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Design and Analysis of Augmented Designs in Screening Trials

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Presentation on theme: "Design and Analysis of Augmented Designs in Screening Trials"— Presentation transcript:

1 Design and Analysis of Augmented Designs in Screening Trials
Kathleen Yeater USDA-ARS-SPA 3rd Curators Workshop February 3, 2010

2 5 Basic Steps of Experiment
1. Research Planning 2. Experimental Design 3. Summarize Observations 4. Analysis – Statistical Inference 5. Document / Present study results Why start with this? This is how the talk is constructed / organized.

3 Research Planning - What is the Question?
Is the focus on development? Are you trying to find something better? Is it discovery research? Not necessarily a specific hypothesis This is why the phrase “screening” is in the Title.

4 Remember the Basics, the 3 R’s ?
Replication Valid estimation of error variance Reduction of variation among plots Controls (reduces) error variance Use blocking to control heterogeneity present in experiment; Block at scale of variability Randomization Unbiased estimates of means and variances Minimum blocking –fewer blocks; blocking at scale of variability Cause and Effect

5 What if replication is ? Impractical, prohibitively expensive, impossible Not enough material (seed, -icides) Not enough space Not enough time Too many entries

6 What leads to unreplicated design in field trials?
3 R’s, making cause and effect statements In screening, making a cut based on good/bad in testing …What is the Research Question again? Design for the experiment under consideration; DO NOT experiment for design In principles of experimental design (3 Rs), trying to make cause and effect statements. Whereas in screening, we’re trying to make first cut diagnoses like in an Early Generation Variety Testing.

7 Augmented Design Introduced by various publications of W.T. Federer
Developed for plant breeding research Genotypes Yield Disease Insecticides All are excellent subject Herbicides variables in a Screening Fertilizers Trial With Screening Designs – focus is on development – trying to find something better – it is discovery research We have identified our Research Question in the Research Planning phase. Now we move on to step two, which is to identify an appropriate design to implement the experiment.

8 Augmented Design as Experimental Design
Utilizes experimental designs principles for arrangement of checks New treatments (n) are not replicated and checks are replicated as points of reference Usually want between 4-6 checks n can be large

9 Augmented Design - Implementation
I – Select any experiment design for the check(s) II – Enlarge the blocks or increase number of rows and/or columns to accommodate the new test entries (treatments, n) III – New test entries are randomly distributed among blocks/rows/columns

10 Design Set-up - RCB A B C D moisture gradient
Block effect now removes moisture effect, fair comparisons among treatments.

11 Design Set-up – Augmented RCB
14 19 C 12 D 23 5 20 13 8 C 18 A 4 24 D 16 B moisture gradient 1 9 22 D 11 C B 3 A 6 Now we’ve expanded the block out to include the unreplicated treatments. B 10 2 15 D A C 21 17 7

12 Advantages of Augmented Design
More than one check included 4 to 6 optimal Allows for estimate of experimental error and is efficient Less physical space needed

13 How to select a check Checks are units of experimentation [varieties/genotypes/cultivars/entries] with known ranges of various measurement characteristics that you want to evaluate What are good checks for your objective? Yield CHO content Seed characteristics A quantitative measurement that holds constant

14 Variability of Checks The test entries (n) range of measurement will be 5.0 – 40.0. The mean of the overall test entries ~ 12.6. What do you think about these checks? Did I do a good job of selecting appropriate checks?

15 Consistent Checks that cover the range of our test data.
The test entries (n) range of measurement will be 5.0 – 40.0. The mean of the overall test entries ~ 12.6.

16 Augmented RCBD Goal: Screen 300 new entries for response X. This is an Early Generation Screening Trial. 4 additional genotypes are CHECK entries (A, B, C, D) 6 Blocks (field plots, time placement for lab assay, location in growth chamber) Randomize location of A, B, C, D within each block (replicate the check genotypes within block for spatial variation) 300/6 = 50 new entries randomly selected and placed within each block Give each genotype a random number assignment from 1 to 300, then numbers 1-49 are used in Block 1, numbers are in Block 2, etc.\ Discuss how any design is selectable here, you could have incomplete block, split plot/block are easily applied, the key is that the any experimental design can be augmented to accommodate a set of new treatments that are to be replicated once.

