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Types of Epidemiological Research
Discovering Biostatistics , Using SPSS

Dr. Ahmed ALbehairy, M.D
Consultant Psychiatry, MOH, Egypt

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  1. 1. Types of EpidemiologicalResearchDr. Ahmed ALbehairy, M.DConsultant of Psychiatry, MOH,Egypt
  2. 2. Types of Epidemiological Research• Descriptive.• Analytical• Experimental and clinical trials• Meta-analysis.
  3. 3. Types of Analytical EpidemiologicalStudies• Retrospective studies.• Prospective studies.• Historical prospective studies.• Cross sectional, prevalence or a surveystudy.
  4. 4. Retrospective studies and /or CaseControl Studies• In this kind of studies, the subject under the study havethe disease, and their past experiences are comparedwith other persons who do not have this or relateddisease.• Selection of cases should consist of all newly diagnosedcases with specified parameters under study during aspecified period of time.• Controls are representative of the general population interms of probability of exposure to the risk factor underthe study.
  5. 5. Prospective studies and /or Cohort• The philosophy of this approach is that exposedsubject in the investigation are representativesof all exposed persons in regard with risk underconsideration.• Healthy individuals , cohorts are allocated andfollowed forward in time for development ofspecific disease.• Types of cohort :Birth cohort, marriage , specific graduation.
  6. 6. Historical prospective studies.• Include the follow up of healthy exposedand unexposed subjects, cohort, for thedevelopment of disease.• However this cohort are allocatedretrospectively through medical records.
  7. 7. Cross sectional, prevalence or asurvey study.• Both the risk factor and the disease areexamined at the same time .• Temporality of the risk is not evident .
  8. 8. PAST PRESENT FUTUREretrospcontrol prosphist.prosptypes of analytic studiesSelectcasesLook forexposureto risk factorSelect cohortaccordingto exposureFollow upTo record theDiseaselevelAccording toExistingRecords, determineExposure inthe pastIdentifycohortin the pastDevelopmentOf disease
  9. 9. Research /service project model• Type of study.• Budget.• Site, community.• Date, start, close.• Criteria ( inclusion and exclusion).• Procedure plan.
  10. 10. DiscoveringBiostatistics ,Using SPSSDr. Ahmed ALbehairy, M.DConsultant of Psychiatry, MOH, Egypt
  11. 11. Introduction for BiostatisticsThe main goal to improve patient carethrough more understanding of researchand to be critical thinkers, do study designand do statistics.Of course , you may need statistician to bewith you in advanced issues.
  12. 12. Population vs. samplesAs a researcher , we are interested infinding results that apply to entirepopulation of people or things. ( we cannotcollect data from every human being).Therefore , we collect data from a smallsubset of population ( known as sample).
  13. 13. Source of Data in Population(Epidemiological data )• Census.• Vital statistics.• Morbidity data.• National health network.
  14. 14. SamplingHow to collect data that representpopulation ??????>>>> reducethe population to a statisticalmodel>>>>>so this statisticalmodel make predictions about thereal – world phenomenon.
  15. 15. Sampling
  16. 16. Hypotheses• A hypothesis is a proposed explanation forthe occurrence of a phenomena that aresearcher formulates prior to conductingan experiment.• Types of hypotheses.??????• How to test your hypotheses.????
  17. 17. Types of Hypotheses.??????Null hypothesisVs.Alternative hypothesis
  18. 18. Null hypothesis, Vs. AlternativehypothesisIf non directional ,H0:µ or P = K ( i.e. mean has no diff. to a value).HΑ:P # K ( i.e. mean is not equal to a value).if directionalH0:P ≤ KHΑ:P <KH0:P ≥ KHΑ:P >K
  19. 19. Testing Null Hypothesis
  20. 20. Testing Null Hypothesis• N0:= hypothesis that there is no relation ordifference .• If significant , P >0.05, reject N0, , i.e. falsehypothesis , there is a relation or there is adifference.• If non significant ,accept N0, i.e. true hypothesis,there is no relation , there is no difference. ( it isnot no relation , but it is only statistically nonsignificant ).
  21. 21. Available data and hypothesis ,what we will do ???Statistical tools ,results todiscuss
  22. 22. Statistics• A branch of applied mathematicsconcerned with the collection andinterpretation of quantitative data , and theuse of probability theory to estimatepopulation parameters.• Concerned with treatment of quantitativeinformation from groups of individuals.
  23. 23. What can statistics do?• Provide objective criteria for evaluatinghypothesis.• Synthesis of information.• Help to detect the pattern of data( descriptive statistics).• Help to evaluate argument ( researchquestions and hypothesis ).
  24. 24. Statistics Cannot?• Tell the truth ( it can only give probabilityonly ).• Compensate poor design.• Indicate clinical significance.
  25. 25. Statistics don not Prove any thing• Statistics suggest a relationship.• In order to make conclusion you need :- Multiple converging indicators.- Multiple confirmatory studies.- Temporal relationship.- Dose response.- Biological response.- Biological plausibility ( reasoning).
