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User Manual - Laboratory of Applied Pharmacokinetics

1. Notes By omitting a Diffeq block with ODEs Pmetrics understands that you are specifying the model to be solved algebraically In this case at least KE and V must be in the Primary or Secondary variables KA KCP and KPC are optional and specify absorption and transfer to and from the central to a peripheral compartment respectively Pmetrics User s Guide 16 Brief Fortran Tutorial Much more detailed help is available from http www cs mtu edu shene COURSES cs201 NOTES fortran html inet operar meaa E emm rien operar Anemat operator Wem I e ma IF logical expression one statement IF T gt 100 CL 10 IF logical expression THEN IF T gt 100 THEN statements CL 10 END IF V 210 IF logical expression THEN IF T gt 100 THEN statements 1 CL 10 ELSE statements 2 END IF Pmetrics User s Guide 17 How to use R and Pmetrics Setting up a Pmetrics project When beginning a new modeling project it is convenient to use the command PMtree project name This command will set up a new directory in the current working directory named whatever you have included as the project name For example a directory called DrugX will be created by PMtree DrugX Beneath this directory several subdirectories will be also created Rscript Runs Sim and src The Rscript subdirectory will contain a skeleton R script to begin Pmetrics runs in the new project The Runs subd
2. You supply these files Pmetrics does the rest ul iu i Pmetrics User s Guide 7 General Workflow The general Pmetrics workflow for IT2B and NPAG is shown in the following diagram R is used to specify the working directory containing the data csv and model txt files Through the batch file generated by R the preparation program is compiled and executed The instruction file is generated automatically by the contents of the data and model files and by arguments to the NPrun ITrun or ERRrun commands The batch file will then compile and execute the engine file according to the instructions which will generate several output files upon completion Finally the batch file will call the R script to generate the summary report and several data objects including the ITZBout Rdata or NPAGout Rdata files which can be loaded into R subsequently using ITload or NPload Both input files data model are text files which can be edited directly Pmetrics User s Guide 8 Pmetrics Input Files Data csv Files Pmetrics accepts input as a spreadsheet matrix format It is designed for input of multiple records in a concise way Please keep the number of characters in the file name s 8 Files are in comma separated values csv format Examples of programs that can save csv files are any text editor e g TextEdit on Mac Notepad on Windows or spreadsheet program e g Excel Click on hyperlinked items to see an expl
3. This function plots first second and third order makeErrorPoly polynomial functions fitted to pairs of observations and associated standard deviations for a given output assay In this way the standard deviation associated with any observation may be calculated and used to appropriately weight that observation in the model building process Observations are weighted by the reciprocal of the variance or squared standard deviation Output of the function is a plot of the measured observations and fitted polynomial curves and a list with the first second and third order coefficients This function performs a Probability of Target Attainment nakePTA analysis for a set of simulated doses and time concentration profiles Targets e g Minimum Inhibitory Concentrations the type of target attainment i e time above target Cmax target AUC target Cmin target or Cx target where x is any time point and the success threshold e g time gt 0 7 or Cmax target gt 10 can all be specified Output is a list class PMpta with two objects e Results a 4 dimensional array with dimension size of number of doses number of targets number of simulated profiles 1 which gives the target attainment e g Cmax target for each dose target and profile Outcome For each dose and target a summary of the target attainment for all the profiles including mean standard deviation and proportion above the success threshold PMpta ob
4. Out Y 1 X 2 V Error This block contains all the information Pmetrics requires for the structure of the error model In Pmetrics each observation is weighted by 1 error There are two choices for the error term 1 error SD gamma 2 error SD lamda Note that lambda is only available in NPAG currently where SD is the standard deviation SD of each observation obs and gamma and lambda are terms to capture extra process noise related to the observation including mis specified dosing and observation times SD is modeled by a polynomial equation with up to four terms Co Ci obs C2 obs C3 obs The values for the coefficients should ideally come from the analytic lab in the form of inter run standard deviations or Pmetrics User s Guide 13 coefficients of variation at standard concentrations You can use the Pmetrics function PMerrorPoly to choose the best set of coefficients that fit the data from the laboratory Alternatively if you have no information about the assay you can use the Pmetrics function ERRrun to estimate the coefficients from the data Finally you can use a generic set of coefficients We recommend that as a start Co be set to half of the lowest concentration in the dataset and C1 be set to 0 15 C2 and C3 can be 0 In the multiplicative model gamma is a scalar on SD In general well designed and executed studies will have data with gamma values approaching 1 Poor quality
