Altria Group, Inc. · NYSE:MO
Altria — Technical Presentation (September 2018)
20 slides from Altria's technical presentation, Sep 18, 2018
Slide 1: Temporal variability of analytical testing for e-vapor products and impact on number of replicates
Temporal variability of analytical testing for e-vapor products and impact on number of replicates Michael Morton, William Gardner, Kimberly Agnew-Heard, John Miller Presentation #104 Altria Client Services l Michael J. Morton l September 18, 2018 l 2018 TSRC Memphis, TN l 1
Slide 2: Testing for E-Vapor Products
Testing for E-Vapor Products FDA/CTP PMTA ENDS draft guidance recommends that testing should be based on three different batches with a minimum of 10 replicates per batch. The reason for doing replicates is to improve the precision of the resulting estimated values. However temporal variabilit
Slide 3: Laboratory Variability
Laboratory Variability Variability using the same laboratory, same operator, same equipment, same materials over the shortest practical period of time is called repeatability Variability using different laboratories and implicitly different operators, different equipment, different materials is
Slide 4: Illustration of Temporal Variability
Illustration of Temporal Variability Sample-to-sample variation much larger than within sample standard error Each data point represents 3 replicates and is plotted as Mean ± 2*SE Shown in: FDA CTP Tobacco Product Analysis Scientific Public Workshop, April 12, 2012 Altria Client Services l Michael J
Slide 5: Temporal Variability and Reproducibility
Temporal Variability and Reproducibility Over a long span of time, temporal variability within a lab often approximates the lab-to-lab variation seen in collaborative studies Temporal variability within lab Collaborative study results NNK smoke yield (ng/cig) of 2R4F NNK smoke yield (ng/cig) of 3R
Slide 6: Effects of Temporal Variability on Uncertainty
Effects of Temporal Variability on Uncertainty The (naïve) expectation for the uncertainty associated with replicate testing could 𝜎𝑒2 𝜎𝑒 be 𝜎𝑦 = 𝑛= 𝑛 • The uncertainty appears to get quite small with replicate testing – but only by ignoring temporal variation (method instability) When…
Slide 7: Confidence intervals for mean with or without temporal variation
Confidence intervals for mean with or without temporal variation Includes temporal variation Ignores temporal variation 𝜎𝑇 =11.4 ng/cigarette Altria Client Services l Michael J. Morton l September 18, 2018 l 2018 TSRC Memphis, TN l 7
Slide 8: Temporal Variation Giving Apparent Differences in E-vapor Liquid
Temporal Variation Giving Apparent Differences in E-vapor Liquid Altria Client Services l Michael J. Morton l September 18, 2018 l 2018 TSRC Memphis, TN l 8
Slide 9: What affects the utility of additional reps?
What affects the utility of additional reps? The ratio of the temporal variation to rep-to-rep variation determines the utility of additional replicates • The larger the temporal variation is a proportion of the rep- to-rep variation, the less useful are additional replicates Altria Client Service
Slide 10: How to estimate temporal variability?
How to estimate temporal variability? When available, the variance components can often be estimated using long-term QC data in the lab Alternatively, the variance components coming from collaborative studies can approximate the temporal variation Altria Client Services l Michael J. Morton l Sep
Slide 11: E-vapor Products Nicotine in aerosol
E-vapor Products Nicotine in aerosol CORESTA E-vapour Sub-Group 2015 collaborative study • Lab-to-lab variation of nicotine was 9.8% of the mean. • Rep-to-rep standard deviation of nicotine averaged 26.5% of the mean. Separate testing of Nu Mark products has shown rep-to-rep standard deviation o
Slide 12: Estimated Effect of Additional Replicates for Nicotine in aerosol
Estimated Effect of Additional Replicates for Nicotine in aerosol* Variability observed in CORESTA collaborative ~5-8 reps sufficient Variability observed with Nu Mark product testing ~3 to 5 reps sufficient * Temporal variation estimated to be 9.8% of mean Altria Client Services l Michael J. Morton
Slide 13: Formaldehyde results
Formaldehyde results How to treat the occasional values spiking high? Altria Client Services l Michael J. Morton l September 18, 2018 l 2018 TSRC Memphis, TN l 13
Slide 14: E-vapor Products − Formaldehyde in Aerosol
E-vapor Products − Formaldehyde in Aerosol To date there have not been collaborative studies on carbonyls such as formaldehyde in e-vapor aerosol As a first approximation use collaborative study results in cigarette smoke. • Lab-to-lab standard deviation for formaldehyde is estimated to be 29% o
Slide 15: Estimated Effect of Additional Replicates for formaldehyde in aerosol
Estimated Effect of Additional Replicates for formaldehyde in aerosol* 60% raw rep-to-rep standard deviation 41% rep-to-rep standard deviation from robust SD estimate Little additional improvement after ~4 or 5 reps * Temporal standard deviation estimated to be 29% of mean Altria Client Services l M
Slide 16: Analysis: I can just do a t-test, right?
Analysis: I can just do a t-test, right? Many common statistical techniques (such as a two-sample t-test or one-way analysis of variance) make the implicit assumption that there is no temporal variability in analytical methods Temporal variability causes the standard statistical tests to give mi
Slide 17: Probability of t-test finding a difference when there is none
Probability of t-test finding a difference when there is none Sigma T is the temporal variability standard deviation Sigma e is the rep-to-rep standard deviation Calculated by simulation, assuming 10 reps per group Altria Client Services l Michael J. Morton l September 18, 2018 l 2018 TSRC Memphis,
Slide 18: Analytical Alternatives
Analytical Alternatives If stability samples can be stored in way that keeps them from changing, all time points can (theoretically) be analyzed at the same time and temporal variability avoided • I.e., stabilize samples at time 0, 3 months, 6 months, etc., then analyze them all of them at the end
Slide 19: Summary
Summary Temporal variability is inevitable with any analytical method. In the presence of temporal variability, comparing test results tested at different time points is difficult and requires the temporal variability to be taken into account When comparisons are made from testing at different
Slide 20: References
References Michael Morton, “Variability Observed when Analyzing Reference Materials for Tobacco Specific Nitrosamines (TSNAs),” FDA CTP Tobacco Product Analysis Scientific Public Workshop, April 12, 2012. CORESTA E-Vapour Sub-Group Technical Report – 2015 Collaborative Study for Determination of
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