Introduction to biostatistic
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HISTORY AND BASIC TERMNOLOGY OF BIOSTATISTICS
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Introduction to biostatistic
- 3. . – Records of population census in Babylonia and China were found Arthasasthra,written by chanakya, describes the population and economic census during mauryan
- 4. (578-534 B.C.) The sixth king of Rome, was the first one to institute population gathering data. Each male in the roman empire had to return to the city of his birth to be counted and taxed.
- 5. : • British statistician • Father Of demography. • Book- Natural And Political Observation Made Upon Bills Of Mortality(1662).
- 6. • Gottfried Achenwall coined the term “STATISTICS” in 1749. • Latin word – Council Of State • Italian –politician
- 7. • Father Of Biostatistics • Concept- Correlation • He Used Questionnaires
- 8. Indian Statistical Scientist Prasanta Chandra Mahalanobis (Born-29 JUNE 1893) Contribution:- • Mahalanobis distance, a statistical measure. • Member of first planning commission of free India • Founded Indian Statistical Institute. 29 June - A National Statistical Day
- 9. SOFTWARE SAS-developed in 1976. • Used for advanced analysis • Multivariate analysis • Data management • Predictive analysis
- 13. Statistics is a branch of mathematics that deals with the methods of : Collection, Compilation, Analysis, Presentation, And Interpretation Of Data. It is defined as application of statistical methods to Medical, Biological and Public health related problems.
- 15. : Arrangement of a group of number in rows and columns in ascending order of value. Qualitative character that can be expresed numerically .
- 16. : qualitative or quantitative information required from an individual from a population or sample during survey. Eg:weight,height,gender,marital status etc : Quantities that do not vary e.g . mean, standard deviation, are considered constant for a population .
- 17. : It is a summarized characteristic of a Population Eg;every people in India have black hair. : Statistics is a summarized characteristic of a Sample Eg; Out of 500 students in a college 200 students are girls.
- 18. Probability is the chance that event will happen. Its measured in scales from p=0 impossibility p=1 absolute certainty Raw data of statistics are groups of number before they are organised and processed.
- 19. is the ongoing collection by government agencies of data relating to events such as births, deaths, marriages, divorces and health- and – disease related conditions deemed reportable by local health authorities.
- 20. :Population refers to the entire collection of objects which a study is interested in. -Objects can be people,animals,plants etc. INFINITE FINITE POP
- 21. - Countable, it contains finite number of objects (individual) Eg-Number Of students in a class -Not countable, it contains infinite number of objects(individuals). Eg-Depth of various points on earths ocean.
- 22. : A collection of sampling units selected from the population : A member of the population.
- 24. • A variable is something whose value can vary. For example:- age, sex and blood type are variables. • Data are the values you get when you measure a variable. For example:-32 years (for the variable age), or female (for the variable sex).
- 27. Categorical variables 1.Nominal categorical variables • Consider the variable blood type. Let’s assume for simplicity that there are only four different blood types: O, A, B, and A/B.
- 28. • The data do not have any units of measurement. • The ordering of the categories is completely arbitrary.
- 29. • Ordinal categorical variables Eg.the Glasgow Coma Scale(GCS) GCS score can vary from 3 (death or severe injury) to 15 (mild or no injury).
- 31. • Metric variables Continuous metric variables
- 32. • Metric continuous variables can be properly measured and have units of measurement. • They produce data that are real numbers (located on the number line). • Because metric data values are real numbers, you can apply all of the usual mathematical operations to them. This opens up a much wider range of analytical possibilities than is possible with either nominal or ordinal data
- 33. • Discrete metric variables
- 34. • Continuous metric data - measuring. • Discrete metric data - counting. For example:- number of deaths, number of pressure sores, number of angina attacks • Metric discrete variables can be properly counted and have units of measurement • They produce data which are real numbers located on the number line.
- 36. – Data are the basic building blocks of statistics and refers to the individual values presented, measured, or observed.
- 38. Data in numerical quantities such as continuous measurements or counts. –Observation for which the difference between numbers have meaning on numerical scale –They measure the quantity of something •Continuous scale (e.g. Age, height) •Discrete scale (e.g. Number of pregnancy)
- 39. • Observation of information characterized by measurement by categorical scale (dichotomous,nominal and ordinal scale) •Data that describe a quality of the subject studied. E.g. Gender, Presence Of Diabetes, Nationality etc.