  26. 26. Think in Research? How to• Hypotheses and introduction.• Is the research quantitative or qualitative .• Collecting data . Sampling , prepare tools and surveymethod.• Preparing Data. Types of variables, Dependent,Independent, Categorical ,Continuous.• Data entry.• Exploring Data. ( parametric, nonparametric ).• Descriptive statistics.• Inferential / analytical Statistics.• Results, discussion , conclusion .
  27. 27. Types of Variablesstring and numerical• Qualitative:- categorical,- Nominal- Usually independent- Analyzed byfrequency table.- Example?• Quantitative• Continuous• Scale, ordinal,• Usually dependent onpredictor• Analyzed byexamining centraltendency (mean..etc.)• examples
  28. 28. In Qualitative Research N.B:• Prepare information as variables.then• Descriptive and analytical statistics.
  29. 29. Statistics & Population• Descriptive : frequencies.• Inferential .• Periodic report : SWAT AnalysisStrengths, weakness, opportunities, threats• Ratio and percentages.
  30. 30. Statistics & Population• Incidence rate =no. of new cases at point of time * 100 or 1000population at riskno. of new cases during a period of time * 100 or 1000population at risk
  31. 31. Statistics & Population• Incidence rate of rare disease =no. of new cases during a period of timepopulation at mid of the year during this period of time• Incidence rate in outbreak situation = attack rate• Inception rate = new attacks of illness ina population / year .( attacks may exceed thenumber of population).
  32. 32. Statistics & Population• Prevalence rate =no. of existing cases at a point of time * 100 or 1000total number of populationNo of existing cases during a period of time * 100 or 1000total number of populationAnnual prevalence : total no. of disease at any time during a year.Life time prevalence : total no. of individuals known to have thedisease at least part of their life time.
  33. 33. Statistics & Population• Segmentation: Divide populations intosegments.• Profiling: Develop profiles of hotspot segments.• Drill-down: drill-down dimensions and numericalvalue ranges.• Variable selection: select variables used inprofiling and segmentation.• Ranking: Order segments based rankingcriteria.• Visualization: Visualize result statistics.
  34. 34. Statistics & Sample
  35. 35. Statistics & Sample• The sample can be summarized statistically by what is called“mean”. The center of distribution of the scores.• It is hypothetical value of typical score X . ????• Sum of Deviances from the mean = total error = of course 0• To be considered mathematicallySum of squared error (SS) are done.• To avoid the effect number of sample on the error, to estimate theerror in the population ----variance = SSn-1 ?? df?• SD+ = Square root of variance.• SD, a measure of how well the mean represent the data.• Small SD, indicates that the data points are close to the mean.• Large SD, indicates that the data points are distant from the mean.• Larger SD , i.e. that the mean is not accurate representation of thedata .• Smaller SD, i.e. that the mean is of small fluctuation.
  36. 36. Statistics & Sample• of course , in different sample the mean and SD , showsthat the sample is not in normal distribution .• By z score , ( when any sample can be reformed to anormal distribution , by making mean =0 and SD = 1 ) wecan calculate the probability, cumulative percentage ofany values in the data, and how the distribution.• e.g when 95% z score lies between + 1.96• Standard error.SE is SD of sample means. Small SEindicates that most sample means are similar to thepopulation mean, and so our sample is likely to be anaccurate reflection of the population .
  37. 37. StatisticsZscore: writing scoreN Valid 200Missing 0Mean .0000000Std. Error of Mean .07071068Median .1292387Std. Deviation 1.00000000Minimum -2.29728E0Maximum 1.50075Percentiles25 -7.9389478E-150 1.2923869E-175 7.6224449E-1
  38. 38. Statistics & Sample• Another way to think in the sample andrepresent the data than mean is “linearmodel “. It s the basic of ANOVA &regression .• Linear model is based on central tendencyand means .
  39. 39. Descriptive Statistics• Method of organizing and summarizingdata in table , graph or numbers.• Frequencies , %, cumulative %• Mean , median ,mode• SD , SE of mean. Level of confidence• Skewness ,kurtosis , SE of skew , SE ofkurtosis . Parametric / non parametric
  40. 40. Inferential analysis• It s a decision to choose the right way to do your analysis accordingto :1- parametric vs. non parametric.2- level of confidence.3- hypothesis4- type of independent variable/s.5- type of dependent variables/s6- number of group / means .7- related participants or not .(one or more)8- repeated means .8- difference , correlation, or regression.9- reliability and validity for scales .
  41. 41. Inferential analysisdecision treeexamples
  42. 42. Most popular examplesparametric
  43. 43. Most popular examplesnon parametric
  44. 44. SPSS, training• View : data/variable• Creating data file• Name of variable• ID• Abbreviation list• Variable type, width, decimal, label,value• Missing value• Measurement.• Entering variable.
  45. 45. SPSS, training• Option• Help : topic , tutorial , statistical coach• Transform ( recode – compute variables)• Analyze :Frequency, descriptive, cross table,compare means t test , GLM, correlation ,regression, log linear , scalenonparametric
  46. 46. Clinical• Data entry• ‫مسائل؟‬