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Research Methodology

General principles of research methodology. Terms frequently used in this chapter. It is a course subject for fourth Pharm D in The Tamilnadu Dr.MGR. Medical University, Chennai.

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Research Methodology

  1. 1. RESEARCH METHODOLOGY By Gladys Stephen M.Pharm
  2. 2. •Research refers to a search for knowledge, a scientific and systematic search for pertinent information on a specific topic. • Research is an art of scientific investigation or inquiry specially through search for new facts in any branch of knowledge. By Gladys Stephen M.Pharm
  3. 3. OBJECTIVES OF RESEARCH •To discover answers to questions through the application of scientific procedures. •1. To gain familiarity with a phenomenon or to achieve new insights into it. •2. To portray accurately the characteristics of a particular individual, situation or a group •3. To determine the frequency with which something occurs or with which it is associated with something else. •4. To test a hypothesis of a causal relationship between variables (such studies are known as hypothesis-testing research studies). By Gladys Stephen M.Pharm
  4. 4. • For doing research, statistics plays a major role. Many of the terms that comprise statistical nomenclature that are familiar. • Specifically, such terms are • Variables and variations • discrete and continuous variables, • frequency distribution, • population, • sample, • mean, median, standard deviation, • variance, coefficient of variation (CV), range, accuracy,and precision By Gladys Stephen M.Pharm
  5. 5. VARIABLES •Variables are the measurements, the values, which are characteristic of the data collected in experiments. •These are the data that will usually be displayed, analyzed, and interpreted in a research report or publication. In statistical terms, these observations are more correctly known as random variables. • Random variables take on values, or numbers, according to some corresponding probability function. •Duplicate determinations of serum concentration of a drug 1 hr after an injection will not be identical no matter if the duplicates come from (a) the same blood sample or (b) from separate samples from two different persons or (c) from the same person on two different occasions. By Gladys Stephen M.Pharm
  6. 6. Continuous Variables •A continuous variable is one that can take on any value within some range or interval (i.e., within a specified lower and upper limit). •The limiting factor for the total number of possible observations or results is the sensitivity of the measuring instrument. •When weighing tablets or making blood pressure measurements, there are an infinite number of possible values that can be observed if the measurement could be made to an unlimited number of decimal places. •Often, continuous variables cannot be easily measured but can be ranked in order of magnitude. By Gladys Stephen M.Pharm
  7. 7. Discrete Variables •discrete variables can take on a countable number of values. •These kinds of variables are commonly observed in biological and pharmaceutical experiments and are exemplified by measurements such as the number of anginal episodes in 1 week or the number of side effects of different kinds after drug treatment. •Although not continuous, discrete data often have values associated with them which can be numerically ordered according to their magnitude. E.g rating scale. •Discrete data that can be named (nominal), categorized into two or more classes, and counted are called categorical variables, or attributes; for example, the attributes may be different side effects resulting from different drug treatments or the presence or absence of a defect in a finished product. By Gladys Stephen M.Pharm
  8. 8. Continuous Variables •Continuous variables can always be classified into discrete classes where the classes are ordered. •For example, patients can be categorized as ‘‘underweight,’’ ‘‘normal weight,’’ or ‘‘overweight’’ based on criteria such as those listed in Metropolitan Life Insurance tables of ‘‘Desirable Weights for Men and Women’’. •Thus data can be classified as: •1. Continuous (blood pressure, weight) •2. Discrete (number of anginal episodes per week) •3. Categorigical variable: categorical, ordered (degree of overweight) •4. Attributes: categorical, not ordered (male or female) By Gladys Stephen M.Pharm
  9. 9. VARIATIONS •Variation is an inherent characteristic of experimental observations. To isolate and to identify particular causes of variability requires special experimental designs and analysis. •Variation in observations is due to a number of causes. For example, an assay will vary depending on: •1. The instrument used for the analysis •2. The analyst performing the assay •3. The particular sample chosen •4. Unidentified, uncontrollable background error, commonly known as ‘‘noise’’ By Gladys Stephen M.Pharm
  10. 10. By Gladys Stephen M.Pharm