5. define them first within this block Equation syntax must be Fortran It is permissible to have conditional statements but because expressions in this block are translated into variable declarations in Fortran expressions other than of the form X function Y must be prefixed by a and contain only variables which have been previously defined in the Primary Covariate or Secondary blocks Example Sec CL Ke V wt 0 75 IF cyp GT 1 CL CL cyp Bolus inputs By default inputs with DUR duration of 0 in the data csv file are delivered instantaneously to the model compartment equal to the input number i e input 1 goes to compartment 1 input 2 goes to compartment 2 etc This can be overridden with NBOLUS input number compartment number Example HBol NBCOMP 1 2 Initial conditions By default all model compartments have zero amounts at time 0 This can be changed by specifying the compartment amount as X expression where is the compartment number Primary and secondary variables and covariates may be used in the expression as can conditional statemtents in Fortran code A prefix is not necessary in this block for any statement although if present will be ignored Example Ini X 2 IC V i e IC is a covariate with the measured trough concentration prior to an observed dose X 3 IC3 i e IC3 is a fitted amount in this unobserved compartment In the first case the initial con
6. file from the 50 cycle run and continuing with as many additional cycles as you specify in item 21 So in the example if you specify 100 cycles in 21 the total number of cycles will be 50 100 150 31 Enter 1 if you wish to save all the instructions in an instruction file If you do this you can specify this instruction file in Pmetrics by using the NPrun instr yourfile option and including yourfile in the working directory with the model txt file and the data csv file 32 Some output will print to the terminal window which contains information that you can ignore while running NPAG from Pmetrics Press 1 followed by return to begin the NPAG analysis 33 The NPAG run can complete in seconds for small populations with analytic solutions or days for large populations with complex differential equations At the end of a successful run the results will be automatically parsed and saved to the output directory Your default browser will launch with a summary of the run IT2B Runs In the past users had to run IT2B once before generating and saving an instruction file which could automate subsequent runs As of version 0 4 the instruction file is generated automatically using information in the data file the model file and arguments to ITrun However if ITrun auto F is specified then Pmetrics will allow the user to manually answer all the questions below Note the default auto T option means that this section does not ap
7. n 1 20 120 Observed Prodictod Time Weighted residual error plot PMop object plot PMop object resid T Pmetrics User s Guide 33 s Ha 3 8 a s S 2 s 3 FR g PA e A s pS 2 r 3 c B Le E 3 i i a pa 3 3 8 t PS 3 S fs 3 S oro 600 800 400 900 S00 5 E te E 3 i 8 2 Le st n 99 r8 8 z324 t5 e Q act 22898 Q 3 I 111 E Pee E H8 amp r8 E 5 EE emm NES emt cue wd Le e y e ps imal 3 mm Sst See sez SO L COL S60 060 OL ool 060 S e 3 S O 3 3 eS gt n F 3 o gt 3 m F8 8 F8 3 S a E Lg 2 ba 5 2 A E E oro 600 800 00 900 S00 F 8 8 FS a A 3 LJ 2 E 8 E e SS La i 8 i 85 f 8 3 gt o E o s o i E 5 o e 2 2 z e de pS 8 pS z 5 e332 s 2E Hl J E 3 E o 3 r T T Tpm CI a 3 u Oster Oey 0 p o os osez ort 001 oro 600 soo 400 900 S00 oro 600 900 400 900 S00 Theoretical Quantiles pd 02 04 06 08 10 Q Q plot versus U 0 1 for pd 15 Frequency 10 o 0 2 0 4 0 6 0 8 1 0 Sample quantiles pd 23 8 z PPP A eae SITS EE une y 6 3 p 8 d o o o a an oa poene a E o o 8 gt A o 120 125 130 135 140 145 Time Predicted
8. noisy data will result in gammas of 5 or more Lambda is an additive model to capture process noise rather than the multiplicative gamma model We tend to prefer lambda To specify the model in this block the first line needs to be either L number or G number for a lambda or gamma error model The number term is the starting value for lambda or gamma Good starting values for lambda are 1 times Co for good quality data 3 times Co for medium and 5 or 10 times Co for poor quality Note that Co should generally not be 0 as it represents machine noise e g HPLC or mass spectrometer that is always present For gamma good starting values are 1 for high quality data 3 for medium and 5 or 10 for poor quality If you include an exclamation point in the declaration then lambda or gamma will be fixed and not estimated Note that you can only fix lambda currently to zero The next line s contain the values for Co Ci C2 and C3 separated by commas There should be one line of coefficients for each output equation By default Pmetrics will use values for these coefficients found in the data file If none are present or if the model declaration line contains an exclamation point the values here will be used Example 1 estimated lambda starting at 0 4 one output use data file coefficients but if missing use 0 1 0 1 0 0 HErr L 0 4 0 1 0 1 0 0 Example 2 fixed gamma of 2 two outputs use data file coefficients but if missing
