©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Opportunities for Statistical Contributionsto Bioassay
David Lansky, Ph.D.
Burlington, Vermont, USA
May, 2012
1 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Abstract
A recent major update to the USP guidance on bioassaypreserved the fundamentals of bioassay statistics. Parts of thisupdate expanded some explanations and examples (i.e., morecomplex design structures), to address issues that have oftenbeen ignored in practice. The update includes conceptualchanges that are important advances for bioassay based oncareful application of statistics; two important changes are:equivalence testing for similarity and guidance on how to deriveassay performance requirements from product specifications.This talk will briefly summarize the practical and statisticalchallenges in bioassays, review the USP guidance, highlightimportant changes then move on to describe recent researchresults that can guide current practice in bioassay as well asindicate where more research is needed. The wrap-up willsummarize areas where there are opportunities for statisticalresearch and improved statistical practice; these are importantopportunities for statisticians to contribute to improved bioassaypractices.
2 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Bioassay Basics
I EC50 varys
I Relative potency iff”biosimilar” active
I ’Biosimilar’ ⇒similar curves
I Reduced modelI potencyI variance
w/Fieller’s
logEC50
log potency
3 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Types of Bioassays
1. Direct (random dose, fixed response)2. Indirect (fixed doses, random responses)
I Discrete response (logit/probit-log)I Quantitative response
I Slope RatioI Parallel Line
I straight lineI nonlinear ⇐I smooth
4 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Assumptions (Parallel Line)
I From a consistent process
I Normal
I Constant Variance
I Independent
I Ref and test have ”biosimilar” activematerial
I Between-assay σ2 of log potency is zero
I Fieller’s works
5 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Violations of Assumptions (1)
I Non-Constant VarianceI TransformI Weight
I Non-normalityI TransformI Change likelihood
I Non-IndependenceI Change assay designI Model design structure
6 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Violations of Assumptions (2)
I Non-Biosimilar:Potency has no meaning
I Non-zero between-assay σ2Log Potency
I Combine potencies w/o wt or ”semi-wt”I Mean log potency & sampling SD
I Fieller’s fails: From 1 assay don’t reportI σ2 of (log) potencyI CI on potency
7 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
USP Big Changes
I Develop, rather than routinely testI Constant varianceI Normality
I Transform recommended vs. wtI Outliers: assess after transform and all-data
minimal-assumptions model
I Assess similarity with equivalenceI Emphasize and explain design structureI Routinely combine potencies w/”simple”I Validation
I Performance requirements from needs (Cp)I Reportable value (geometric mean potency)I Variance components approach
8 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Why Bioassay?
I σ2Log EC50 >> σ2
Log Potency (Blocks)
I Multi-dose test needed (not single-pointw/calibration curve) to assess similarity
I High variance system need replicatesI Parallel line relative potency is:
I Linear: intercept differenceslope
I Nonlinear: difference in Log EC50s
9 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Causes of 6= Variance
I 25 typical reference curvesI Several processes contribute to common
variance patterns:I variation changes with responseI σ2
Log EC50 largeI serial dilution errorI underlying binomial response
Ref curves from 25 assays
log concentration
Response
SD estimated at each concentration
log concentration
SD
(Resp
onse
)
10 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Weight vs. Transform
I Good Weighting Practices:I Weights known (i.e., Probit)I If weights from data
I Non-correlated observationsI Dangers if weights = f (y)
I Good Transform Practices:I residual plotsI theoretical modelI Box-Cox
11 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Setting Equivalence Bounds
I Equivalence (quickly) acceptedI Setting equivalence bounds?
I Assay capability? BAD IDEAI Time-honored: by eyeI Driven by medicine (safe & effective)I Can we do better?
I Parameter-specific boundsI parameters meaningfulI certain combinations particularly badI quality attributes
I Goal: universally meaningful amounts ofnon-similarity?
12 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Universal Non-Similarity
I y ∗ = Ai
1+e−Bi (log(x)−Ci )+ Di + ε
I Responses and parameters in varyingunits
I Scaled Similarity ParametersI %∆A = 100× (ATest − ARef)/ ARef
I %∆D = 100× (DTest − DRef)/ ARef
I %∆B = 100× (BTest − BRef)/ BRef
I Concerns:I Is meaning consistent across assays?I Variances and confidence intervals for
%∆A, %∆B , and %∆D
13 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Interpret ∆A:B :D 10:50:10
% Shifts have consistent meaning
A: Range
B: Slope
D: Lower Asy.
