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The Test for Significant Toxicity (TST) – A “New” Hypothesis Testing Approach for Aquatic Bioassay Testing PowerPoint Presentation
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The Test for Significant Toxicity (TST) – A “New” Hypothesis Testing Approach for Aquatic Bioassay Testing. Philip Markle Environmental Scientist pmarkle@lacsd.org. History of the TST. June 2010 – EPA released WET TST guidance (EPA 833-R-10-003) Also referred as: Bioequivalence Testing

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The Test for Significant Toxicity (TST) – A “New” Hypothesis Testing Approach for Aquatic Bioassay Testing

Philip Markle

Environmental Scientist

pmarkle@lacsd.org

history of the tst
History of the TST
  • June 2010 – EPA released WET TST guidance

(EPA 833-R-10-003)

  • Also referred as:
    • Bioequivalence Testing
    • Alternative Null Hypothesis Testing
  • Accepted for FDA drug trials and evaluations
  • Originally proposed for use in toxicity testing in 1995 (Erickson and McDonald)
  • Recently proposed for CA’s WET Policy
limitations of the tst
Limitations of the TST
  • It is still a statistical hypothesis test
    • Not very useful for comparing results spatially or temporally
    • Pass/Fail test, provides no information on magnitude
  • Requires knowledge/use of a “threshold” response – “b” or bioequivalence factor
  • Probably (and debatably) best suited for regulatory purposes
statistical hypothesis testing 101
Statistical Hypothesis Testing 101
  • Statistical speaking;
    • You can’t “prove” anything with a hypothesis test – we only “disprove”
  • The “White Swan” Parable:
statistical hypothesis testing 1015
Statistical Hypothesis Testing 101
  • You can’t prove that “all swans are white”
  • If we see 10,000 white swans and no non-white swans, we fail reject our hypothesis
  • In the absence of evidence to the contrary, we then assume the hypothesis is true
proving with statistics
“Proving” with Statistics
  • However, after observing just one non-white swan, we can then confidently reject or disprove our hypothesis that all swans are white
statistical hypothesis testing background
Statistical Hypothesis Testing - Background
  • Null or “Initial” Hypothesis (Ho)
    • Mean(sample) Mean(control)
  • Conduct statistical analyses to try to reject this hypothesis
  • If unable to reject, we assume the null or “Initial” hypothesis is correct
  • Type I and Type II error
type i and type ii errors
Type I and Type II Errors
  • Type I Error
    • Probability of rejecting when the null or “Initial” hypothesis when it is “true”
    • Controlled directly by setting alpha ()
  • Type II Error
    • Probability of accepting the null or “Initial” hypothesis when it is “false”
    • Also called “power” ()
    • Controlled indirectly
standard hypothesis testing noec
Standard Hypothesis Testing (NOEC)
  • With the NOEC:
    • The initial hypothesis is

mean (sample)  mean (control)

In other words, the sample is non-toxic!

    • If we don’t/can’t “prove” this to be incorrect statistically, we assume it is true
    • Type I error = Identifying a non-toxic sample as toxic
tst hypothesis
TST Hypothesis
  • With the TST:
    • The hypothesis is

mean(effluent) =/< 0.75 * mean(control)

In other words, the sample is toxic!

    • If we don’t/can’t “prove” this to be incorrect statistically, we assume it is true – we assume the sample is toxic
    • Type I error = Identifying a toxic sample as non-toxic
bioequivalence factor b
Bioequivalence Factor (b)
  • In the EPA Guidance
    • Set as an unacceptable or “toxic” threshold
  • For Chronic:
    • B = 0.75 = 25% Effect
  • For Acute
    • B = 0.80 = 20% Effect
regulatory management decisions rmds
Regulatory Management Decisions (RMDs)
  • Setting the Type I Error Rate–alpha ()
    • How frequent will you reject the Ho when it is true?
  • EPA desires that no more than 25% of the tests with a 25% effect or more are identified as “non-toxic”
  • Alpha () is then set at 0.05 to 0.25, depending on the test
why the different alphas
Why the Different Alphas?
  • EPA’s Second Regulatory Management Decision
    • No more than 5% of tests with effects less than 10% should be identified as toxic
    • Type II Error Rate – not really a “false positive”
  • Alpha adjusted down until no more than 5% of tests with effects less than 10% were identified as “toxic”
    • Monte Carlo simulations
tst equation welch s t test
TST Equation (Welch’s t-test)
  • t =
  • t (calculated) < t (table/critical) = toxic
  • t (calculated) > t (table/critical) = non-toxic
factors that impact ability to statistically reject the hypothesis
Factors That Impact Ability to Statistically Reject the Hypothesis
  • Magnitude of Effect
  • Number of Replicates
  • Within Test Variability
tst equation welch s t test17
TST Equation (Welch’s t-test)
  • t =
  • All tests (100%) with an effect of 25% will be identified as “toxic”
  • The greater the within test variability, the harder or less likely it will be to identify a sample as being statistically different (non-toxic).
  • The more replication, the more likely it will be to identify a sample as being statistically different (non-toxic).
controllable factors that impact ability to statistically reject the hypothesis
Controllable Factors That Impact Ability to Statistically Reject the Hypothesis
  • Variability
    • The greater the within test variability, the harder or less likely it will be to identify a sample as being statistically different.
    • For the “regular” hypothesis test
      • Less frequent identification of “toxicity”
    • For the TST
      • Less frequent identification of “no toxicity”
  • Replication
procedures that may reduce variability
Procedures That May Reduce Variability
  • Maximize Mean Response
      • CV = S.D. / Mean

From EPA Test of Significant Toxicity (TST) Document

EPA 833-R-10-003

impact of control mean
Impact of Control Mean
  • At the 10th Percentile (17.7) - a 25% effect is reduction of 4.4 neonates
  • At the 50th Percentile (25.5) - a 25% effect is reduction of 6.4 neonates
  • At the 95th Percentile (35.6) - a 25% effect is reduction of 8.9 neonates
procedures that may increase mean response
Procedures That May Increase Mean Response
  • Dilution Water Selection
    • Match sample condition as much as possible
  • Food Supplements, Combinations
    • Specifically allowed (13.6.16.9.2)
  • Feeding Rates
    • Twice or three times per day
    • Amount of food
procedures that may decrease variability
Procedures That May Decrease Variability
  • Set Internal Control CV Criteria
procedures that may decrease variability27
Procedures That May Decrease Variability
  • Set Internal Control Mean Criteria
statistical and non statistical error
Statistical andNon-statistical Error
  • False Determinations of Toxicity
dose response evaluation
Dose Response Evaluation
  • Eliminating multiple concentrations may limit ability to evaluate spurious results.
conclusions
Conclusions
  • Same limitations as any hypothesis test
    • Implications associated with variability and “power” shifted
  • Not a magical “black box”
    • You need to be aware of the impact variability, QA/QC, and test design may have
  • May be useful for regulation
    • NPDES Permits
    • Possible use for remediation goals?
questions
Questions?

Contact info: pmarkle@lacsd.org