Introduction to Biostatistics PDF

Introduction  STATISTICS and BIOSTATISTICS Statistics is the art and science of data. It deals with Planning Research Collecting Data Describing Data Summarizing- Presenting Data Analyzing Data Interpreting Results Reaching decisions or discovering new knowledge ...

Introduction  STATISTICS and BIOSTATISTICS Statistics is the art and science of data. It deals with Planning Research Collecting Data Describing Data Summarizing- Presenting Data Analyzing Data Interpreting Results Reaching decisions or discovering new knowledge  * Biostatistics: The tools of statistics are employed in many fields: business, education, psychology, agriculture, economics, … etc. When the data analyzed/managed are derived from the biological science and medicine, we use the term biostatistics to distinguish this particular application of statistical tools and concepts.  Goals of Biostatistics Improvement of the intellectual content of the data Organization of data into understandable forms Reliance on test of experience as a standard of validity The Cycle of Statistical Investigation Real problems Design method of Curiosity data collection Pose the question Answer to original question Collect data Interpret the results - Summary and What do they mean? analysis of data Data: The raw material of Statistics is data. We may define data as figures. – Figures result from the process of counting or from taking a measurement. For example: - When a hospital administrator counts the number of patients (counting). - When a nurse weighs a patient Patient’s temperature, weight, height, arterial blood pressure (measurement)  * Sources of Data: We search for suitable data to serve as the raw material for our investigation. Such data are available from one or more of the following sources: 1- Routinely kept records. For example: - Hospital medical records contain immense amounts of information on patients. 2- External sources. The data needed to answer a question may already exist in the form of “published reports, commercially available data banks, or the research literature”, i.e. someone else has already asked the same question. 3- Surveys: The source may be a survey, if the data needed is about answering certain questions. For example: If the administrator of a clinic wishes to obtain information regarding the mode of transportation used by patients to visit the clinic, then a survey may be conducted among patients to obtain this information. 4- Experiments. Frequently the data needed to answer a question are available only as the result of an experiment. For example: If a nurse wishes to know which of several strategies is best for maximizing patient compliance, she might conduct an experiment in which the different strategies of motivating compliance are tried with different patients.  Variable: (Measurement) ‫متغير‬ It is a characteristic that takes on different values in different persons, places, or things. For example: - heart rate, - the heights of adult males, - the weights of preschool children, - the ages of patients seen in a dental clinic.  Constant ‫ثابت‬ Observation which do not vary from time to time or from person to person  number of fingers, number of eyes  Types of Data There are different ways to classify variables and measurements – Categorical measurements place observations into unordered categories – Ordinal measurements place observations into categories that can be put into rank order – Quantitative measurements impose equal spacing between ordered intervals  Type of Variables (Measurements) Quantitative Variables Qualitative Variables (Categorical Observations are along a or Nominal) numeric scale Many characteristics are not capable of being measured. For example: Some of them can be ordered or - the heights of adult males, ranked. - the weights of preschool For example: children, - Sex, - Age - Blood pressure - Blood Group - Volume - Taste - Density - color - Mass - social classes based on income, education, etc.  Quantitative variables A discrete variable A continuous variable is characterized by gaps or can assume any value within a specified interruptions in the relevant interval of values assumed values that it can by the variable. assume. For example: For example: - Height, - The number of daily - weight, admissions to a general - skull circumference. hospital, - The number of decayed, Noobserved matter how close together the heights of two people, we missing or filled teeth per can find another person whose child in an elementary height falls somewhere in between. school - -.  KNOW YOUR Qualitative Variables (Categorical or Nominal)  Qualitative Variables with two categories (Binary = Dichotomous=0/1) These often relate to the presence or absence of some attribute Male/female Disease/No disease Married/Unmarried Diabetic/non diabetic Smoker/non smoker (ex-smoker?) Hypertensive/normotensive  Qualitative Variables with more than two categories 1= Nominal – Country of birth – Blood group A/B/AB/O – Married/Single/Divorced/Separated/ Widow 2= Ordinal: qualitative variables whose categories can be put in a definite order – STAGE OF CANCER classified as stage I, stage II, stage III, stage IV – OPINION classified as strongly agree (5), agree (4), neutral (3), disagree (2), strongly disagree (1); so-called Liekert scale Any quantitative variable can be converted into categorical one (two categories or ordinal)  Quantitative Variable Categorization Ordinal Two categories Systolic blood pressure >140 mm Hg Hypertensive Normal 90-140 Normal Abnormal 120 Hyperglycaemia Normal 80-120 Normal Abnormal 10/day Heavy Smoker 5-10 Moderate Non-smoker