- 41. : – Nominal – Ordinal A variable measured on a nominal scale is characterized by named categories having no particular order. For example: –patient gender (male/female), –use of fluoridated water (yes/no) are all categorical variables measured on a nominal scale.
- 42. Ordinal scale data are variables whose categories possess a meaningful order. For example: –Severity of Anaemia(0=none, 1=mild,2=moderate, 3=severe) – Length of time spent in a hospital waiting room (1= less than 15 min, 2= 15 to less than 30 minutes, 3= 30 minutes or more) are variables measured on ordinal scales.
- 43. are measured by • Interval • Ratio In this scale interval between two successive number is fixed and equal. Eg; In Temperature difference between 30 and 40 degree Celsius is 10 degree Celsius ,likewise difference between 60 and 70 degree Celsius is also 10 degree Celsius. But one can not say that 60 degree Celsius is twice warm as temperature of 30 degree Celsius . In Interval scale a real zero doesn't exist.
- 44. :In addition to interval scale of measurement ,here true zero exist. • eg:length in centimeter,weight in kilograms.
- 45. are numerical, or based on numbers. An example of quantitative data is height measured in inches. are non-numerical, or based on a categorical scale.An example of qualitative data in height measured in terms of short, medium, and tall.
- 46. • Biostatistics helps to define what is normal and it also gives limits of normality. eg;Hemoglobin level. • It allows us to compare information from one city or region to that of other . • Find out correlation between two variables • Provides useful information for Planning and Implementation of various health program, Monitoring and Evaluation of such programmes.
- 47. • To provide the magnitude of any health problem in the community. • To find out the basic factors underlying the ill-health. • To evaluate the health programs which was introduced in the community (success/failure).
- 48. • Biostatistics cannot be used for study of qualitative phenomena. Eg; race,sex. as it cannot be expressed numerically. • It does not study individuals. • Statistical law are not exact like physics, its only a approximations by which it suggest appropriate models.
- 49. • S.Karthikeyan,biostatistic and research methodology,1st edition,2016,pg no.36-57 • David bowers medical statistics from scratch :an introduction for health rofessionals,2nd edition,2016 • Dr.J.V.Dixit,prnciple and practice of biostatistics,7th edition,2017,pg no 1-11
- 50. THANK YOU
Editor's Notes
- Today we are going to enter into the most powerful topic in the world but it hasn’t got its recognition as it must have got It could turn the economy upside down and it has the powe to chose the pm or presicent Yes im going to talk about statistic
- DEMOGRAPHY IS THE SATISTICAL STUDY OF POPULATION Book PRESENTED POP AND MORTALITY STATISTIC OF LONDON DURING THE TIME OF PLAGUE First estimation of population statisticaly based. .
- 1749 meaning was restricted to information about the population for taxation and military In 19th centuary the meaning was extended to analysis and interpretation of such data
- 1860 s He was charles darwin half cousin Used questionare for collecting data in human communities Biostatistic aka biometry
- Born-29 JUNE 1893 He was awarded pathma vibushan by goi in 1968 for his contribution in statistic Goi in 2006 to celebrate his bday 29 june as a national statistic day NATIONAL STATISTIC DAY -20 OCT 2010-EVERY 5 YEARS LAST 2015
- SAS-Statistical analysis softwareNorth Carolina State University First version was called 76 version Recent was released in 2017 sas studio 3.71 and run on sas 9.4m5
- MINISTRY WAS EST IN 15/10/1999 IT HA STWO WINGS STATISTICAL WING AND THE PROGRAMME IMPLEMENT WING concerned with coverage and quality aspects of PROGRAM .The surveys conducted by the Ministry
- The National Data Bank of Socio-Religious categories is developed to provide users access to all data, pertaining to various aspects of socio-economic life of population in different social/religious categories, from a single window 1-Education 2-Health 3-Labour and Employment 4-Population 5-Programmes/Schemes Related Data 6-Socio-Economic Caste Census Data 7- State-wise Distribution of Working Enterprises by Social/Religious Ownership of Registered / Unregistered Sectors 8-Population By Religious Community (India & States/UTs/District/Sub-Distt/Town Level) 9-Census 2011 Data 10-Census 2001 Tables 11-Schemes 12-DLHS 4 data on various aspect on SRC
- DISTRICT LEVEL HOUSEHOLD SURVEY
- BASICALLY STATISTICS COMPRISES OF 2 SETS OF ACTIVITIES DESCRIPTIVE STATISTICS :GENERALLY CHARATERISE OR DESCRIBE A SET OF DATA ELEMENTS BY GRAFICALLY DISPLAYING THE INFO EG OF DESCRIPTIVE STATISTIS INCLUDE :STATISTICAL AVERAGE ,MEASURES OF DISPERSION,CENSUS IS ONE OF OLDEST FORM OF DESRIPTIVE STATISTICS INFERENTIAL STATISTICS : DO NOT JUST DESCRIBE THE NUMBERS ,THEY INFER CAUSES For example, you might stand in a mall and ask a sample of 100 people if they like shopping at Sears. You could make a bar chart of yes or no answers (that would be descriptive statistics) or you could use your research (and inferential statistics) to reason that around 75-80% of the population (all shoppers in all malls) like shopping at Sears.