9. Mac zip for Windows Do not open the archive To install Pmetrics from the R console use the command install packages file choose and navigate when prompted to the folder in which you placed the Pmetrics package archive zip or tgz file Pmetrics will need the following R packages for some functions chron Defaults and R2HTML However you do not have to install these if you do not already have them in your R library They should automatically be downloaded and installed the first time you use a Pmetrics function that requires them but if something goes awry such as no internet connection or busy server you can do this manually Fortran In order to run Pmetrics a Fortran compiler is required After you have installed Pmetrics the first time you load Pmetrics into R with the function library Pmetrics the program will ask you which Fortran compiler you are using If you have no compiler you will have the option to automatically link you to the OS specific page of our website with explicit instructions and a link to download and install gfortran on your system Details of this procedure follow but are not relevant if you already have a compiler installed For Mac users the correct version of gfortran will be downloaded for your system Mountain Lion 64 bit Lion 64 bit Snow Leopard 64 or 32 bit You will also be provided a link to download and install Apple s Xcode application if you do not already have it on your system Xcod
10. MinGW w64 Runtime Snaps A Y mingw32 make MinGW Stable 3 82 5 v ce 7 gdb Stable Release 7 3 1 tdm64 1 pa META s ET Cinnt wn M ti m Li 4 m Li Download 31 2 MB Install 260 MB Download 36 7 MB Install 296 MB mk Jima conca cams Rstudio A text editor that can link to R is useful for saving scripts Both the Windows and Mac versions of R have rudimentary text editors that are stable and reliable Numerous other free and paid editors can also do the job and these can be located by searching the internet We prefer Rstudio What This Manual Is Not We assume that the user has familiarity with population modeling and R and thus this manual is not a tutorial for basic concepts and techniques in either domain We have tried to make the R code simple regular and well documented A very good free online resource for learning the basics of R can be found at http www statmethods net index html We recognize that initial use of a new software package can be complex so ul JiLu i Pmetrics User s Guide 4 please feel free to contact us at any time preferably through the Pmetrics forum at http www lapk org or directly by email at contact lapk org This manual is also not intended to be a theoretical treatise on the algorithms used in IT2B or NPAG For that the user is directed to our website at www lapk org Getting Help and Updates There is an active LAPK forum available fr
11. SD gamma This model is rarely used and is equivalent to specifying a model with Co only i e a constant error regardless of concentration Once you select the assay error model for each output equation you are then offered four more options on which assay error polynomial coefficients to use Choices 1 and 2 are the most commonly used 17 1 Choice 1 The default Use coefficients in the subject record Co Ci C2 C3 in the data csv file and if missing use the default values to be entered in the program item 18 below 17 2 Choice 2 Use the default values to be entered in the program for all subjects regardless of what is in the data csv file 17 3 Choice 3 To multiply data csv values and default entered values by a fixed gamma and use them 17 4 Choice 0 Specify coefficients on a subject by subject basis either those in the data csv file already the default values entered into the program or other values For each output equation after you have selected the option in item 17 you will be prompted to supply the required information including the general default values for missing or overridden values in the data csv file After assay error pattern and estimates are specified for all output equations enter the salt fraction of the drug usually 1 Salt fraction is the percentage of administered compound that contains active drug For example the mean salt fraction for theophylline is 0 85 This is not the same as bioav
12. be loaded by ensuring that the Runs folder is set as the working directory and then using the Pmetrics commands ITload run num or NPload run_num Both commands load their respective Rdata files into R making the contained objects available for plotting and other analysis Objects loaded by ITload run num and NPload run num op class PMop list pop1 postl Population and posterior predictions for each output equation i e 1 2 oom mmm 00000 pred Prediction based on median of population or p pop posterior parameter value distributions Pmetrics User s Guide 19 block Dosing block usually 1 unless data file contains EVID 4 dose reset events in which case each such reset within a given ID will increment the dosing block by 1 for that ID obsSD Calculated standard deviation error of the observation based on the assay error polynomial final class PMfinal list popPoints NPAG only Data frame of the final cycle joint population density of grid points with column names equal to the name of each random parameter plus prob for the associated probability of that point popMean The final cycle mean for each random parameter distribution popSD The final cycle standard deviation for each random parameter distribution popCV The final cycle coefficient of variation for each random parameter distribution popVar The final cycle variance for each random parameter distribution popCov The final
