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Statistical thinking in antibiofilm research Cord Hamilton Al Parker Marty Hamilton MBL and SBML: 23 October 2008 1

Statistical thinking in antibiofilm research

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Statistical thinking in antibiofilm research. Cord Hamilton Al Parker Marty Hamilton. MBL and SBML: 23 October 2008. Topics (presenter). Calculating LR and the within-experiment standard error of LR (Cord) - PowerPoint PPT Presentation

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Page 1: Statistical thinking in antibiofilm research

Statistical thinking in antibiofilm research

Cord HamiltonAl Parker

Marty Hamilton

MBL and SBML: 23 October 20081

Page 2: Statistical thinking in antibiofilm research

Topics (presenter)

Calculating LR and the within-experiment standard error of LR (Cord)

Using data from repeated experiments to find more reliable LR values in the future (Al)

Analysis of dilution series counts (Marty)

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Page 3: Statistical thinking in antibiofilm research

Log Reduction (LR) fora Quantitative Assay

Vc = viable cell density of biofilm grown in the absence of antimicrobial treatment

Vd = viable cell density of biofilm grown in the presence of the disinfectant

Log Reduction = log10(Vc) - log10(Vd)

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Page 4: Statistical thinking in antibiofilm research

Numerical Example

Vc = 107 & Vd = 10

Log Reduction = log10(107) - log10(10) LR = 7 - 1 LR = 6

Interpretation: disinfectant killed 99.9999% of the bacteria

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Page 5: Statistical thinking in antibiofilm research

Calculating LR whenthere are multiple coupons

= mean of control log10 densities

= mean of disinfected log10 densities

Log Reduction =

C

D

C - D

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Page 6: Statistical thinking in antibiofilm research

Example: Mean of logsfor 3 disinfected coupons

Coupon Density log10Density (i) cfu / cm2 (Di) 1 9.6·104 4.982 2 1.7·104 4.230 3 9.7·103 3.987

Mean= 4.400 =

mean density = 4.09 10∙ 4

log of mean density = 4.61

D

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Page 7: Statistical thinking in antibiofilm research

Example: Control coupons

Coupon log10Density (i) (Ci)

1 7.499 2 7.013 3 7.863

C = 7.458

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Page 8: Statistical thinking in antibiofilm research

Calculating LR whenthere are multiple coupons

= 7.458 & = 4.400

Log Reduction =

C D

C - D

= 7.458 - 4.400

LR = 3.058

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Page 9: Statistical thinking in antibiofilm research

Within-experiment standard error (SE) of the LR

Sc = variance of control log10 densities

Sd = variance of disinfected log10 densities

nc = number of control coupons

nd = number of disinfected coupons

SE of LR = (within-experiment)

Snc

Snd

c2

d2

2

2

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Page 10: Statistical thinking in antibiofilm research

Example: Calculating SEfor single reactor experiment

Sc = 0.181865 and nc = 3

Sd = 0.269272 and nd = 3

SE = 0.181865

30.269272

3

2

2

= 0.3878

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Page 11: Statistical thinking in antibiofilm research

Uncertainty in LR Estimate

LR ± SE = 3.058 ± 0.388

or 3.06 ± 0.39

or 3.1 ± 0.4

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Page 12: Statistical thinking in antibiofilm research

3

2

1

0Log

Redu

ction

± S

E

Experiment1 2

RDR biofilm: 5 ppm chlorine for 10 minutes

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3 4 5

Page 13: Statistical thinking in antibiofilm research

Experiment repeated 3 times, each using three control and 3 disinfected coupons

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Page 14: Statistical thinking in antibiofilm research

Statistical summary for data from 3 experiments, with 3 control and 3 disinfected coupons per experiment

log density mean log density SD log density Standard error ofExp control disinfected control disinfected   log reduction control disinfected log reduction

1 6.73849 3.081151 6.82056 3.29326 6.83240 3.13546 3.69695 0.10036 0.13886 0.098921 6.93816 3.03196

2 6.66276 2.923342 6.73957 3.03488 6.71440 3.05656 3.65784 0.04473 0.14528 0.087762 6.74086 3.21146

3 6.91564 2.737483 6.74557 2.66018 6.85293 2.70805 4.14488 0.09341 0.04183 0.059093 6.89758 2.72651

Pooled within-experiment SD of the control log density: 0.08326Pooled within-experiment SD of the disinfected log density: 0.11851

Between-experiment SD of the log reduction: 0.25736

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Page 15: Statistical thinking in antibiofilm research

S

nc • m

c2

+

Formula for the SE of the mean LR, averaged over experiments

Sc = within-experiment variance of control coupon LD

Sd = within-experiment variance of disinfected coupon LD

SE = between-experiments variance of LR

nc = number of control coupons

nd = number of disinfected coupons

m = number of experiments

2

2

2

S

nd • m

d2

+S

m

E2

SE of mean LR =

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Page 16: Statistical thinking in antibiofilm research

Formula for the SE of the mean LR, using estimated standard deviations

0.0833

nc • m+

0.1185

nd • m

2

+0.2574

m

2

SE of mean LR =

2

Pooled within-experiment SD of the control log density: 0.0833Pooled within-experiment SD of the disinfected log density: 0.1185

Between-experiment SD of the log reduction: 0.2574

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Page 17: Statistical thinking in antibiofilm research

Choosing the numbers of coupons and the number of experiments. Table cell is the the SE of the mean LR. Shaded SE values are designs requiring 24 coupons.

no. control coupons (nc): 2 3 6 12no. disinfected coupons (nd): 2 3 6 12

no. experiments (m)  1 0.277 0.271 0.264 0.2612 0.196 0.191 0.187 0.1843 0.160 0.156 0.152 0.1514 0.138 0.135 0.132 0.1306 0.113 0.110 0.108 0.106

10 0.088 0.086 0.084 0.082100 0.028 0.027 0.026 0.026

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Page 18: Statistical thinking in antibiofilm research

Dilution series and drop plate technique

Source: BiofilmsOnline

Counted dilution32 colonies

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Page 19: Statistical thinking in antibiofilm research

Find the fraction of initial beaker volume in each of the dilution tubes

Source: BiofilmsOnline

Beaker: containedall cells fromcoupon

0.1 0.01 0.001 0.0001

fraction of beaker volume in tube

10

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Page 20: Statistical thinking in antibiofilm research

Estimated number of cells in beaker = cfu count divided by the volume fraction plated

Beaker: containsall cells fromcoupon

10-4 fraction in tube

Plated 50 μl from tube;plate contains a fraction50/10000 = 5 x 10-3 of the volume in the tube.

f = (5 x 10-3) 10-4 = 5 x 10-7

Estimate:32/(5 x 10-7) = 6.4 x 107

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Page 21: Statistical thinking in antibiofilm research

Dilution series and filter technique: pooling data from two tubes

9 mlfiltered

10 ml filtered

Count 20 fields on each filter;corresponds to 0.02 of filter area

f = 0.001 x 0.9 x 0.02 = 1.8 x 10-5

f = 0.0001 x 1.0 x 0.02 = 2.0 x 10-6

421cfu

39cfu

The 460 cfu corresponds to this fraction of the beaker volume:f = 1.8x10-5 + 2.0x10-6

= 2.0 x 10-5

Estimate for beaker = 460/(2.0x10-5)= 2.3 x 107

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