Re: missing data items

From: LSheiner <lewis@c255.ucsf.edu> Subject: Re: missing data items Date: Mon, 11 Sep 2000 08:48:18 -0700 The problem is unfortunately much more complex than finding a code ... It involves the answer to the following question: What should the estimation scheme do with someone who has a missing value? This is one version of a classical statistical issue ("missing data"), for which there are many proposed solutions. For example: 1. delete all cases with missing data 2. integrate the likelihood across the missing data to use a marginal likelihood for those individuals with missing data 3. simply impute the value (e.g., set it to the population mean) 4. more sophisticatedly impute the value (from all other data values, using a regression formula derived from the complete cases) 5. multiply impute the value as in 4. Only numbers 2 and 5 are completely satisfactory, and even those require, in general, that the missingness mechanism be "ignorable." None of these approaches are implemented by default in NONMEM, so that coding the missing value would accomplish nothing. When you decide how you want to deal with the missing data, it will be possible to write your NONMEM control file to implement that decision, and when you do, you can, of course, choose any coding you want for the missing values since it will be your computer code that will recognize and deal with them. LBS. -- _/ _/ _/_/ _/_/_/ _/_/_/ Lewis B Sheiner, MD (lewis@c255.ucsf.edu) _/ _/ _/ _/_ _/_/ Professor: Lab. Med., Bioph. Sci., Med. _/ _/ _/ _/ _/ Box 0626, UCSF, SF, CA, 94143-0626 _/_/ _/_/ _/_/_/ _/ 415-476-1965 (v), 415-476-2796 (fax)