13. cycle covariance matrix for each random parameter distribution popCor The final cycle correlation matrix for each random parameter distribution popMedian The final cycle median for each random parameter distribution gridpts NPAG only The initial number of support points ab Matrix of boundaries for random parameter values For NPAG this is specified by the user prior to the run for IT2B it is calculated as a user specified multiple of the SD for the parameter value distribution ul ilu i Pmetrics User s Guide 20 cycle class PMcycle list names Vector of names of the random parameters 11 Matrix of cycle number and 2 Log likelihood at each cycle gamlam A matrix of cycle number and gamma or lambda at each cycle see item 16 under NPAG Runs below for a discussion of gamma and lambda mean A matrix of cycle number and the mean of each random parameter at each cycle normalized to initial mean sd A matrix of cycle number and the standard deviation of each random parameter at each cycle normalized to initial standard deviation median A matrix of cycle number and the median of each random parameter at each cycle normalized to initial standard deviation aic A matrix of cycle number and Akaike Information Criterion at each cycle bic A matrix of cycle number and Bayesian Schwartz Information Criterion at each cycle cov class PMcov i Subject identification data frame Covariate values for each
14. field It can be 0 1 or 4 Every row must have an entry 0 observation 1 input e g dose 2 3 are currently unused 4 reset where all compartment values are set to 0 and the time counter is reset to 0 This is useful when an individual has multiple sampling episodes that are widely spaced in time with no new information gathered This is a dose event so dose information needs to be complete TIME This is the elapsed time in decimal hours since the first event It is not currently clock time e g 21 30 although this is planned Every row must have an entry and within a given ID rows must be sorted chronologically earliest to latest DUR This is the duration of an infusion in hours If EVID 1 there must be an entry otherwise it is ignored For a bolus i e an oral dose set the value equal to 0 Pmetrics User s Guide 9 DOSE This is the dose amount If EVID 1 there must be an entry otherwise it is ignored ADDL This specifies the number of additional doses to give at interval II It may be missing for dose events EVID 1 or 4 in which case it is assumed to be 0 It is ignored for observation EVID 0 events Be sure to adjust the time entry for the subsequent row if necessary to account for the extra doses If set to 1 the dose is assumed to be given under steady state conditions ADDL 1 can only be used for the first dose event for a given subject or an EVID 4 event as you cannot suddenly be at steady state in
15. file in the working directory The structure of these files is identical to those used by IT2B and NPAG The data csv contains the template dosing and observation history as well as any covariates Observation values the OUT column for EVID 0 events can be any number they will be replaced with the simulated values You can have any number of subject records within a data csv file each with its own covariates if applicable Each subject will cause the simulator to run one time generating as many simulated profiles as you specify from each template subject This is controlled from the SIMrun command with the include and nsim arguments The first specifies which subjects in the data csv file will serve as templates for simulation The second specifies how many profiles are to be generated from each included subject Simulation from a non parametric prior distribution from NPAG can be done in one of two ways The first is simply to take the mean standard deviation and covariance matrix of the distribution and perform a standard Monte Carlo simulation The second way is what we call semi parametric and was devised by Goutelle et al In this method the non parametric support points in the population model each a vector of one value for each parameter in the model and the associated probability of that set of parameter values serve as the mean of one multi variate normal distribution in a multi modal multi variate joint distribution Th
16. in 13 You now have the option to specify the standard deviation for the parameter value distribution which by default is half of the range in 413 Choose 1 to accept this the usual answer or 0 to change it to something else expressed as a multiple of the range In IT2B the standard deviation SD of the observation obs is modeled by a polynomial equation with up to four terms Co Ci obs C2 obs Cs obs You will specify the coefficients Co C1 C2 and C3 You can now choose 1 if every subject has the same coefficients or 0 to use a unique set of coefficients for each subject The first case is the more usual when all samples from all subjects are analyzed in the same lab If samples are analyzed in different labs and you have the assay data from each lab then you would enter 0 This information should ideally come from the analytic lab in the form of inter run standard deviations or coefficients of variation at standard concentrations You can use the Pmetrics function PMerrorPoly to choose the best set of coefficients that fit the data from the laboratory Alternatively if you have no information about the assay you can use the Pmetrics function ERRrun to estimate the coefficients from the data see 15 1 3 below Finally you can use a generic set of coefficients We recommend that as a start Co be set to half of the lowest concentration in the dataset and C be set to 0 15 C2 and C3 can be 0 15 1 If you choose 1 one
