statistics in pharmaceutical sciences
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This document outlines the key topics and concepts covered in a course on statistics in pharmaceutical sciences. It includes definitions of important statistical terms like population, sample, parameter, statistic, variables, and study types. It also describes levels of measurement, the basics of control groups and placebos in studies, and provides an overview of the steps involved in planning a statistical study.
statistics in pharmaceutical sciences
- 1. 1 Statistics in Pharmaceutical Sciences PHR 112
- 2. 2 • Statistics and its application • Variables and Attributes • Classification and tabulation of data • Populations and samples • Frequency distributions • Graphical presentation of data • Describing and summarizing data: statistical averages & measures of dispersion. • Probability and probability distributions. • Hypothesis testing: concepts, types, p-value. • Test of significance: Parametric tests (t-test, One way ANOVA, multiple comparison tests (Bonferoni, Duncan, Dunnet, Tukey), repeated measure ANOVA; • Non-parametric tests: Mann-whiteney, Wilcoxon rank test, Kruskal-Wallis test, multiple comparison tests (Tukey), Friedman's test. Regression (simple linear & nonparametric regression) and correlation (simple & rank correlation), Chi- square & odds ratio. Course Contents
- 3. 3 Course Plan Topic and Test No. of Lectures Dates - Statistics and its application, Variables and Attributes, Classification and tabulation of data, Populations and samples 5 Test 1 - Frequency distributions, Graphical presentation of data, Describing and summarizing data: statistical averages & measures of dispersion 5 Test 2 - Hypothesis testing: concepts, types, p-value, t-test 2 Test 3 Mid Term Examination - One way ANOVA, multiple comparison tests (Bonferoni, Duncan, Dunnet, Tukey), repeated measure ANOVA 5 Test 4 - Non-parametric tests: Mann-Whitney, Wilcoxon rank test, Kruskal- Wallis test, multiple comparison tests (Tukey), Friedman's test. Regression (simple linear & nonparametric regression) and correlation (simple & rank correlation), Chi-square & odds ratio 5 Test 5 Presentation 2 Final Examination
- 4. 4 Statistics and its application
- 5. 5 1.1 What is Statistics? Statistics is a scientific study of numerical data based on natural phenomena. It is also the science of collecting, organizing, interpreting and reporting data.
- 6. 6 Evaluate the activity of a drug; e.g.; effect of caffeine on attention; compare the analgesic effect of a plant extract and NSAID To explore whether the changes produced by the drug are due to the action of drug or by chance To compare the action of two or more different drugs or different dosages of the same drug are studied using statistical methods. To find an association between disease and risk factors such as Coronary artery disease and smoking Pharmaceutical statistics is the application of statistics to matters concerning the pharmaceutical industry. This can be from issues of design of experiments, to analysis of drug trials, to issues of commercialization of a medicine. Statistics and its application
- 7. 7 Public health, including epidemiology, health services research, nutrition, environmental health and healthcare policy & management. Design and analysis of clinical trials in medicine Population genetics, and statistical genetics in order to link variation in genotype with a variation in phenotype. In biomedical research, this work can assist in finding candidates for gene alleles that can cause or influence predisposition to disease in human genetics Analysis of genomics data. Example: from microarray or proteomics experiments. Often concerning diseases or disease stages. Systems biology for gene network inference or pathways analysis Demographic studies: Age, gender, height, weight, BMI Epidemiology: deficiency of iron in anemia, iodized salt and goiter, hygiene and microbial disease Statistics and its application
- 8. 8 Two Meanings Specific numbers Method of analysis Statistics
- 9. 9 Specific number numerical measurement determined by a set of data Example: 65% student use facebook account Statistics
- 10. 10 Statistics Method of analysis A collection of methods for planning experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data
- 11. 11 Population the complete collection of all elements (scores, people, measurements, and so on) to be studied. The collection is complete in the sense that it includes all subjects to be studied. Population refers to all members of a defined group Definition
- 12. 12 Census the collection of data from every element in a population Sample a subcollection of elements drawn from a population • Example; patients in a hospital would constitute the entire population for a study of infection control in that hospital. However, for a study of infected patients in the nation’s hospitals, the same group of patients would be but a tiny sample. The same group can be a sample for one question about its characteristics and a population for another question. Definition
- 13. 13 Parameter a numerical measurement describing some characteristic of a population Definition
- 14. 14 Parameter a numerical measurement describing some characteristic of a population population parameter Definition
- 15. 15 Statistic a numerical measurement describing some characteristic of a sample sample statistic Definition
- 16. 16 Quantitative data numbers representing counts or measurements Qualitative (or categorical or attribute) data can be separated into different categories that are distinguished by some nonnumeric characteristics Definition
- 17. 17 Quantitative data Cholesterol level in the blood Qualitative (categorical or attribute) data The genders (male/female) of college graduates Definition
- 18. 18 Discrete data result when the number of possible values is either a finite number or a ‘countable’ number of possible values 0, 1, 2, 3, . . . Continuous (numerical) data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps 2 3 Definition