I A and D × (2/3, 1, 2/3)
I B × (1/3, 1, 3)
14 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Factorial %∆A:B :D 5:35:5
Lower Asy -5%
{ -35 }{ -5 }
{ 0 }{ -5 }
{ 35 }{ -5 }
{ -35 }{ 0 }
{ 0 }{ 0 }
{ 35 }{ 0 }
{ -35 }{ 5 }
{ 0 }{ 5 }
{ 35 }{ 5 }
15 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Factorial %∆A:B :D 5:35:5
Lower Asy 0% change
{ -35 }{ -5 }
{ 0 }{ -5 }
{ 35 }{ -5 }
{ -35 }{ 0 }
{ 0 }{ 0 }
{ 35 }{ 0 }
{ -35 }{ 5 }
{ 0 }{ 5 }
{ 35 }{ 5 }
16 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Factorial %∆A:B :D 5:35:5
Lower Asy +5%
{ -35 }{ -5 }
{ 0 }{ -5 }
{ 35 }{ -5 }
{ -35 }{ 0 }
{ 0 }{ 0 }
{ 35 }{ 0 }
{ -35 }{ 5 }
{ 0 }{ 5 }
{ 35 }{ 5 }
17 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Remaining Equivalence Challenges
I Some advocate composit testingI Evaluating ”universal” bounds
I 5% asymptote and range seem largeI 35% slope seems okI some combinations likely to induce bias
I Experience with %∆A:B:DI excellent assays can use 5:35:5I noisy assays struggle with 10:50:10
I truncation bias
18 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Quotes from Finney (1978)
A biochemist, pharmacologist, or microbiologist whose own
statistical expertise is small will perhaps object to some of the
designs in later chapters: ...because when he had obtained the
data he would have no idea how to analyze them. This difficulty
illustrates the need for close collaboation between the
experimental scientist and the statistician. ...the right policy is
surely to learn how to analyze the data or to obtain assistance
from a professional statistician.
In framing his advice, the statistician needs to remember that a
simple design can give better results ...
19 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Simple Design?
G
F
E
D
C
B
2 3 4 5 6 7 8 9 10 11
20 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Simple Design?
G
F
E
D
C
B
2 3 4 5 6 7 8 9 10 11
21 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
6-Block Randomized Strip-Plot Design
ABCDEFGH
1 2 3 4 5 6 7 8 9101112
1
1 2 3 4 5 6 7 8 9101112
2
1 2 3 4 5 6 7 8 9101112
ABCDEFGH
3
1 2 3 4 5 6 7 8 9101112
ABCDEFGH
4
1 2 3 4 5 6 7 8 9101112
5
1 2 3 4 5 6 7 8 9101112
ABCDEFGH
6
22 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Mixed Model Fit
Log Concentration
Lo
g R
esp
on
se
1.0
1.5
2.0
2.5
-10 -8 -6 -4 -2 0
11111
1
1111
DDDDDD
DD
DD
HHH
HH
HHHHH
RRRR
R
RRRRR
1
1111
11
1111
DDDDD
DD
DDD
HHH
H
HHHHHH
RRRR
RR
RRRR
2
-10 -8 -6 -4 -2 0
11111
11111
DDDDDD
DDDD
HHHH
HHHHHH
RRRR
RR
RRRR
3
11111
111
11
DDDDDD
DD
DD
HHHH
HHHH
HH
RRRRR
RR
RRR
4
-10 -8 -6 -4 -2 0
1111
1
111
11
DDDDD
D
DD
DD
HHHH
HHHHHH
RRRR
R
RRR
RR
5
1.0
1.5
2.0
2.5
1111
11
1111
DDDDDD
DD
DD
HHH
H
HHHHHH
RRRRR
RRRRR
6
23 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Problems with Variance Estimates
I Without design structure in the model,variance estimates are meaningless
I Experience with real and simulated dataon typical bioassays:
I In linear, Fieller’s substantiallyunderestimates variance of potency
I In nonlinear, model-based variance ofpotency is an underestimate
I Good variance estimators would agreewith sampling variance of log potency
I Sampling variance of log potency fromindependent assays is simple
24 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Combining potencies
I Experience with real (typical) bioassays:I Assays with between-assay variance of log
potency = 0 are very rareI Both from a lab with:
I long and strong experience delivering greatbioassays
I robotsI strip-plot design in assayI strip-plot in analysis
I Conclusion: Don’t expect to weight or”semi”-weight
25 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Highlights of Recent Results
I Avoid Slope Ratio
I Avoid Straight Parallel Line
I Four-parameter logistic broadly robustI Truncation Bias common, to avoid:
I Wide dose range neededI reduce variation around curveI within-assay blocks help
26 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Truncation Bias Simulation
I log 2 target potencies -1.5 to 1.5 (by 0.1)
I CRD
I 4PL data (A=1, B=1.6, C=6.5, D=0)I four doseDesigns:
1 10 doses, 1-12, skip 3 & 102 10 doses 2-113 10 doses 1-12 (= spacing)4 20 doses 1-12 (= spacing)