17. set of coefficients for all subjects you will then be presented with 3 additional choices 15 1 1 Choice 1 Gamma is a scalar to capture additional process noise related to the observation including mis specified dosing and observation times In general well designed and executed studies will have data with gamma values approaching 1 Poor quality noisy data will result in gammas of 5 or more Choose this option if you wish to fix the assay error coefficients to values either in the data csv file or as specified in item 16 and to fix gamma to 1 15 1 2 Choice 2 Choose this option if you wish to fix the assay error coefficients to values either in the data csv file or as specified in item 16 but to estimate gamma based on the data This is the usual option 15 1 3 Choice 3 Choose this option if you wish to estimate the assay error coefficients based on your data for use in future runs Although you can access this option by using either ITrun or ERRrun in R the instruction files that you save and the generated output files will be different Therefore we recommend that if you intend to choose this option use ERRrun in R which will generate an ASS0001 file that contains the estimates for Co C1 C2 and C3 You can then include this file in the working directory along with a model txt file and a data csv file to do an IT2B run supplying the file name in 16 1 below Pmetrics User s Guide 29 16 17
18. supplied answers to the following questions What data input format will you use The standard format is the matrix block csv file so the answer should be 1 Working copy files are an older format The csv file is actually converted to these wrk files one file per subject prior to an IT2B run However some function will be lost in the Pmetrics package by using wrk input directly without a csv file such as the ability to plot raw subject data via the plot PMmatrix function Enter the name of your csv file now Enter the total number of unique subjects defined by ID in the csv file How many of the total number do you want to analyze Enter 1 if you want to analyze all of them 0 if you want to analyze a subset 11 1 If you entered 0 you will then choose 1 to include specific subjects or 2 to exclude specific subjects 11 2 Enter the inclusion or exclusion subject numbers not IDs in order using a combination of numbers hyphens and commas For example 1 3 5 7 10 Press return and then enter 0 to conclude entry The program will then open the csv file and read the number of output equations reporting each subject as it is read This can take some time if it is a very large dataset Enter the initial boundary values for the random parameters in the model in the form min max followed by return The estimated mean for each parameter value distribution during the first iteration will be the median of the range specified
19. 18 19 20 21 22 23 24 25 26 27 15 2 If you choose 0 unique coefficients for each subject you will be presented with two choices 15 2 1 Choice 1 Fix gamma to 1 See the discussion above in 15 1 1 15 2 2 Choice 2 Estimate gamma based on the data 15 2 3 You now need to specify where to obtain the values for Co Ci C2 C3 either from the data csv file and from the entry in 16 Choice 1 or on an individual basis during the IT2B run1 Choice 0 Enter the values for Co Ci C2 C3 that will be used for all patients who do not have values associated with them in the data csv file 16 1 You have the option of entering a file name that contains the output of a previous estimation generated by choosing 3 in 15 1 3 above Usually this file will be called ASS0001 and it must be in the working directory After assay error pattern and estimates are specified for all output equations enter the salt fraction of the drug usually 1 Salt fraction is the percentage of administered compound that contains active drug For example the mean salt fraction for theophylline is 0 85 This is not the same as bioavailability which is the fraction of drug absorbed after non parenteral administration e g oral compared to intravenous administration Enter the convergence criterion When the difference between log likelihoods of successive iterations is less than or equal to this criterion IT2B will converge and term
20. A dau eccevesvecwsuesssuswcoscceccaesearceec 25 Wire H M 28 Simulator ON 31 A Keno 31 Examples of Pmetrics plots 5 ees etie opponat a ot iii rra 33 Model DI3gitOsti6s cuoi anie a A 36 Internal CICERO 36 External Val ation PP OP yq 37 A 38 Pmetrics User s Guide 2 Introduction Thank you for your interest in Pmetrics This guide provides instructions and examples to assist users of the Pmetrics R package by the Laboratory of Applied Pharmacokinetics at the University of Southern California Please see our website at http www lapk org for more information Here are some tips for using this guide Items that are hyperlinked can be selected to jump rapidly to relevant sections At the bottom of every page the text User s Guide can be selected to jump immediately to the table of contents Items in courier font correspond to R commands Citing Pmetrics Please help us maintain our funding to provide Pmetrics as a free research tool to the pharmacometric community If you use Pmetrics in a publication you can cite it as below In R you can always type citation Pmetrics to get this same reference Neely MN van Guilder MG Yamada WM Schumitzky A Jelliffe RW Accurate Detection of Outliers and Subpopulations With Pme
