%PDF-1.7
%
236 0 obj
<>
endobj
272 0 obj
<>stream
11.0
8.5
26
PDFium
<p>The CMPTMODEL statement is a new enhancement to the NLMIXED procedure in SAS/STAT® 14.3. This statement enables you to fit a large class of pharmacokinetics (PK) models, including one-, two-, and three-compartment models, with intravenous (bolus and infusion) and extravascular (oral) types of drug administration. The CMPTMODEL statement also supports multiple dosages and PK models that have various parameterizations. This paper introduces the new statement and illustrates its usage through examples. Related concepts are also discussed, such as the %PKCONVRT autocall macro (which converts PK data sets that are stored according to industry standard to data sets that can be directly used by PROC NLMIXED), extension to Emax models, prediction, visualization, and fitting Bayesian PK models (by using the MCMC procedure).<br>
<br>
Raghavendra Rao Kurada, SAS<br>
<br>
Fang Chen, SAS<br>
<br>
Raghavendra Rao Kurada, SAS</p>
Session 1883
en
PDFium
2021-02-25T17:16:11.000-05:00
2021-02-25T17:16:11.000-05:00
2021-02-25T11:37:03.000-05:00
application/pdf
2021-02-25T17:27:25.936-05:00
The CMPTMODEL statement is a new enhancement to the NLMIXED procedure in SAS/STAT® 14.3. This statement enables you to fit a large class of pharmacokinetics (PK) models, including one-, two-, and three-compartment models, with intravenous (bolus and infusion) and extravascular (oral) types of drug administration. The CMPTMODEL statement also supports multiple dosages and PK models that have various parameterizations. This paper introduces the new statement and illustrates its usage through examples. Related concepts are also discussed, such as the %PKCONVRT autocall macro (which converts PK data sets that are stored according to industry standard to data sets that can be directly used by PROC NLMIXED), extension to Emax models, prediction, visualization, and fitting Bayesian PK models (by using the MCMC procedure).
Fitting Compartment Models Using PROC NLMIXED
uuid:8a118811-09e4-405c-88e1-e1a806c791ae
uuid:a8f11dbb-ff0d-4bab-8fc2-3528c35ca605
thirdparty
PDFium
support:sgf-papers
year:2018
industry:3150
software:STAT
event-type:180/session-type/breakout
support:skill-level/intermediate
support:sgf-papers/topic/analytics/statistics
support:customer-roles/statistician
endstream
endobj
233 0 obj
<>
endobj
237 0 obj
<>
endobj
1 0 obj
<>
endobj
4 0 obj
<>
endobj
14 0 obj
<>
endobj
17 0 obj
<>
endobj
20 0 obj
<>
endobj
27 0 obj
<>
endobj
30 0 obj
<>
endobj
33 0 obj
<>
endobj
36 0 obj
<>
endobj
39 0 obj
<>
endobj
46 0 obj
<>
endobj
53 0 obj
<>
endobj
60 0 obj
<>
endobj
67 0 obj
<>
endobj
74 0 obj
<>
endobj
81 0 obj
<>
endobj
84 0 obj
<>
endobj
88 0 obj
<>
endobj
91 0 obj
<>
endobj
95 0 obj
<>
endobj
98 0 obj
<>
endobj
101 0 obj
<>
endobj
104 0 obj
<>
endobj
107 0 obj
<>
endobj
110 0 obj
<>
endobj
111 0 obj
<>stream
xXnF}WH."+ib[r%%E6QtxI")K)wW3srZ?xqn
lus°#r(3D"+]y`w#=FeիGQFhQLߨ)'6)=cʼ6v.Tn#Ʀ'D8Dޞ{_,g-ux#Aaq'wcE(!¹P{r!܅dLS!EwJoȵN,L $G
;:}}@>͝1F!'OP0$Hԃxõ2˟m`6~(6HJbMˑBLjr:P3>X^yft6,/Edgld9#t7dt3vJb唴jBvHr2>̆h<Ç^NNYM>'hĕur0s::fNaqoadUSAy |}8\tdĊbz1V&` w
!"*b]Ǿ\}p}glԙ/wa2:vHU2x@GeXɿ- C5!b8z
:!"MB.
l6q>=}q1GzV Lh8A yݤoҮ^59\˫fcV