  11. 11. RATING SCALE •In the assessment of pain in a clinical study of analgesics, a patient can have a continuum of pain. To measure pain on a continuous numerical scale would be difficult. On the other hand, a patient may be able to differentiate slight pain from moderate pain, moderate pain from severe pain, and so on. •In analgesic studies, scores are commonly assigned to pain severity, such as no pain 0, slight pain 1, moderate pain 2, and severe pain 3. •The scoring system above is a representation of a continuous variable by discrete ‘‘scores’’ which can be rationally ordered or ranked from low to high. This is commonly known as a rating scale, and the ranked data are on an ordinal scale. •The rating scale is an effort to quantify a continuous, but subjective, variable. By Gladys Stephen M.Pharm
  12. 12. Frequency Distributions •The frequency distribution is an example of a data summary, a table or categorization of the frequency of occurrence of variables in various class intervals. •A frequency distribution of a set of data is simply called a ‘‘distribution.’’ •For a sampling of continuous data, a frequency distribution is constructed by classifying the observations (variables) into a number of discrete intervals. By Gladys Stephen M.Pharm
  13. 13. •For categorical data, a frequency distribution is simply a listing of the number of observations in each class or category, such as 20 males and 30 females entered in a clinical study. By Gladys Stephen M.Pharm
  14. 14. SAMPLE •Samples are usually a relatively small number of observations taken from a relatively large population or universe. •The sample values are the observations, the data, obtained from the population. •The sample could consist of a selection of patients to participate in a clinical study, or tablets chosen for a weight determination. •The sample is only part of the available data. •Observations on a relatively small sample is done in order to make inferences about the characteristics of the whole, the population. By Gladys Stephen M.Pharm
  15. 15. POPULATION •The population consists of data with some clearly defined characteristic(s). E.g. a population may consist of all patients with a particular disease, or tablets from a production batch. •The total available data is the population or universe. When designing an experiment, the population should be clearly defined so that samples chosen are representative of the population. •This is important in clinical trials, for example, where inferences to the treatment of disease states are crucial. •The exact nature or character of the population is rarely known, and often impossible to ascertain, although we can make assumptions about its properties. By Gladys Stephen M.Pharm
  16. 16. Population Sample Tablet batch Twenty tablets taken for content uniformity Normal males between ages 18 and 65 years 24 subjects selected for a phase I clinical study Sprague – Dawley weaning rats 100 rats selected to test possible toxic effects of a NDC Analysts working for company X Three analysts from a company to test a new assay method Persons with diastolic blood pressure between 105 and 120 mmHg in the USA 120 patients with diastolic pressure between 105 and 120 mmHg to enter clinical study to compare two antihypertensive agents Serum cholesterol levels of one patient Blood samples drawn once a week for 3 months from a single patient By Gladys Stephen M.Pharm
  17. 17. Population parameters •Any measurable characteristic of the universe is called a parameter. •E.g. the average weight of a batch of tablets or the average blood pressure of hypertensive persons in the Tamilnadu are parameters of the respective populations. •Parameters are characteristic of the population, and are values that are usually unknown to us. By Gladys Stephen M.Pharm
  18. 18. Precision •precision refers to the extent of variability of a group of measurements observed under similar experimental conditions. •A precise set of measurements is compact Accuracy •Accuracy refers to the closeness of an individual observation or mean to the true value. •The ‘‘true’’ value is that result which would be observed in the absence of error. •E.g., the true mean tablet potency or the true drug content of a preparation being assayed. By Gladys Stephen M.Pharm
  19. 19. Bias •Any deviation of results or inferences from the truth, or processes leading to such deviation. •Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. •There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions. By Gladys Stephen M.Pharm
  20. 20. • Accuracy can also be associated with the term bias. • The meaning of bias in statistics is similar to the everyday definition in terms of ‘‘fairness.’’ • An accurate measurement, no matter what the precision, can be thought of as unbiased, because an accurate measurement is a ‘‘fair’’ estimate of the true result. • A biased estimate is systematically either higher or lower than the true value. By Gladys Stephen M.Pharm

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