17 How many repeats of each check for optimal design?
Design Resources Server Indian Agricultural Statistics Research Institute (ICAR), New Delhi, India. Online Design Generation-I Augmented Design The reference by Parsad et al has not been peer-reviewed as far as my research tells me.

18 Construction of Augmented Designs via IASRI site
Home (Augmented designs) Outline of Analysis Welcome to construction of Augmented Designs. Use this to generate augmented design. Fill in the number of test treatments, control treatments and number of blocks etc.   Number of Test treatments (w):        Number of Control Treatments (u)    Number of Blocks (b)                       Number of replication of control        Requires Javascript to be enabled; Runs in Microsoft(R) Internet Explorer(R) 5.0 and above and Mozilla(R) Firefox (R) 2.0 & above Kindly send us your comments, problems to V.K. Gupta  /  Rajender Parsad / A. Dhandapani. Home (Augmented designs)  Design Resources Copyright        Disclaimer        How to Quote this page        Report Error        Comments/suggestions  Inserted from <>

19 Cells filled out, Enter block sizes
Augmented Designs Home (Augmented designs) Outline of Analysis Welcome to construction of Augmented Designs. Use this to generate augmented design. Fill in the number of test treatments, control treatments and number of blocks etc. Number of Test treatments (w): 300 Number of Control Treatments (u) 4 Number of Blocks (b) 6 Number of replication of control 2 Optimum Total Number of Experimental Units required: 348 To enter Block Sizes click here Block 1: 58 Block 2: 58 Block 3: 58 Block 4: 58 Block 5: 58 Block 6: 58 Total Number of Experimental Units = 348; Assigned so far = 348; Remaining = 0. Submit

20 Generated Design Generated Design Block 1: (T132, T1, T214, T159, T15, T55, T69, T197, C3, T31, T88, T124, T134, T245, T165, C1, T290, T163, T291, T101, T238, T298, T74, T282, C2, T35, C3, T135, T185, T181, T8, T72, T166, T217, T3, T260, T156, T270, T7, T220, T84, T207, T170, C4, T20, T240, C1, T51, T210, T138, T112, T68, T5, C2, C4, T108, T258, T118) Block 2: (T295, C4, T208, T152, T39, T91, T178, T219, T215, T52, T237, T82, T248, C1, T62, T133, T29, T53, T162, C3, T60, T66, T172, T198, T231, T271, T269, T183, T106, C2, T253, T80, T235, C4, T199, T257, C1, T232, T289, T125, T204, T78, T43, T119, T9, T97, T16, T115, C3, T102, T294, T192, T85, C2, T300, T143, T160, T223) Block 3: (T107, T184, T280, T42, T200, T131, T17, T21, T46, T276, T100, T169, T93, T180, C1, T267, T283, C1, T4, T239, T275, T120, T262, T236, T233, T193, T70, T277, T56, T168, T110, T105, T287, T38, T171, T27, T18, C2, T274, T136, T265, C3, T293, T281, C2, T175, T59, T79, T128, C4, T158, T146, C4, T252, T161, C3, T225, T89) Block 4: ( C2, T41, T234, T14, T10, T86, T206, T145, T230, T249, T196, T114, T209, T90, T205, C2, C3, T13, C3, T244, T142, T48, T76, C1, T2, C4, T261, T12, T36, T23, T109, T113, T255, T213, T191, T202, T218, T58, C1, T285, T96, T67, T188, T45, T150, T137, T164, T259, T221, T273, T73, T144, T226, T155, T40, T194, C4, T167) Block 5: (T266, T71, C2, T157, T179, T222, T123, C1, T104, T278, T111, T272, T22, T47, T148, T182, T212, T81, T195, T247, T216, T174, T25, C3, T130, T251, T203, T28, T228, T263, T297, C4, T246, T92, T64, T117, C1, T61, C4, T32, T19, T147, T288, T94, T99, T189, T34, T98, T243, C3, T57, T254, T126, T6, T122, C2, T242, T121) Block 6: (T54, T139, T151, T77, T49, C4, C2, T201, T284, T129, T241, T227, C2, T65, T95, T264, T11, T211, T268, T296, T75, T154, T141, T33, T149, C3, T173, T176, T286, T224, T250, T37, C1, T256, T87, T177, C3, T44, T116, T103, C4, T187, C1, T190, T299, T26, T140, T50, T127, T30, T279, T229, T63, T292, T24, T83, T153, T186)

