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UNIT 5.pptx
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- 2. 2 Problem statement,research questions, purposes, benefits Theory, assumptions, background literature Variables and hypotheses Operational definitions and measurement Research design and methodology Instrumentation, sampling Data analysis Conclusions, interpretations, recommendations
- 3. A sample is“a smaller (but hopefully representative) collection of units from a population used to determine truths about that population” (Field, 2005) Why sample? Resources (time, money) and workload Gives results with known accuracy that can be calculated mathematically The sampling frame is the list from which the potential respondents are drawn Registrar’s office Class rosters Must assess sampling frame errors 5
- 4. What isyour population of interest? To whom do you want to generalize your results? All doctors School children Indians Women aged 15-45 years Other Can you sample the entire population? 6
- 5. 3 factors thatinfluence sample representative- ness Sampling procedure Sample size Participation (response) When might you sample the entire population? When your population is very small When you have extensive resources When you don’t expect a very high response 7
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- 8. Probability (Random) Samples Simple random sample Systematic random sample Stratified random sample Multistage sample Multiphase sample Cluster sample Non-Probability Samples Convenience sample Purposive sample Quota 10
- 9. 9 The sampling processcomprises several stages: Defining the population of concern Specifying a sampling frame, a set of items or events possible to measure Specifying a sampling method for selecting items or events from the frame Determining the sample size Implementing the sampling plan Sampling and data collecting Reviewing the sampling process
- 10. 10 A population canbe defined as including all people or items with the characteristic one wishes to understand. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population.
- 11. 11 A probability samplingscheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. . When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight.
- 12. 12 Probability sampling includes: SimpleRandom Sampling, Systematic Sampling, Stratified Random Sampling, Cluster Sampling Multistage Sampling. Multiphase sampling
- 13. 13 Any sampling methodwhere some elements of population have no chance of selection (these are sometimes referred to as 'out of coverage'/'undercovered'), or where the probability of selection can't be accurately determined. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements is nonrandom, nonprobability sampling not allows the estimation of sampling errors.. Example: We visit every household in a given street, and interview the first person to answer the door. In any household with more than one occupant, this is a nonprobability sample, because some people are more likely to answer the door (e.g. an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls) and it's not practical to calculate these probabilities.
- 14. • 14 Nonprobability Sampling includes: AccidentalSampling, Quota Sampling and Purposive Sampling. In addition, nonresponse effects may turn any probability design into a nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled.
- 15. • Applicable when populationis small, homogeneous & readily available • • • All subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection. It provides for greatest number of possible samples. This is done by assigning a number to each unit in the sampling frame. A table of random number or lottery system is used to determine which units are to be selected. 19
- 16. 16 Estimates are easyto calculate. Simple random sampling is always an EPS design, but not all EPS designs are simple random sampling. Disadvantages If sampling frame large, this method impracticable. Minority subgroups of interest in population may not be present in sample in sufficient numbers for study.
- 17. Optimization Concept: Theterm Optimize is defined as to make perfect , effective , or as functional as possible. It is the process of finding the best way of using the existing resources while taking in to the account of all the factors that influences decisions in any experiment Traditionally, optimization in pharmaceuticals refer to changing one variable at a time, so to obtain solution of a problematic formulation. Modern pharmaceutical optimization involves systematic design of experiments (DoE) to improve formulation irregularities. 17
- 18. ⚫Optimization is usedin pharmacy relative to formulation and processing . ⚫It is the process of finding the best way of using the existing resources while taking in to the account of all the factors that influences decisions in any experiment. ⚫Final product not only meets the requirements from the bioavailability but also from the practical mass production criteria. . In development projects , one generally experiments by a series of logical steps, carefully controlling the variables & changing one at a time, until a satisfactory system is obtained 18
- 19. ⚫Target processing parameters– ranges for each excipients & processing factors . Questions optimization requires: • How we can make Formulation perfect ? ⚫What should be characteristics? ⚫ What should be the conditions?
