By Hatim Jaber MD MPH JBCM PhD 27+29 - 11- 2016 Faculty of Medicine Introduction to Community Medicine Course (31505201) Introduction to Statistics and Demography By Hatim Jaber MD MPH JBCM PhD 27+29 - 11- 2016

World AIDS Day 2016: end AIDS by 2030 People living with HIV 36.7 million People on antiretroviral therapy 18.2 million Mother-to-child transmission 7 out of 10

Presentation outline Time Introduction and Definitions of Statistics and biostatistics 12:00 to 12:10 Role of Statistics in Clinical Medicine 12:10 to 12:20 Basic concepts 12:20 to 12:30 Methods of presentation of data 12:30 to 12:40 12:40 to 12:50

Introduction to Biostatistics

Definition of Statistics Different authors have defined statistics differently. The best definition of statistics is given by Croxton and Cowden according to whom statistics may be defined as the science, which deals with collection, presentation, analysis and interpretation of numerical data. The science and art of dealing with variation in data through collection, classification, and analysis in such a way as to obtain reliable results. —(John M. Last, A Dictionary of Epidemiology ) Branch of mathematics that deals with the collection, organization, and analysis of numerical data and with such problems as experiment design and decision making. —(Microsoft Encarta Premium 2009)

Definition of Biostatistics= Medical statistics Biostatistics may be defined as application of statistical methods to medical, biological and public health related problems. It is the scientific treatment given to the medical data derived from group of individuals or patients Collection of data. Presentation of the collected data. Analysis and interpretation of the results. Making decisions on the basis of such analysis

Role of Statistics in Clinical Medicine The main theory of statistics lies in the term variability. There is No two individuals are same. For example, blood pressure of person may vary from time to time as well as from person to person. We can also have instrumental variability as well as observers variability. Methods of statistical inference provide largely objective means for drawing conclusions from the data about the issue under study. Medical science is full of uncertainties and statistics deals with uncertainties. Statistical methods try to quantify the uncertainties present in medical science. It helps the researcher to arrive at a scientific judgment about a hypothesis. It has been argued that decision making is an integral part of a physician’s work. Frequently, decision making is probability based.

Role of Statistics in Public Health and Community Medicine Statistics finds an extensive use in Public Health and Community Medicine. Statistical methods are foundations for public health administrators to understand what is happening to the population under their care at community level as well as individual level. If reliable information regarding the disease is available, the public health administrator is in a position to: ●● Assess community needs ●● Understand socio-economic determinants of health ●● Plan experiment in health research ●● Analyze their results ●● Study diagnosis and prognosis of the disease for taking effective action ●● Scientifically test the efficacy of new medicines and methods of treatment.

Why we need to study Medical Statistics Why we need to study Medical Statistics? Three reasons: (1) Basic requirement of medical research. (2) Update your medical knowledge. (3) Data management and treatment.

Role of statisticians To guide the design of an experiment or survey prior to data collection To analyze data using proper statistical procedures and techniques To present and interpret the results to researchers and other decision makers

I. Basic concepts Homogeneity: All individuals have similar values or belong to same category. Example: all individuals are Chinese, women, middle age (30~40 years old), work in a computer factory ---- homogeneity in nationality, gender, age and occupation. Variation: the differences in feature, voice… Throw a coin: The mark face may be up or down ---- variation! Treat the patients suffering from pneumonia with same antibiotics: A part of them recovered and others didn’t ---- variation! If there is no variation, there is no need for statistics. Many examples of variation in medical field: height, weight, pulse, blood pressure, … …

2. Population and Sample Population: The whole collection of individuals that one intends to study. Sample: A representative part of the population. Randomization: An important way to make the sample representative.

limited population and limitless population All the cases with hepatitis B collected in a hospital in Amman . (limited) All the deaths found from the permanent residents in a city. (limited) All the rats for testing the toxicity of a medicine. (limitless)  All the patients for testing the effect of a medicine. (limitless)  hypertensive, diabetic, …

Random By chance! Random event: the event may occur or may not occur in one experiment. Before one experiment, nobody is sure whether the event occurs or not. Example: weather, traffic accident, … There must be some regulation in a large number of experiments.

3. Probability Measure the possibility of occurrence of a random event. A : random event P(A) : Probability of the random event A P(A)=1, if an event always occurs. P(A)=0, if an event never occurs.

Estimation of Probability----Frequency Number of observations: n (large enough) Number of occurrences of random event A: m f(A)  m/n (Frequency or Relative frequency) Example: Throw a coin event: n=100, m (Times of the mark face occurred)=46 m/n=46%, this is the frequency; P(A)=1/2=50%, this is the Probability.

4. Parameter and Statistic Parameter : A measure of population or A measure of the distribution of population. Parameter is usually presented by Greek letter. such as μ,π,σ. -- Parameters are unknown usually To know the parameter of a population, we need a sample Statistic: A measure of sample or A measure of the distribution of sample. Statistic is usually presented by Latin letter such as s , p, t.

5. Sampling Error error :The difference between observed value and true value. Three kinds of error: (1)   Systematic error (fixed) (2)   Measurement error (random) (Observational error) (3) Sampling error (random)

Sampling error The statistics of different samples from same population: different each other! The statistics: different from the parameter! The sampling error exists in any sampling research. It can not be avoided but may be estimated.

