/2011 RKB
  37. 37. Common confounders <ul><li>Age and sex are almost universal confounders for all exposure – disease associations </li></ul><ul><li>This is because they are markers for a whole lot of cumulative exposures. They may not be causally related to disease, but are markers for many other exposures which might be truly related to disease. </li></ul>8/4/2011 RKB
  38. 38. Controlling confounders <ul><li>Restriction of the study population </li></ul><ul><li>Matching </li></ul><ul><li>Randomization of exposure </li></ul><ul><li>Stratification </li></ul><ul><li>Multivariable analysis </li></ul>8/4/2011 RKB
  39. 39. I am indeed thankful for getting access to Educational materials made available to E-course participants under Indo-US collaboration, under Fogarty Grant with Medical College, Baroda. 8/4/2011 RKB
  40. 40. Acknowledgements: Prof. SL Kantharia Dr. NR Godara Dr. Deepak saxena Dr. RP Sridhar Prof. VS Mazumdar Dr. Shobha Misra Dr. Sangita Patel Dr. Kedar Mehta 8/4/2011 RKB
  41. 41. 8/4/2011 RKB