Introduction to Pharmaceutical Statistics PDF

Introduction to Pharmaceutical Statistics  Statistics Statistics is the science of collection, organization, presentation, analysis and interpretation of data. Statistics is a set of concepts, rules, and procedures that help us to: – organize numerical information i...

Introduction to Pharmaceutical Statistics  Statistics Statistics is the science of collection, organization, presentation, analysis and interpretation of data. Statistics is a set of concepts, rules, and procedures that help us to: – organize numerical information in the form of tables, graphs, and charts; – understand statistical techniques underlying decisions that affect our lives and well-being; and – make informed decisions.  Branches of Statistics Two branches of statistics: Descriptive statistics – Collecting, summarizing, and processing data to transform data into information Inferential statistics – provide the bases for predictions, forecasts, and estimates that are used to transform information into knowledge Both are integral to data analysis, with descriptive statistics giving the groundwork for understanding data, and inferential statistics allowing for broader conclusions based on that data. Descriptive Statistics Collect data – e.g., Survey Present data – e.g., Tables and graphs Summarize data – e.g., Sample mean =  Inferential Statistics Estimation – e.g., Estimate the population mean weight using the sample mean weight Hypothesis testing – e.g., Test the claim that the population mean weight is 120 pounds Inference is the process of drawing conclusions or making decisions about a population based on sample results  Population A population is a complete set of individuals, objects or measurements having some common characteristics. It includes all the elements of a set of data. N represents the population size  Sample A sample is a subset or part of the population selected to represent the population It consists of one or more observations from the population. n represents the sample size Example: Population: paracetamol tablet batch contains 100K tablets. Sample: 20 tablets randomly taken from each batch for test of dissolution.  Population vs. Sample Population Sample a b cd b c Ef ghi jkl m n gi n o pq rs t uv w o r u x y z y Values calculated using Values computed from population data are called sample data are called parameters statistics  Parameter vs. Statistic Definition: A parameter is a numerical value that describes a characteristic of an entire population. Since the population includes every possible member of a group, a parameter is a fixed value, although it is often unknown because it is impractical or impossible to measure the entire population. Example: In a pharmaceutical study, if researchers are interested in the average blood pressure reduction for all patients who take a certain drug, this average (mean) blood pressure reduction for the entire patient population would be a parameter. Symbols for parameters often include Greek letters. For example: Population mean: μ (mu) Population standard deviation: σ (sigma)  Parameter vs. Statistic Definition: A statistic is a numerical value that describes a characteristic of a sample (a subset of the population). Since the statistic is calculated from actual data collected from the sample, it can vary depending on the sample chosen. Statistics are used to estimate parameters. Example: If the researchers in the same study collect blood pressure data from a sample of 100 patients, the average blood pressure reduction in this sample would be a statistic. Symbols for statistics often include Latin letters. For example: Sample mean: x̄ (x-bar) Sample standard deviation: s Sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. The subset is meant to reflect the whole population and statisticians attempt to collect samples that are representative of the population. 11  Classification of Sampling The sampling procedures that are commonly used may be classified in to TWO categories: 1. Probability Sampling This is the method of selecting samples according to certain laws of probability in which each unit in the population has some definite probability of being selected in the sample. 2. Non-probability Sampling This is the method of selecting samples, in which the choice of selection of sampling units depends entirely on the judgment of the sampler. 12 Types of Non-Probability Sampling 1. Convenient (or Convenience) Sampling 2. Quota Sampling 3. Judgment Sampling 4. Snowball Sampling 13  Convenient Sampling Selecting easily accessible participants with no randomization. For example: One of the most common examples of convenience sampling is using student volunteers as subjects for the research. Subjects that are selected from a clinic, a class or an institution that is easily accessible to the researcher. Five people from a class or choosing the first five names from the list of patients.  