- eg
- 1)This describes a population ,hence it is a parameter. 2) This 45% will be statistic as it describes the sample.
- In statistics the term population also called the universe ,dsnt necessarily mean people .It is a techniqual term for the whole group of people ,animals n objects under study . A complete set of data elements about which a statistical enquiry is being made is called population Or universe. The total number of objects or individuals in population is known as size of population.population is finite when it contents finite numbers of objects or individual such as number of students in class.Population is infinite if it contains infinite number of objects or individual such as depths at various points on the earths oceans 3)Sample : finite set of objects drawn from population for further analysis or study is called sample.the total number of individuals in sample is called sample size. 4)Statistic :it is a measure derived from sample such as sample mean ,sample standard deviation and corelation coefficient.these measures describe the sample .
- Variables are put in
- Suppose we have a group of 100 patients.We can first determine the blood type of each and then allocate the result to one of the four blood type categories.We might end up with a table like Table 1.2. By the way, a table like Table 1.2 is called a frequency table, or a contingency table. It shows how the number, or frequency, of the different blood types is distributed across the four categories. So 65 patients have a blood type O, 15 blood type A, and so on.We’ll look at frequency tables in more detail in the next chapter. The variable ‘blood type’ is a nominal categorical variable. Notice two things about this variable, which is typical of all nominal variables: The data do not have any units of measurement.3 The ordering of the categories is completely arbitrary. In other words, the categories cannot be ordered in any meaningful way.4 In other words we could just as easily write the blood type categories as A/B, B, O, A or B, O, A, A/B, or B, A, A/B, O, or whatever. We can’t say that being in any particular category is better, or shorter, or quicker, or longer, than being in any other category.
- By the way, a table like Table 1.2 is called a frequency table, or a contingency table. It shows how the number, or frequency, of the different blood types is distributed across the four categories. So 65 patients have a blood type O, 15 blood type A, and so on.We’ll look at frequency tables in more detail in the next chapter. The variable ‘blood type’ is a nominal categorical variable. Notice two things about this variable, which is typical of all nominal variables: The data do not have any units of measurement.3 The ordering of the categories is completely arbitrary. In other words, the categories cannot be ordered in any meaningful way.4 In other words we could just as easily write the blood type categories as A/B, B, O, A or B, O, A, A/B, or B, A, A/B, O, or whatever. We can’t say that being in any particular category is better, or shorter, or quicker, or longer, than being in any other category The data do not have any units of measurement.3 The ordering of the categories is completely arbitrary
- Let’s nowconsider another variable some of youmay be familiar with – the Glasgow Coma Scale, or GCS for short As the name suggests, this scale measures the degree of brain injury following head trauma.Apatient’s GlasgowComaScale score is judged by their responsiveness, as observed by a clinician, in three areas: eye opening response, verbal response and motor response. The GCS score can vary from 3 (death or severe injury) to 15 (mild or no injury). In other words, there are 13 possible values or categories of brain injury.
