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Mohamed Alosh, Ph.D. Biostatistics Team Leader DBIII, OB, CDER, FDA

Assessing the Relationship between Success on the Investigator Global Evaluation and Acne Lesion Counts. Mohamed Alosh, Ph.D. Biostatistics Team Leader DBIII, OB, CDER, FDA. Outline. 1. Background 2. Modeling IGE Success 3. Interpretation and Assessment of Fit 4. Final Comments.

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Mohamed Alosh, Ph.D. Biostatistics Team Leader DBIII, OB, CDER, FDA

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  1. Assessing the Relationship between Success on the Investigator Global Evaluation and Acne Lesion Counts Mohamed Alosh, Ph.D. Biostatistics Team Leader DBIII,OB,CDER,FDA

  2. Outline 1. Background 2. Modeling IGE Success 3. Interpretation and Assessment of Fit 4. Final Comments

  3. Background Measures used for efficacy evaluation • Lesion counts, change or relative change from baseline (Count Data) • Investigator’s Global Evaluation (IGE) (Ordinal Data) Expect two evaluations to be related Goal: Investigate the relationship between the dichotomized IGE and lesion counts

  4. Use empirical modeling to address the following points: 1. Impact of lesion counts (or changes) on success on the IGE 2. Whether a certain type of lesion has more impact on the IGE success 3. Utility of adding baseline counts to model

  5. Where p = probability of success on IGE Xj = lesion counts (infl or non-infl; baseline or final) or change from baseline (infl or non-infl) j = the change in the log odds of success on the IGE due to an increase of 1 unit in the corresponding count or change Logistic Regression Model for Success on IGE

  6. Data Set Analyzed • 400 subjects • 12 week duration (Evaluations at baseline, Weeks 4, 8, and 12) • IGE: 0=none, 1=minimal, 2=mild, 3=moderate, 4=severe, 5=very severe • Success on IGE = none or minimal

  7. Model 1 - Final Lesion Counts Consider a stepwise approach for including covariates. Where X1=Week 12 inflammatory lesions X2 =Week 12 non-inflammatory lesions (Analysis excludes 1 outlier with influence on Hosmer-Lemeshow test.)

  8. Comments • 1 unit increase in inflammatory lesions at Week 12 implies decrease of e-0.413 = 0.662 in odds for success on IGE • 1 unit increase in non-inflammatory lesions at Week 12 implies decrease of e-0.090 = 0.914 in odds for success on IGE

  9. Comments (Cont’d) • The coefficient for infl. lesions is over 4 times that for non-infl. lesions • may be due to appearance, color, size, surrounding halo of erythema, etc. of infl. lesions • Note that when final lesion counts are given, baseline values provide no additional information for explaining IGE success

  10. Assessing the Fit of the Model Table 1: Obs. and Exp. frequencies of IGE success based on final counts (Model 1) for the Hosmer-Lemeshow test

  11. Fitted model: Model 2 - Change from Baseline Where X1 = change in infl X2 = change in non-infl X3 = baseline infl X4 = baseline non-infl (Analysis excludes 1 outlier with influence on Hosmer-Lemeshow test.)

  12. Comments • Change in lesion counts (infl. or non-infl.) do not fully explain IGE (baseline still important) • Larger reductions in infl. and non-infl. lesions increase the odds of IGE success • Increases in baseline infl. and non-infl. lesions reduce the odds of IGE success • Infl again has about 4 times the impact as non-infl

  13. Table 2: Obs. and exp. frequencies of IGE Success based on change from baseline (Model 2) for the Hosmer-Lemeshow test

  14. Final Comments • Inflammatory lesions have more impact on IGE success than non-inflammatory lesions • Absolute changes in lesion counts alone do not fully explain variability in IGE success (baseline counts still important) • Fitted model useful for checking consistency of study findings based on IGE success and lesion counts

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