21. Covariates Covariates are subject specific data such as body weight contained in the data csv file The covariate names which are the column names in the data file can be included here for use in secondary variable equations The order should be the same as in the data file and although the names do not have to be the same we strongly encourage you to make them the same to avoid confusion Covariates are applied at each dose event The first dose event for each subject must have a value for every covariate in the data file By default missing covariate values for subsequent dose events are linearly interpolated between existing values or carried forward if the first value is the only non missing entry To suppress interpolation and carry forward the previous value in a piece wise constant fashion include an exclamation point 1 in any declaration line Note that any covariate relationship to any parameter may be described as the user wishes by mathematical equations and Fortran code allowing for exploration of complex non linear time dependent and or conditional relationships Example HCov wt Cyp IC Pmetrics User s Guide 11 where IC will be piece wise constant and the other two will be linearly interpolated for missing values Secondary variables Secondary variables are those that are defined by equations that are combinations of primary covariates and other secondary variables If using other secondary variables
22. GUI R is listening and no other activity is possible The GUI is designed to generate Pmetrics R code in response to your input in a friendly intuitive environment That code can be copied and pasted into your Pmetrics R script You can also see live plot previews with the GUI All this is made possible with the shiny package for R Currently the following GUIs are available PMcode NPrun PMcode ITrun PMcode plot More are coming Pmetrics Components There are three main software programs that Pmetrics controls IT2B is the ITerative 2 stage Bayesian parametric population PK modeling program It is generally used to estimate parameter ranges to pass to NPAG It will estimate values for population model parameters under the assumption that the underlying distributions of those values are normal or transformed to normal e NPAG is the Non parametric Adaptive Grid software It will create a non parametric population model consisting of discrete support points each with a set of estimates for all parameters in the model plus an associated probability weight of that set of estimates There can be at most one point for each subject in the study population There is no need for any assumption about the underlying distribution of model parameter values The simulator is a semi parametric Monte Carlo simulation software program that can use the output of IT2B or NPAG to build randomly generated response profiles e g time conce
23. Observation plot PMdiag object 0 95 3 o 8j 05 Time h plot PMsim object Pmetrics User s Guide 35 a as Q o o pa 17 2 58 9 z re z E tt s GO z c Q t o a Ww 9 o a ej o MIC plot PMpta object 1 Model Diagnostics Internal Validation Several tools are available in Pmetrics to assist with model selection The simplest methods are using PMcompare and plot PMop viathe plot command for a PMop object made by makeOP or by using NPload or ITload after a successful run PMstep is another option for covariate analysis All these functions are carefully documented within R and accessible using the command orhelp command syntax To compare models with PMcompare simply enter a list of two or more PMetrics data objects These should be of the NPAG or IT2B class made either by using NPload ITload or NPparse ITparse Although it is possible to compare models of mixed classes the validity of this is dubious The return object will be a data frame with summaries of each model and key metrics such as log likelihood final cycle Akaike Information and Bayesian Information Criteria and root mean squared errors RMSE for observed vs predictions from the population prior distribution and individual posterior distributions By specifying the option plot T observed vs predicted plots for all the models will be generated The opt
24. Pmetrics User Manual April 2013 Package Version 1 0 0 An R package for parametric and non parametric modeling and simulation of pharmacokinetic and pharmacodynamic systems I LAPK Laboratory of Applied Pharmacokinetics Se USC University of UY Southern California Table of Contents A 3 Citing C 3 PIECE UT PIM 3 System Requirements and Installation e ele eee eee eee eene eee eee eene eene nn eene nennen nnne seen s nnn sonne n essa nnne nen 3 What This Manual IS N tr O sss nays ENESES EE ESFE EEEa rakner esea 4 Getting Help and Updates mm 5 Pmetrics Components doses trece ta ee oet ee erba ein asenaan 5 Customizing Pmetr