Quota Sampling Quota sampling refers to selection with controls, ensuring that specified numbers (quotas) are obtained from each specified population subgroup. It may be households or persons classified by relevant characteristics, but with essentially no randomization of unit selection within the subgroups. For example, you include exactly 50 males and 50 females in a sample of 100. Judgment/Purposive Sampling A purposive sample refers to selection of units based on personal judgement rather than randomization. Because, participants are selected based on certain predetermined characteristics, no randomization. 16 Snowball Sampling Selecting participants by finding one or two participants and then asking them to refer you to others. For example, meeting a homeless person, interviewing that person, and then asking him/her to introduce you to other homeless people you might interview. 17 Types of Probability Sampling ▪Simple random sampling ▪Systematic sampling ▪Stratified sampling ▪Cluster sampling ▪Multi-stage sampling 19  Simple Random Sampling Random Sampling – Selected by using chance or random numbers – Each individual subject (human or otherwise) has an equal chance of being selected – Random samples are used to avoid bias and other unwanted effects. – A sample that represents the population. 20 Systematic Sampling Systematic Sampling – Select a random starting point and then select every kth subject in the population – Simple to use so it is used often 21 Stratified Sampling Stratified Sampling ⚫ Divide the population into at least two different groups with common characteristic(s), then draw SOME subjects from each group (group is called strata or stratum) ⚫ Results in a more representative sample 22 Cluster Sampling ⚫ Cluster Sampling ⚫ Divide the population into groups (called clusters), randomly select some of the groups, and then collect data from ALL members of the selected groups ⚫ Used extensively by government and private research organizations 1. Descriptive statistics can be used to make predictions about a population based on sample data. 2. A parameter is a numerical value that describes a characteristic of a sample, while a statistic describes a characteristic of an entire population. 3. In cluster sampling, the population is divided into groups, and all members of selected groups are surveyed. 4. A random sample guarantees that every individual in the population has an equal chance of being selected. 5. Inferential statistics are primarily concerned with summarizing and presenting data. 6. Stratified sampling is a method used to ensure that subgroups within a population are represented in the sample. 7. A statistic is a fixed value, while a parameter can vary depending on the sample chosen.  Sample Surveys The total count of all units of the population for a certain characteristic is known as complete enumeration, also termed as census survey. In statistics, survey sampling describes the process of selecting a sample of elements from a target population to conduct a survey. 25 Advantages of sample surveys over census surveys ▪ Get information about large populations ▪ Less costs ▪ Less field time ▪ More accuracy i.e. Can Do A Better Job of Data Collection ▪ When it’s impossible to study the whole population 26 Principle Steps of a Sample Survey 1. Setting the study objectives What are the objectives of the study? What information/data need to be collected? 2. Defining the study population Sampling frame 3. Decide sample design 4. Questionnaire design Appropriateness, acceptability, culturally appropriate, understandable. 5. Fieldwork Training/Supervision Quality monitoring Timing: seasonality Principle Steps of a Sample Survey 6. Quality assurance Every steps Minimizing errors/bias/cheating 7. Data entry/compilation Validation Feedback 8. Analysis 9. Dissemination 10. Plans for next survey: what did you learn, what did you miss?  Pilot Survey/Study A pilot survey/study/experiment is a small-scale preliminary study conducted in order to evaluate feasibility (conveniently done), time, cost, adverse events, and effect size (statistical variability) for predicting an appropriate sample size and improving the study design prior to perform a full-scale research project.  Biostatistics Application of statistical technique to scientific research in health-related fields, including, medicine, pharmacy, biology, epidemiology and public health and the development of new tools to study these area.  Application of Biostatistics In medical science – Evaluate effectiveness of a new drug – Compare the action of different types of drugs – Find relation between disease and risk factor – Analyze symptom and disease – Define range or limit in biological parameters – Set a range in different lab test  Application of Biostatistics In Epidemiology and Public Health – Role of risk factors are statistically tested e.g. smocking causes cancer, deficiencies of Iodine cause goiter. – Find out the emergence of infectious disease e.g. severity of AIDS in human, evaluation of psychological health etc. – For social welfare. e.g. family planning – Find out the total social health condition.  Application of Biostatistics In pharmaceutical science – In quality control of Pharmaceutical products – In validation process – To design experiment for study – To evaluate the efficacy of the process – To check the productivity of the equipments – In planning area