II. Types of data 1. Numerical Data ( Quantitative Data ) The variable describe the characteristic of individuals quantitatively -- Numerical Data The data of numerical variable -- Quantitative Data

-- Enumeration Data 2. Categorical Data ( Enumeration Data ) The variable describe the category of individuals according to a characteristic of individuals -- Categorical Data The number of individuals in each category -- Enumeration Data

Special case of categorical data : Ordinal Data ( rank data ) There exists order among all possible categories. ( level of measurement) -- Ordinal Data The data of ordinal variable, which represent the order of individuals only -- Rank data

Examples RBC (4.58 106/mcL) Diastolic/systolic blood pressure Which type of data they belong to? RBC (4.58 106/mcL) Diastolic/systolic blood pressure (8/12 kPa) or ( 80/100 mmHg) Percentage of individuals with blood type A (20%) (A, B, AB, O) Protein in urine (++) (－, ±, +, ++, +++) Incidence rate of breast cancer ( 35/100,000)

III. The Basic Steps of Statistical Work 1. Design of study Professional design: Research aim Subjects, Measures, etc.

Statistical design: Sampling or allocation method, Sample size, Randomization, Data processing, etc.

2. Collection of data Source of data Government report system such as: cholera, plague (black death) … Registration system such as: birth/death certificate … Routine records such as: patient case report … Ad hoc survey such as: influenza A (H1N1) …

Data collection – Accuracy, complete, in time Protocol: Place, subjects, timing; training; pilot; questionnaire; instruments; sampling method and sample size; budget… Procedure: observation, interview, filling form, letter, telephone, web.

3. Data Sorting Checking Hand, computer software Amend Missing data? Grouping According to categorical variables (sex, occupation, disease…) According to numerical variables (age, income, blood pressure …)

4. Data Analysis Descriptive statistics (show the sample) mean, incidence rate … -- Table and plot Inferential statistics (towards the population) -- Estimation -- Hypothesis testing (comparison)

About Teaching and Learning Aim: Training statistical thinking Skill of dealing with medical data. Emphasize: Essential concepts and statistical thinking -- lectures and practice session Skill of computer and statistical software -- practice session ( Excel and SPSS )

Types of data Constant Variables

Types of variables Quantitative variables Qualitative variables continuous Qualitative nominal Quantitative descrete Qualitative ordinal

Methods of presentation of data Numerical presentation Graphical presentation Mathematical presentation

1- Numerical presentation Tabular presentation (simple – complex) Simple frequency distribution Table (S.F.D.T.) Title Name of variable (Units of variable) Frequency % - - Categories Total

Table (I): Distribution of 50 patients at the surgical department of AAAAA hospital in May 2008 according to their ABO blood groups Blood group Frequency % A B AB O 12 18 5 15 24 36 10 30 Total 50 100

Table (II): Distribution of 50 patients at the surgical department of AAAAA hospital in May 2008 according to their age Age (years) Frequency % 20-<30 30- 40- 50+ 12 18 5 15 24 36 10 30 Total 50 100

Complex frequency distribution Table Table (III): Distribution of 20 lung cancer patients at the chest department of AAAAA hospital and 40 controls in May 2008 according to smoking Smoking Lung cancer Total Cases Control No. % Smoker 15 75% 8 20% 23 38.33 Non smoker 5 25% 32 80% 37 61.67 20 100 40 60

Smoking Lung cancer Total positive negative No. % Smoker 15 65.2 8 Complex frequency distribution Table Table (IV): Distribution of 60 patients at the chest department of AAAAA hospital in May 2008 according to smoking & lung cancer Smoking Lung cancer Total positive negative No. % Smoker 15 65.2 8 34.8 23 100 Non smoker 5 13.5 32 86.5 37 20 33.3 40 66.7 60

Figure (1): Maternal mortality rate of (country), 1960-2000 Line Graph Year MMR 1960 50 1970 45 1980 26 1990 15 2000 12 Figure (1): Maternal mortality rate of (country), 1960-2000

Frequency polygon Age (years) Sex Mid-point of interval Males Females 20 - 3 (12%) 2 (10%) (20+30) / 2 = 25 30 - 9 (36%) 6 (30%) (30+40) / 2 = 35 40- 7 (8%) 5 (25%) (40+50) / 2 = 45 50 - 4 (16%) 3 (15%) (50+60) / 2 = 55 60 - 70 2 (8%) 4 (20%) (60+70) / 2 = 65 Total 25(100%) 20(100%)

Frequency polygon Age Sex M-P M F 20- (12%) (10%) 25 30- (36%) (30%) 35 40- (8%) (25%) 45 50- (16%) (15%) 55 60-70 (20%) 65 Figure (2): Distribution of 45 patients at (place) , in (time) by age and sex

Frequency curve

Histogram Figure (2): Distribution of 100 cholera patients at (place) , in (time) by age

Bar chart Marital Status

Bar chart Marital Status

Pie chart

Doughnut chart

3-Mathematical presentation Summery statistics Measures of location 1- Measures of central tendency 2- Measures of non central locations (Quartiles, Percentiles ) Measures of dispersion

1- Measures of central tendency (averages) Summery statistics 1- Measures of central tendency (averages) Midrange Smallest observation + Largest observation 2 Mode the value which occurs with the greatest frequency i.e. the most common value

1- Measures of central tendency (cont.) Summery statistics 1- Measures of central tendency (cont.) Median the observation which lies in the middle of the ordered observation. Arithmetic mean (mean) Sum of all observations Number of observations

Measures of dispersion Range Variance Standard déviation Semi-interquartile range Coefficient of variation “Standard error”

Standard déviation SD 7 8 7 7 7 7 7 7 7 7 6 7 3 2 7 8 13 9 Mean = 7 7 8 7 7 7 6 7 7 7 7 7 7 3 2 7 8 13 9 Mean = 7 SD=0.63 Mean = 7 SD=0 Mean = 7 SD=4.04

Standard error of mean SE A measure of variability among means of samples selected from certain population SE (Mean) = S n