Bioequivalence and Bioavailability Forum

LSM limbo [General Sta­tis­tics]

posted by Helmut  – Vienna, Austria, 2014-07-31 04:54 (3885 d 21:42 ago) – Posting: # 13322
Views: 24,666

Dear Ohlbe,

❝ How do you calculate the LSM in a full replicate design study (RTRT and TRTR, with some subjects missing here and there) ?

Manually? Never done that. If you are thinking about RSABE (in their FDA and EMA flavors), that’s a strange cup of tea. To quote Patterson & Jones¹:

[…] simulation findings support that if a subject is missing both reference ob­ser­vations or the test observation, no bias is introduced.
If one R observation is missing, then MoM estimates of δ and σ²_l are biased by period effects if they are present. It should be noted that period effects are known to occur in such designs.
If SABE is applied, subjects with one missing R observation should be eliminated from a MoM analysis until an alternative statistical procedure is available. This is un­precedented in our expe­ri­ence in a regulated bioequivalence setting. Traditionally, one does not exclude data unless there is a scientifically or clinically valid reason to do so. However, with the current draft guidance from FDA for progesterone bio­equi­valence, this appears to be the immediate approach to be applied for SABE. A better statistical alternative is restricted maximum likelihood estimation (to eliminate bias) with the use of the bootstrap for inference.

It’s a complete mess. Subjects with incomplete data are excluded from the estimation of σ²_WR, but kept in the calculation of the upper 95% boundary (FDA) or excluded for the calculation of the 90% CI (EMA, all fixed effects. A mixed-effects model is not acceptable for EMA). To be honest, currently there are so many flaws in these methods that I would not bother to calculate anything by hand. Accept what you get from the FDA’s and EMA’s SAS-code (or alternative software) and set this question aside until somebody finds a method to deal with the more important question: How to prevent inflation^1,2 of the patient’s risk (according to my latest simulations: EMA up to 9.6% and FDA up to 22.5%). This is a nightmare.

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1. Patterson SD and B Jones
   Viewpoint: observations on scaled average bioequivalence
   Pharm Stat 11(1), 1–7 (2011)
   DOI: 10.1002/pst.498
   
2. Wonnemann M, Frömke C, and A Koch
   Inflation of the Type I Error: Investigations on Regulatory Recommendations for Bioequivalence of Highly Variable Drugs
   Pharm Res 31 (preprint published 18 July 2014)
   DOI: 10.1007/s11095-014-1450-z

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна! 
Helmut Schütz


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Science Quotes

Complete thread:

• Confidence Interval for Transformed data Unbalanced study usfda_emea 2007-03-06 14:39 [General Sta­tis­tics] 
  • CI for Transformed data Unbalanced study Helmut 2007-03-06 16:46
    • CI for Transformed data Unbalanced study drshiv 2007-03-06 18:49
    • CI for Transformed data Unbalanced study usfda_emea 2007-03-07 06:21
      • CI for Transformed data Unbalanced study Helmut 2007-03-07 12:04
        • CI for Transformed data Unbalanced study usfda_emea 2007-03-07 12:34
        • CI for Transformed data Unbalanced study Ohlbe 2014-07-30 15:04
          • LSM limboHelmut 2014-07-31 02:54
            • LSM limbo Ohlbe 2014-07-31 09:47
            • 90% CI limbo VStus 2017-03-01 14:07
              • RSABE ⇒ ABEL Helmut 2017-03-03 13:34
                • RSABE ⇒ ABEL VStus 2017-03-03 15:31
                  • s2wR != mse, FDA != EMA d_labes 2017-03-04 14:49
                    • s2wR != mse, FDA != EMA ElMaestro 2017-03-04 19:13
                      • s2wR from ISC in FDA approach d_labes 2017-03-05 12:18
                        • s2wR from ISC in FDA approach ElMaestro 2017-03-05 13:50
                    • s2wR != mse, FDA != EMA VStus 2017-03-04 21:41
                      • s2wR != mse, FDA != EMA Helmut 2017-03-06 13:40