I residual SD = 0.01, 0.03, 0.05, 0.07
I n = 3, 6, 9
I Similarity criteria = 5:35:5, 10:50:10
I 20 simulated assays at each condition
27 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Potency=0.5, σ = 3%, n=6
0.0
0.2
0.4
0.6
0.8
1.0
2 4 6 8 10 12
R
RR
R
R
R
R
RR R
R R
RR
R
R
R
R
RR
R R R
R
R
R
R
RR R
R RR
R
R
R
R
R R R
RR
R
R
R
R
RR
RR
R RR
R
R
R
RR R
R
T T T T
T
T
T
T
T T
T TT
TT
T
T
T
TT
T T T T
T
T
T
TT T
T TT
T
T
T
T
T
TT
T T TT
T
T
T
TT T
T T TT
T
T
T
T
T T
28 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Truncation Results 1 (Nsim=10)
Similarity Failures
Log 2 Target Potency
Perc
ent Sim
ilarity
Failu
re
0
40
80
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
11111111111111111111111111111113333333333333333333333333333333555555
555555
5555555555
5555
5555
5
7777777777777777777777777777777
10:50:10 : n { 3 }
1111111111111111111111111111111
333333333333333333
3333
3333333
33
555555555555555555555555555555577777777777777777777777777777775:35:5 : n { 3 }
111111111111111111111111111111133333333333333333333333333333335555555555555555555555555555
555777777
77777
77777777
77777
77777
77
10:50:10 : n { 6 }
0
40
80
111111111111111111111111111111133
33333333333333333333333333333
55555555555555555555555555555557777777777777777777777777777777
5:35:5 : n { 6 }
0
40
80
1111111111111111111111111111111333333333333333333333333333333355555555555555555555555555555557777777777
777777777777777777777
10:50:10 : n { 9 }
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
1111111111111111111111111111111333333
3333333333333333333333333
55555
5555555555555
5555555555555
77777777777777777777777777777775:35:5 : n { 9 }
29 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Truncation Results 2
Similarity Failures 5:35:5
Log 2 Target Potency
Percent Sim
ilarity Failu
re
0
40
80
-1.5 -0.5 0.5 1.5
1111111111111111111111111111111
3333333333333333333333333333333
55555555555555555555555555555557777777777777777777777777777777 : doseDesign { 1 }
: n { 3 }
1111111111111111111111111111111
333333333333333333333333333333355555555555555555555555555555557777777777777777777777777777777 : doseDesign { 2 }
: n { 3 }
-1.5 -0.5 0.5 1.5
1111111111111111111111111111111
33333333333333333333
33333333333
55555555555555555555555555555557777777777777777777777777777777 : doseDesign { 3 }
: n { 3 }
111111111111111111111111111111133333333333333333333333333
33333
55555555555555555555555555555557777777777777777777777777777777 : doseDesign { 4 }
: n { 3 }1111111111111111111111111111111
3333333333333333333333
333333333
55555555555555555555555555555557777777777777777777777777777777 : doseDesign { 1 }
: n { 6 }
1111111111111111111111111111111
3333333333333333333333
333333333
55555555555555555555555555555557777777777777777777777777777777 : doseDesign { 2 }
: n { 6 }
111111111111111111111111111111133333333333333
33333333333333333
55555555555555555555555555555557777777777777777777777777777777 : doseDesign { 3 }
: n { 6 }
0
40
80
11111111111111111111111111111113333333333333333333333333333333555555
55555555555555555555555
557777777777777777777777777777777
: doseDesign { 4 } : n { 6 }
0
40
80
1111111111111111111111111111111333333333333333
3333333333333333
555555555555555555555
55555555557777777777777777777777777777777
: doseDesign { 1 } : n { 9 }
-1.5 -0.5 0.5 1.5
11111111111111111111111111111113333333333333333333333
333333333
55555555555555555555555555555557777777777777777777777777777777 : doseDesign { 2 }
: n { 9 }
11111111111111111111111111111113333333333333333333333333333333
55555555555555555555555
55555555
7777777777777777777777777777777 : doseDesign { 3 }
: n { 9 }
-1.5 -0.5 0.5 1.5
11111111111111111111111111111113333333333333333333333333333333555555555
55555555555555555555
557777777
77777777777777777777
7777 : doseDesign { 4 }
: n { 9 }
30 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Truncation Results 3
PG Bias of Potency 5:35:5
Log 2 Target Potency
Percent Geometric Bias
-10
0
10
-1.5 -0.5 0.5 1.5
1111111111111111111111111111111333333333
3333333333333333333333
: doseDesign { 1 } : n { 3 }
1111111111111111111111111111111333333333333333333
33333
: doseDesign { 2 } : n { 3 }
-1.5 -0.5 0.5 1.5
