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# Epidemiological statistics

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A simple guide to some commonly used epidemiological statistics for the medical practitioner

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• Nicely presented..brief and clear.Could you please send me a copy to this email (mh.hala2013@gmail.com) ?

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### Epidemiological statistics

1. 1. An introduction to some commonly usedterms of significance for all cliniciansEPIDEMIOLOGICALSTATISTICSMODERATOR: Prof Kakkar and Prof. R M KaushikPRESENTER- Dr.Garima Aggarwal
2. 2.  Epidemiology – It is the study of the rate/occurence of disease in a population Incidence – The number of new cases occuring in a defined population during a specified period of time. Prevalence – Refers to all current cases (OLD and NEW) of a disease/ condition at a given point /over a period of time in a given population P = Incidence X Duration
3. 3.  Sensitivity – Ability of a test to identify correctly all those who have the disease , that is “TRUE POSITIVE” ELISA for HIV is 99.5% sensitive Specificity – It is defined as the ability of a test to identify correctly those who do not have the disease, that is “TRUE NEGATIVE” ELISA for HIV is 98.5% specificFalse negative-Patients who have the disease are told that they do not have the disease.False positive- Patients who do not have the disease are told they have it.
4. 4. Statistical Averages MEAN – individual observations are added together and then divided by the number of observations MEDIAN – data is first arranged in an ascending or descending order of magnitude and then the value of the middle observation is located MODE- most frequently occurring observation in a series of observations STANDARD DEVIATION-
5. 5. EPIDEMIOLOGICALSTUDY OBSERVATIONAL EXPERIMENTALDESCRIPTIVE ANALYTICAL RCTs FIELD COMMUNITY TRIALS TRIALS
6. 6. Observational studies- CASE REPORT – clinical characteristic or outcome from asingle clinical subject CROSS SECTIONAL STUDY – study based on a singleexamination of a cross section of population at ONEPOINT IN TIME , where cross section is such that theresults can be projected on the entire study population CASE CONTROL STUDY – study of a group of peoplewith the disease and compares them with a suitablecomparison group without the disease , i.e. CASES andCONTROLS. Retrospective study.
7. 7.  COHORT STUDY – population group of those who have been exposed to risk factor is identified and followed over time and compared with a group not exposed to the risk factor. Prospective study. CASE CONTROL COHORT CROSS SECTIONAL
8. 8. Experimental studies - RANDOMISED CONTROLLED TRIALS – subjects in the study are randomly allocated into “intervention” and “control” groups to receive or not to receive an experimental preventive or therapeutic procedure or intervention.Most scientifically rigorous studies. Select RAND Experiment Select al V/Spopulation suitable O- control Manipulation Blinding ASSESSMENT sample MISE group
9. 9. Statistical Analysis -  For observational studies Relative Risk – Ratio of the incidence of the disease (or death) among exposedgroup and the incidence among non exposed Relative Risk = 1 = no association, >1 = positive associationDirect measure of the ‘strength’ of association between suspected cause andeffect IMR in whites in the US is 8.9 per 1000 live births, and 18.0 in blacks. So theRelative risk of Black v/s White population is 18/8.9 = 2.02. Therefore Black infantsare twice as likely to die in the first year of life.Attributable Risk – It is the difference in incidence rates of disease (or death)between an exposed group and non exposed group.ATTRIBUTABLE RISK = (incidence of disease among exposed – incidence ofdisease among non exposed) / incidence of disease among exposed x 100 Using above example, AR= 18.0-8.9 = 9.1, hence Of every 1000 black infantsthere were 9.1 more deaths than were obsereved in 1000 white infants
10. 10. Exposure to Risk factor CASES CONTROL (Disease Present) (Disease Absent)PRESENT a bABSENT c d a +c b+ d  ODDS RATIO – looks at the increased odds of getting a disease with exposure to a risk factor as opposed to getting the disease without exposure.  OODS RATIO = a x d / b x c SMOKING LUNG CANCER Without LUNG CA. Smokers 33 55 Non Smokers 2 27 total  ODDS RATIO = 33 X 27 / 2 X 55 = 8.1 Smokers showed a risk of having Lung Cancer 8.1 times that of Non smokers.
11. 11. Inferential statistics CONFIDENCE INTERVAL – Confidence intervals are a way of admitting that any measurement from a sample is only an estimate of the population A confidence interval specifies how far above or below a sample based value , the population value lies within a given range , from a possible high to a possible low. We have 95% confidence intervals and 99% confidence intervals. If the confidence interval contains 1.0 it is not statistically significant
12. 12. What is the ‘p value’??? With scientific methods – we put forward a research question eg. Smokers more likely to get lung cancer! Null hypothesis – says that all findings are a result of chance or random factors i.e. smoking has no real relation with lung cancer Hypothesis testing – ‘p value’ – helps to interpret output from a statistical test. It is the standard against which we compare our results. If p value < or = 0.05 - the results are statistically significant, i.e. REJECT NULL HYPOTHESIS If p value > 0.05 – statistically insignificant, i.e. DO NOT REJECT NULL HYPOTHESIS
13. 13. Statistical tests - META-ANALYSIS- A statistical way of combining results of many studies to produce one overall conclusion. Correlation coefficient – It indicates the degree to which two measures are related It ranges from -1.o to +1.0 Medical school grades and various factors affecting it.Positive value – two variables go together in the same direction. IQ has a positive corelation with medical grades.Negative value – presence of one variable is associated with absence of another. Time spent on outdoor activities negative correlation with grades.
14. 14.  t tests – used to compare MEANS of two groups. Can be used for testing two groups only. Paired t test – when comparing ‘before’ and ‘after’ results in the same group. Unpaired t test – when comparing means of two groups. Chi square – can be used for any number of groups. Used for nominal data.
15. 15. Thank you for your patience.