introduction to biostatistics.pptx

Introduction to biostatistics and its application in various sectors.
Introduction to variables and variation.
Different types of variables and their introduction.
Use of biostatistics in various fields.

1. BIOSTATISTICS AND RESEARCH METHODOLOGY Unit-1: biostatistics introduction and its applications PRESENTED BY Himanshu Rasyara B. Pharmacy IV Year UNDER THE GUIDANCE OF Gangu Sreelatha M.Pharm., (Ph.D) Assistant Professor CMR College of Pharmacy, Hyderabad. email: [email protected]

2. BIOSTATISTICS Also known as “ BIOMETRY”. In Greek Bio means LIFE and Metron means MEASURED, so it is measurement of life. It is the branch of statistics that deals with data related to living organisms. (OR) It is a branch of applied statistics applied in many areas of biology including Epidemiology, Medical Sciences, Health Science, Pharmaceutical Sciences and Environmental sciences, Vital Statistics. (OR) Biostatics as concerned with statistical methods for Collecting, Organising, Summarising, Presenting and analysing data of biological importance with the objective of drawing conclusions and facilitating decision making.

3. In public health/ community health it is called Health Statistics. In medicines it is called Medical Statistics. In this we study the defect, injury, disease, efficacy of drug, serum and line of treatment etc. In population related study it is called vital statistics. Ex- Study of vital events like Births, Marriages and deaths. Biostatics has an important role in both designing a pharmaceutical experiment and evaluating its results. BIOSTATISTICS IN PHARMA HEALTH STATISTICS Biostatistics in pharma will be focusing primarily on analysing clinical trial data. This could be during any phase of a clinical trial and could analyse a range of data from age to adverse events. Some may be responsible for monitoring the methodology of experiments or even designing the experimental plans. “Biostatistics is central to all of science because science need that gathering of evidence and the evaluation of that evidence to make a judgement.

4. VARIABLES AND VARIATION According to Sanford- It is something that is needed to be measured. Variables are the measurements, The values which are characteristics of the data collected in experiments This is usually displayed, analysed and interpreted in a research report/ publication.(These observations are called “ Random Variables” . Random variables depend on random events. Eg- Roll of a dice. TYPES OF VARIABLES

5. VARIATIONS Variation is an inherent characteristics of experimental observations. Special experimental design and analysis are required to isolate and identify particular causes of variability. Eg: Duplicate observations/determinations of serum concentration of a drug one hour after an injection will not be identical no matter if the duplicate comes from The same blood sample. From separate samples from 2 different persons. From same person on 2 different occasions. Variations in observations is due to a number of causes. Eg: An assay will vary depending on The instrument used for the analysis. The analyst performing the assay. The particular sample chosen. Unidentified, uncontrollable, background error, commonly known as “NOISE”.

6. 2 nd CLASSIFICATION

7. Most commonly encountered variables are called Continuous Variables. Eg : Weighing tablets, making BP measurements. Limiting factor for total number of Observations/results is the sensitivity of measuring instrument. In first case, if the balance is sensitive only to the nearest Mg, the data will appear as discrete values. Continuous variable can take on any value within some range(With in specified lower and upper limit). CONTINUOUS VARIABLES For tablets targeted as 1g and weighed to the nearest mg, the tablet weight ranges from 900-1100mg, a total of 201 possible integral values (901,902,903….........1098,1099,1100). If the tablet weighed on a more sensitive balance to the nearest 0.1mg, values from 899.5 to 1100.4 might be possible, a total of 2010 possible values and so on. Few more examples; Height, weight, skull, circumference of a person. Length of a film is continuous variable. Temperature is a continuous variable. Time taken to run a race is a continuous variable.

8. DISCRETE VARIABLES These are commonly observed in biological and Ph. Experiments and are exampled by measurements such as No. of anginal episodes in one week (or) the No. of side effects of different kinds after drug treatment. These values can be numerically ordered according to their magnitude. Discrete data can be named (Nominal),Categorised into 2 (or) more classes and counted. These kinds of data are frequently observed in clinical and pharmaceutical experiments and processes. Eg: A finished tablet classified in QC as “defective” (or) “not defective” is an example of categorial (or) attribute type of variable. In clinical studies, the categorization of a patient by gender(male or female) (or) race is a classification according to attributes. When calculating ED50 (or) LD50 animals are categorised as “Responders” or “Non responders” to various levels of a therapeutic agent, a categorial response. These examples describe variables that cannot be ordered. Continuous variables can always be classified into discrete classes where the classes are ordered.

9. USES OF BIOSTATISTICS Hospital utility statistics Resource Allocation Vaccination Update Magnitudes of a disease/condition Assessing risk factors Knowing disease frequency Making diagnosis and choosing an appropriate treatment. Biostatistics has an important role in both designing a pharmaceutical experiment and evaluating its result. Biostatistics uses the application of statistical methods to conduct research in the areas of biology, public health, and medicine. Biostatisticians help design, manage and analyze cancer clinical trials. They also help identify the causes and characteristics of cancer. Oncologists rely on these numbers to recommend treatments for their cancer patients.