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Complex Experimental Design and Simple Data Analysis: A Pharmaceutical Example Joseph G Pigeon Villanova University.

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Presentation on theme: "Complex Experimental Design and Simple Data Analysis: A Pharmaceutical Example Joseph G Pigeon Villanova University."— Presentation transcript:

1 Complex Experimental Design and Simple Data Analysis: A Pharmaceutical Example Joseph G Pigeon Villanova University

2 Introduction Designs with restricted randomization have multiple error measures Pharmaceutical example where the split plot structure is even more complex –Whole plot structure in two dimensions –Correlation structure in two dimensions Caveats –Limited understanding of the biology involved –No originality of statistical methods claimed

3 Split Plot Designs Originated in agricultural experiments where –Levels of some factors are applied to whole plots –Levels of other factors are applied to sub plots Separate randomizations to whole plots and sub plots –Two types of experimental units –Two types of error measures –Correlation among the observations

4 Split Plot Designs Also common in industrial experiments when –Complete randomization does not occur –Some factor levels may be impractical, inconvenient or too costly to change This restriction on randomization results in some whole plot factors and some sub plot factors Data analysis needs to account for this restricted randomization or split plot structure

5 Split Plot Example Consider a paper manufacturer who wants to study –Effects of 3 pulp preparation methods –Effects of 4 temperatures –Response is tensile strength Pilot plant is capable of 12 runs per day One replicate on each of three days

6 Split Plot Example

7 Initially, we might consider this to be a 4 x 3 factorial in a randomized block design If true, then the order of experimentation within a block should have been completely randomized However, this was not feasible; data were not collected this way

8 Split Plot Example Experiment was conducted as follows: –A batch of pulp was produced by one of the three methods –The batch was divided into four samples –Each sample was cooked at one of the four temperatures Split plot design with –Pulp preparation method as whole plot treatment –Temperature as sub (split) plot treatment

9 Split Plot Example

10 Subplot error is less than whole plot error (typical)

11 Split Plot Example Lessons We must carefully consider how the data were collected and incorporate all randomization restrictions into the analysis –Whole plot effects measured against whole plot error –Sub plot effects measured against sub plot error

12 Description of Example – MQPA Assay Multivalent Q-PCR based Potency Assay Used to assign potencies (independently) to each of five reassortants of a pentavalent vaccine Relies on the quantitation of viral nucleic acid generated in 24 hours Two major components –Biological component (infection of the standard and sample viruses) –Biochemical component (quantitative PCR reaction where PCR = Polymerase Chain Reaction)

13 Polymerase Chain Reaction (PCR)

14 Description of Example- Biological Component Vero cell maintenance and set up Serial dilution of known standard and unknown sample are incubated with trypsin Infected in 4 replicate wells of Vero cell monolayers seeded in a 96 well plate Infection proceeds for 24 hours and then halted with the addition of a detergent and storage at –70C

15 Description of Example- Biochemical Component Lysate is thawed and diluted Preparation of a “master mix” Preparation of Q-PCR plate (master mix + diluted lysates) Configuration of the Q-PCR detection system Potency is determined by parallel line analysis of standard and test samples Specific interest is on optimization of the PCR portion of the assay

16 PCR Optimization Design Discussions with Biologists identified 13 factors –8 factors associated with preparation of master mix –5 factors associated with configuration of PCR detection system (instrument) Discussions with Biologists identified 3 responses –Lowest cycle time (range: 1 – 40) –Least variability between replicates –Valid amplification plot (range: 0 – 4) Completion of experiments and analysis immediately!