- Imagine that we determine the Glasgow Coma Scale scores of the last 90 patients admitted to an Emergency Department with head trauma, and we allocate the score of each patient to one of the 13 categories. The results might look like the frequency table shown in Table The Glasgow Coma Scale is an ordinal categorical variable. Notice two things about this variable, which is typical of all ordinal variables: The data do not have any units of measurement (so the same as for nominal variables). The ordering of the categories is not arbitrary as it was with nominal variables. It is now possible to order the categories in a meaningful way. In other words, we can say that a patient in the category ‘15’ has less brain injury than a patient in category ‘14’. Similarly, a patient in the category ‘14’ has less brain injury than a patient in category ‘13’, and so on. However, there is one additional and very important feature of these scores, (or any other set of ordinal scores). Namely, the difference between any pair of adjacent scores is not necessarily the same as the difference between any other pair of adjacent scores. For example, the difference in the degree of brain injury between Glasgow Coma Scale scores of 5 and 6, and scores of 6 and 7, is not necessarily the same. Nor can we say that a patient with a score of say 6 has exactly twice the degree of brain injury as a patient with a score of 12. The direct consequence of this is that ordinal data therefore are not real numbers. They cannot be placed on the number line.5 The reason is, of course, that the Glasgow Coma Scale data, and the data of most other clinical scales, are not properly measured but assessed in some way, by the clinician working with the patient.6 This is a characteristic of all ordinal data. Because ordinal data are not real numbers, it is not appropriate to apply any of the rules of basic arithmetic to this sort of data. You should not add, subtract, multiply or divide ordinal values. This limitation has marked implications for the sorts of analyses we can do with such data
- The variable ‘weight’ is a metric continuous variable.With metric variables, proper measurement is possible. For example, if we want to know someone’s weight, we can use a weighing machine, we don’t have to look at the patient and make a guess (which would be approximate), or ask them how heavy they are (very unreliable). Similarly, if we want to know their diastolic blood pressure we can use a sphygmometer.7 Guessing, or asking, is not necessary. Because they can be properly measured, these variables produce data that are real numbers, and so can be placed on the number line. Some common examples of metric continuous variables include: birthweight (g), blood pressure (mmHg), blood cholesterol (μg/ml), waiting time (minutes), body mass index (kg/m2), peak expiry flow (l per min), and so on. Notice that all of these variables have units of measurement attached to them. This is a characteristic of all metric continuous variables. In contrast to ordinal values, the difference between any pair of adjacent values is exactly the same. The difference between birthweights of 4000 g and 4001 g is the same as the difference between 4001 g and 4002 g, and so on. This property of real numbers is known as the interval property (and aswe have seen, it’s not a property possessed by ordinal values).Moreover, a blood cholesterol score, for example, of 8.4 μg/ml is exactly twice a blood cholesterol of 4.2 μg/ml. This property is known as the ratio property (again not shared by ordinal values)
- Consider the data in Table 1.5. This shows the number of times in the past 24 hours that each of six children with asthma used their inhalers
- Continuous metric data usually comes from measuring. Discrete metric data, usually comes from counting. For example, number of deaths, number of pressure sores, number of angina attacks, and so on, are all discrete metric variables. The data produced are real numbers, and are invariably integer (i.e. whole number). They can be placed on the number line, and have the same interval and ratio properties as continuous metric data: Metric discrete variables can be properly counted and have units of measurement – ‘numbers of things’. They produce data which are real numbers located on the number line.
- The easiest way to tell whether data is metric is to check whether it has units attached to it, such as: g, mm, ◦C, μg/cm3, number of pressure sores, number of deaths, and so on. If not, it may be ordinal or nominal – the former if the values can be put in any meaningful order
- QUALITATIVE DATA : NON NUMERIC :EG SKIN COLOR, RACE,COLOR OF EYES ,GENDER, OCCUPATION,PLACE OF BIRTH ,BLD GRPS Could be in the form of words,texts,photographs,videos or sound recording . Quantitative data:numeric discrete: has specific numeric values.,that r whole numbers .obtained by counting n not by measuring .eg number of babies born,number of persons with specs,no of siblings . continuous : infinite numerical values in form of whole number,decimals and frations . eg :physically measurable quantities,like cost,length,ht,wt,bp,blood cell counts ,hb,bld sugar level
- Data in numerical quantities such as continuous measurements or counts. –Observation for which the differences between numbers have meaning on a numerical scale. –They measure the quantity of something
- Observation or information characterized by measurement on a categorical scale (dichotomous, nominal or ordinal scale). Data that describe a quality of the subject studied.
- Severity of pain, staging of cancer, grades of malnutrition.
- Ratio scale has highest level of refinement & represent the actual measurement of the variable.
- In pubic health it provides useful information for Planning and Implementation of various health program, Monitoring and Evaluation of such programmes.