Re: When to do transformation of data?

From: Mats Karlsson Subject: Re: [NMusers] When to do transformation of data? Date: Mon, 29 Apr 2002 06:03:48 +0200 Hi, To get the same error structure for log-transformed data as the additive+proportional on the normal scale, I use Y=LOG(F)+SQRT(THETA(x)**2+THETA(y)**2/F**2)*EPS(1) with $SIGMA 1 FIX THETA(x) and THETA(y) will have the same meaning as on the untransformed scale with Y=F+SQRT(THETA(y)**2+THETA(x)**2*F**2)*EPS(1) with $SIGMA 1 FIX As for zero predictions with lag-time models, you would have to condition this LOG(F)-variance model. Alternatively, compared to a lag-time model, I have not seen worse behaviour with a chain of transit compartments (all with the same rate constant) and often better (lower OFV, more stable). A chain of transit compartments will not predict a zero concentration. The only drawback is sometimes longer runtimes. I usually use 3-5 compartments in the chain. If you want really lag-time like behaviour (still without zero predictions), you could increase that further. In general with log-transformation, I have found that run-times can be both considerably longer and considerably shorter than without transformation. I have not seen a pattern that allows me to make a prediction which will happen. Maybe someone has an explanation. Best regards, Mats Mats Karlsson, PhD Professor of Pharmacometrics Div. of Pharmacokinetics and Drug Therapy Dept. of Pharmaceutical Biosciences Faculty of Pharmacy Uppsala University Box 591 SE-751 24 Uppsala Sweden phone +46 18 471 4105 fax +46 18 471 4003 mats.karlsson@farmbio.uu.se