21 RCB Model - Augmented Y = u + check + block + test entry + error
checks are fixed effects (source of experimental error) block, test entry, and error are random effects Recall: Fixed effects = parameter estimation (mean and experimental error) Random effects = sources of variability Just like you can’t replicate a block – we’re also not replicating a treatment These are random effects – variance estimates Treatments are Random b/c 1) they represent a random selection of the population 2) Another time we might have a different sample, hence they are random Select appropriate model to account for variation present in data from experiment

22 Data Structure – Summarize Observations
Data data-set; input BLOCK ENTRY $ CHECK $ Response ; datalines; 1 99_3 C 1 99_1 A 2 99_3 C 99_2 B 6 99_4 D Switch Genotypes to Entry or Entries in previous slides We need to have dummy coding to estimate the fixed and random effects

23 Analysis of Augmented RCB
proc mixed; class CHECK BLOCK ENTRY; model response = CHECK / solution; random BLOCK ENTRY / solution; lsmeans CHECK; run; Remember – Check has a label of 0 for the overall entries in the data

24 Covariance Parameters
Covariance Parameter Estimates Cov Parm Estimate BLOCK variance component of block ENTRY variance component of entry Residual error variance Variance estimate corresponding with this response is greatest with the Entry – this is what you want, it shows the greatest variability

25 LSMEANS – Checks Least Squares Means Standard
Effect CHECK Estimate Error CHECK CHECK A CHECK B CHECK C CHECK D Can you all see that where the entries lie within the checks. Checks are more appropriate.

26 SOLUTION option in MODEL statement
presents estimates of the fixed effect parameters Solution for Fixed Effects Standard Effect CHECK Estimate Error Intercept CHECK CHECK A CHECK B CHECK C CHECK D Intercept = grand mean

27 Estimated BLUPs Best Linear Unbiased Predictors
Random effects – estimate the variance Estimate “realized values of random variables” (test entries) Augmented designs – use SOLUTION option in RANDOM statement random BLOCK ENTRY / solution;

28 Solution for Random Effects

29 “Realized values” Rearrange estimates of entries from highest to lowest proc sort data=data-set; by DESCENDING estimate; run; Add Intercept to Estimate values – calculate predicted adjusted mean values data data-set; pred_adjmean = estimate ;

30 Predicted Adjusted Mean Values
StdErr pred_ Obs Effect BLOCK ENTRY Estimate Pred DF tValue Probt adjmean ENTRY _ < ENTRY _ < 3 ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < ENTRY _ < Discuss how to ‘look’ at the data. So, if our focus is to find something ‘better’ i.e. ‘higher up the list’. The focus is not really on doing any multiple comparisons, there is no need. The ranking of the pred_adjusted means allows you to visualize which ‘treatments’ or worth pushing forward for further research. You need to be more willing to accept Type I or Type II errors, because they will probably happen. This is a Linear Model based on the checks. These numbers are a linear model prediction. The ranks and the ordering are the information that help you move forward, it is the potential of the test entry. The effect of the test entry is random, conclusion drawn pertain only to the response of the fixed effects (Checks). Conclusions about the levels at hand – Narrow Space Inference

31 Augmented Designs - Recap
Screening – Discovery Driven Select 4-6 meaningful checks Select appropriate experimental design and increase rows and columns to include unreplicated test entries in each block RCB is simplest case, split-plots, factorials are also possibilities (can look at interactions and autocorrelations) Mixed model analyses Phase II begins – Select entries to do pilot study to elicit better estimate of true response; generate hypotheses

32 Augmented Designs - References
To get started: Google! Federer et al (2001) Agron. J. 93: Federer, W.T. (2005) Agron. J. 97: Burgueño and Crossa (2000) SAS Macro for Analysing Unreplicated Designs CIMMYT CRIL (crop research informatics laboratory) IASRI, Augmented Design tool IRRISTAT

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