1111111111111111111111111111111333333333333
33333333333333333
33
: doseDesign { 3 } : n { 3 }
11111111111111111111111111111113333333333333333333333333333333555555
555555
: doseDesign { 4 } : n { 3 }
111111111111111111111111111111133333333333333333333333333333335555 55
55
555555555
: doseDesign { 1 } : n { 6 }
11111111111111111111111111111113333333333333333333333333333333
55
: doseDesign { 2 } : n { 6 }
1111111111111111111111111111111333333333333333333333333333333355555
5555
555
555555555
: doseDesign { 3 } : n { 6 }
-10
0
10
1111111111111111111111111111111333333333333333333333333333333355555555555555555555555555555557777777 7
: doseDesign { 4 } : n { 6 }
-10
0
10
1111111111111111111111111111111333333333333333333333333333333355555555555555555555555
55555555
: doseDesign { 1 } : n { 9 }
-1.5 -0.5 0.5 1.5
1111111111111111111111111111111333333333333333333333333333333355555555555555555
: doseDesign { 2 } : n { 9 }
111111111111111111111111111111133333333333333333333333333333335555555555555555555555555555555
: doseDesign { 3 } : n { 9 }
-1.5 -0.5 0.5 1.5
111111111111111111111111111111133333333333333333333333333333335555555555555555555555555555555777777777777777777
7777777777777
: doseDesign { 4 } : n { 9 }
31 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Truncation Results 4
PGSD of Potency 5:35:5
Log 2 Target Potency
Perc
ent G
eom
etric
SD
048
12
-1.5 -0.5 0.5 1.5
111111111111111111111111111111133333
333333333333333333
33333333
: doseDesign { 1 } : n { 3 }
1111111111111111111111111111111333333
333333
333
: doseDesign { 2 } : n { 3 }
-1.5 -0.5 0.5 1.5
1111111111111111111111111111111
33
333333333
333333333333333333
33
: doseDesign { 3 } : n { 3 }
1111111111111111111111111111111333333333
33333333333333333333335 5
: doseDesign { 4 } : n { 3 }
11111111111111111111111111111113333333
333333333333333333333333
55 5 5
: doseDesign { 1 } : n { 6 }
111111111111111111111111111111133333333333333333333333333
33333
: doseDesign { 2 } : n { 6 }
111111111111111111111111111111133333333333333333333333333333
3355555
5 5
: doseDesign { 3 } : n { 6 }
04812
1111111111111111111111111111111333333333333333333333333333333355555
555555555555555555555
55555
7 : doseDesign { 4 }
: n { 6 }048
12
111111111111111111111111111111133333333333333333333333333333335555555555555555
5555
5555555
5555
: doseDesign { 1 } : n { 9 }
-1.5 -0.5 0.5 1.5
1111111111111111111111111111111333333333333333333333333333333
3555
5
555 5
: doseDesign { 2 } : n { 9 }
1111111111111111111111111111111333333333333333333333333333333355555555
555555555555555555555
55
: doseDesign { 3 } : n { 9 }
-1.5 -0.5 0.5 1.5
11111111111111111111111111111113333333333333333333333333333333555555
5555555555555555555555555
77777777
77777777
777777
777777
777
: doseDesign { 4 } : n { 9 }
32 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Practical Challenges
I Broad ignorance of design structure
I Bioassay software very limitedI Many constraints on bioassays
I USP/EP guidanceI limited access to statistical supportI 8× 12 platesI complex process, many grouped steps
I Robotics
I many think a fit with smaller residual is(always) better
33 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Statistical Challenges
I Learn, use, and teach design structure
I Get into the lab, work WITH ”clients”
I Robot softwareI Nonlinear RE models on complex design
structures are delicateI sensitive to outliersI must do model selection (HOW?)
I Consider more than bias and SD, failuresmatter
34 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Summary
I Core ideas of bioassay 60+ years old
I Use advances in statistics
I Bioassay uses many statistical methods
I Oopportunities for statisticians inbioassay, people and biology skills matter
I Substantial need for better bioassays
35 / 36
©
D. Lansky
Abstract
BioassayFundamentals
Changes to USPBioassay Guidance
Changes Driven by Science
Equivalence
Design Structure
Variances
Challenges in Bioassay
Summary
Acknowledgements
Acknowledgements
I Consulting clients
I USP and USP bioassay panel members
I Stan Deming
I Tim Schofield
I Carrie Wager
I NSF EPSCoR
I NIH SBIR 3R44RR02198-03S1
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