- 20. Why is Optimizationnecessary? OPTIMIZATION Safety & Reducing error Reproducib ility 20 Save Time Primary objective may not be to optimize absolutely but to compromise effectively & thereby produce the best formulation under a given set of restrictions . Reducing cost
- 21. Formulation and Processing ClinicalChemistry Medicinal Chemistry High Performance Liquid Chromatographic Analysis Formulation of Culture Medium in Virological Studies. Study of Pharmacokinetic Parameters. APPLICATIONS: 21
- 22. Terms Used • 22 • o FACTOR:It is an assigned variable such as concentration , Temperature etc.., Quantitative: Numerical factor assigned to it Ex- Concentration- 1%, 2%,3% etc. Qualitative: Which are not numerical Ex- Polymer grade, humidity condition etc. o LEVELS: Levels of a factor are the values or designations assigned to the factor. o RESPONSE: It is an outcome of the experiment. • It is the effect to evaluate. Ex- Disintegration time.
- 23. Terms Used o EFFECT:It is the change in response caused by varying the levels It gives the relationship between various factors & levels. o INTERACTION: It gives the overall effect of two or more variables Ex- Combined effect of lubricant and glidant on hardness of the tablet FACTOR LEVELS Temperature 300 , 500 Concentration 1%, 2% 23
- 24. Advantages o Yield the“Best Solution” within the domain of study. o Require fewer experiments to achieve an optimum formulation. o Can trace and rectify problem in a remarkably easier manner.
- 26. Softwares for Optimization ⚫Design Expert 7.1.3 ⚫ SYSTAT Sigma Stat 3.11 ⚫ CYTEL East 3.1 ⚫ Minitab ⚫ Matrex ⚫ Omega ⚫ Compact 21-Apr-15 O
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- 28. Problem Types Unconstrained • Inunconstrained optimization problems there are no restrictions. • For a given pharmaceutical system one might wish to make the hardest tablet possible. • The making of the hardest tablet is the unconstrained optimization problem. Constrained • The constrained problem involved in it, is to make the hardest tablet possible, but it must disintegrate in less than 15 minutes. 28
- 29. Variables • Independent variables: The independent variables are under the control of the formulator. These might include the compression force or the cavity filling or the mixing time. • Dependent variables : The dependent variables are the responses or the characteristics that are developed due to the independent variables. The more the variables that are present in the system the more the complications that are involved in the optimization.
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- 31. ⚫Once the relationshipbetween the variable and the response is known, it gives the response surface as represented in the Fig. 1. Surface is to be evaluated to get the independent variables, X1 and X2, which gave the response, Y.Any number of variables can be considered, it is impossible to represent graphically, but mathematically it can be evaluated. 31
- 33. Factorial Design (FD) ⚫Factorial experiment is an experiment whose design consist of two or more factor each with different possible values or “levels”. ⚫FD technique introduced by “Fisher” in 1926. ⚫Factorial design applied in optimization techniques. ⚫Factors : Factors can be “Quantitative” (numerical number) or they are qualitative. They may be names rather than numbers like Method 1, site B, or present or absent .
- 34. ⚫Factorial design dependson independent variables for development of new formulation . ⚫Factorial design also depends on Levels as well as Coding ⚫There are three types of levels : 1) LOW 2)INTERMEDIATE 3) HIGH ⚫Simultaneously CODING takes place for Levels : 1) for LOW = (-1) 2)For intermediate = (0) 3) for HIGH =(+1) 34
- 35. ⚫FD is forthe evaluation of multiple factors simultaneously. ⚫2 3 means 2 is level while 3 is factor . ⚫ Factorial Design is divided into two types 1. Full Factorial Design 2.Fractional factorial design 35
- 36. Response surface methodology,or RSM Collection of mathematical and statistical techniques Useful for the modeling and analysis of problems Response of interest is influenced by several variables The objective is to optimize the response. Temperature (x₁) and pressure (x₂) & yield (y) The levels of temperature (x₁) and pressure (x₂) maximize the yield (y) of a process The process yield is a function of the levels of temperature and pressure, say y = f (x₁, x₂) + 𝜖 For example Region of Interest Region of Operability