17 PCR Optimization Design

18 PCR Optimization Design Considerations Interactions not expected to exist Experiments performed in a 96 well plate Each plate can accommodate at most 15 master mix combinations –12 run PB deign for 8 factors

19 PCR Optimization Design Considerations Time constraints imply at most 16 plates (instrument settings) –2 5-1 fractional factorial for 5 factors (5 = 1234) Concern about using only 12 of 2 8 combinations –Half of the plates use a 12 run PB design (123 = 45 = +1) –Half of the plates use the foldover PB design (123 = 45 =  1)

20 Plackett-Burman Design Factors: 8 Replicates: 1 Design: 12 Runs: 12 Center pts (total): 0 Data Matrix (randomized) Run A B C D E F G H

21 Half Fraction Design Factors: 5 Base Design: 5, 16 Resolution: V Runs: 16 Replicates: 1 Fraction: 1/2 Blocks: none Center pts (total): 0 Design Generators: E = ABCD Row StdOrder RunOrder A B C D E

22 PCR Optimization Design Layout Each  represents a 12 run PB design 16 × 12 = 192 observations

23 PCR Optimization Design Layout Master Mix 12   XXXX 2XXXX Plate  15XXXX 16XXXX

24 PCR Optimization Design Layout Master Mix 12   XXXX 2XXXX Plate  15XXXX 16XXXX Whole plot structure in two dimensions

25 PCR Optimization Results Biologists provided this summary of the 21 runs with an amplification plot rating of 4

26 PCR Optimization Results plate Count mm Count mm1 Count mm2 Count mm3 Count mm4 Count N= 21 N= 21 N= 21 N= N= N= 21 mm5 Count mm6 Count mm7 Count mm8 Count instr1 Count N= N= 21 N= 21 N= 21 N= 21 instr2 Count instr3 Count instr4 Count instr5 Count N= 21 N= 21 N= 21 N= 21

27 PCR Optimization Analysis Log mm7 = 1; instr4 = –1

28 PCR Optimization Results plate Count mm Count mm1 Count mm2 Count mm3 Count mm4 Count N= 63 N= 63 N= 63 N= N= 63 N= 63 mm5 Count mm6 Count mm7 Count mm8 Count instr1 Count N= 63 N= 63 N= 63 N= 63 N= 63 instr2 Count instr3 Count instr4 Count instr5 Count N= 63 N= 63 N= 63 N= 63

29 PCR Optimization Analysis Log mm7 = 1; instr4 = –1 mm3 = 1; mm7 = 1; mm8 = –1; instr3 = 1

30 PCR Optimization Results Fractional Factorial Fit: ctgm Estimated Effects and Coefficients for ctgm (coded units) Term Effect Coef SE Coef T P Constant instr instr instr instr instr instr1*instr instr1*instr instr1*instr instr1*instr instr2*instr instr2*instr instr2*instr instr3*instr instr3*instr instr4*instr

31 PCR Optimization Results

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35 PCR Optimization Analysis Log mm7 = 1; instr4 = -1 mm3 = 1; mm7 = 1; mm8 = -1; instr3 = 1 Instr3 = 1; instr2 and instr5 should have opposite signs?

36 PCR Optimization Results

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38 Estimated Effects and Coefficients for ctgm (coded units) Term Effect Coef SE Coef T P Constant mm mm mm mm mm mm mm mm

39 PCR Optimization Results

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42 PCR Optimization Analysis Log mm7 = 1; instr4 = – 1 mm3 = 1; mm7 = 1; mm8 = –1; instr3 = 1 instr3 = 1; instr2 and instr5 should have opposite signs? mm3 = 1; mm7 = 1; mm8 = –1

43 PCR Optimization Results Row plate mm ct1 ct2 ct3 ct4 ctgm well1 well Row amprating mm1 mm2 mm3 mm4 mm5 mm6 mm7 mm8 instr1 instr Row instr3 instr4 instr

44 PCR Optimization Results

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46 PCR Optimization Summary No complex models – all simple analyses 5 factors were found to be significant (mm3, mm7, mm8, instr3 and instr4) These factors were further studied using response surface experiments Scientists seem quite happy with the results of the PCR optimization experiments

47 Concluding Remarks Many industrial experiments do have a split or strip plot structure which means multiple and possibly complex error measures Arises from the conduct of an experiment and/or any restrictions on the randomization We need to incorporate these considerations into a proper analysis and interpretation of experimental data

48 Concluding Remarks Experimental designs with balance, symmetry and orthogonality permit simple but effective graphical analyses (even with some missing data) Much can be learned from simple analyses following suitable experimental design –All models are wrong, but some models are useful –All models are wrong, but some models are more wrong than others


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