- 19. 19 Discrete The number of eggs that hens lay; for example, 3 eggs a day. Continuous The amounts of milk that cows produce; for example, 2.343115 gallons a day. Definition
- 20. 20 nominal level of measurement characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as low to high) Example: survey responses yes, no, undecided Definition
- 21. 21 ordinal level of measurement involves data that may be arranged in some order, but differences between data values either cannot be determined or are meaningless Example: Course grades A, B, C, D, or F Definition
- 22. 22 interval level of measurement like the ordinal level, with the additional property that the difference between any two data values is meaningful. However, there is no natural zero starting point (where none of the quantity is present) Example: Years 1000, 2000, 1776, and 1492 Definition
- 23. 23 ratio level of measurement the interval level modified to include the natural zero starting point (where zero indicates that none of the quantity is present). For values at this level, differences and ratios are meaningful. Example: Prices of college textbooks Definition
- 24. 24 Nominal - categories only Ordinal - categories with some order Interval - differences but no natural starting point Ratio - differences and a natural starting point Levels of measurement
- 25. 25 Variables A variable is just a term for an observation or reading giving information on the study question to be answered. Blood pressure is a variable giving information on hypertension. Blood uric acid level is a variable giving information on gout. Independent variable is a variable that, for the purposes of the study question to be answered, occurs independently of the effects being studied. A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated. Dependent variable is a variable that depends on, or more exactly is influenced by, the independent variable. A variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome.
- 26. 26 Variables In a study on gout, suppose we ask if blood uric acid (level) is a factor in causing pain. We record blood uric acid level as a measurable variable that occurs in the patient. Then we record pain as reported by the patient. We believe blood uric acid level is predictive of pain. In this relationship, the blood uric acid is the independent variable and pain is the dependent variable.
- 27. 27 Control Groups and Placebos A frequent mechanism to pinpoint the effect of a treatment and to reduce bias is to provide a control group having all the characteristics of the experimental group except the treatment under study. Example: Paracetamol tablet (drug group) and lactose tablet (placebo); then compare their effect on fever reducing property Basics
- 28. 28 Case-Control Study A case-control study is a study in which an experimental group of patients is chosen for being characterized by some outcome factor, such as having acquired a disease, and a control group lacking this factor is matched patient for patient. STUDY TYPES Cohort Study A cohort study starts by choosing groups that have already been assigned to study categories, such as diseases or treatments, and follows these groups forward in time to assess the outcomes.
- 29. 29 Randomized Controlled Trial The soundest type of study is the randomized controlled trial (RCT), often called a clinical trial. An RCT is a true experiment in which patients are assigned randomly to a study category, such as clinical treatment, and are then followed forward in time (making it a prospective study) and the outcome is assessed. STUDY TYPES Paired and Crossover Designs Some studies permit a design in which the patients serve as their own controls, as in a “before-and-after” study or a comparison of two treatments in which the patient receives both in sequence.
- 30. 30 STEPS THAT WILL AID IN PLANNING A STUDY 1. Start with objectives. Specify, clearly, unequivocally, a question to be answered about an explicitly defined population. 2. Develop the background and relevance. 3. Plan your materials. From where will you obtain your equipment? 4. Plan your methods and data. Identify at least 1 measurable variable capable of answering your question. Define the specific data that will satisfy your objectives and verify that your methods will provide these data. Develop clearly specified null and alternate hypotheses. 5. Plan data recording. Develop a raw data entry sheet and a spreadsheet to transfer the raw data to that will facilitate analysis by computer software. 6. Define the subject population, verify that your sampling procedures will sample representatively. 7. Ensure that your sample size will satisfy your objectives. 8. Anticipate what statistical analysis will yield results that will satisfy your objectives. 9. Plan tests for sampling bias. 10. Plan the bridge from results to conclusions. 11. Anticipate the form in which your conclusions will be expressed 12. Now you can draft an abstract.
Editor's Notes
- #11: page 4 of text
- #13: Emphasize that a population is determined by the researcher, and a sample is a subcollection of that pre-determined group. For example, if I collect the ages from a section of elementary statistics students, that data would be a sample if I am interested in studying ages of all elementary statistics students. However, if I am studying only the ages of the specific section of elementary statistics, the data would be a population.
- #14: page 5 of text
- #19: Understanding the difference between discrete versus continuous data will be important in Chapters 4 and 5. When measuring data that is continuous, the result will be only as precise as the measuring device being used to measure.
- #21: page 7 of text
- #22: Understanding the differences between the levels of data will help students later in determining what type of statistical tests to use. Nominal and ordinal data should not be used for calculations (even when assigned ‘numbers’ for computerization) as differences and magnitudes of differences are meaningless.
- #23: Students usually have some difficulty understanding the difference between interval and ratio data. Fortunately, interval data occurs in very few instances.
- #25: review of four levels of measurement