[NMusers] Sparse (pediatric) and rich (adult) data
Hi, I am working on a pop PK model to estimate PK parameters in pediatric and adult patients. Pediatric study (n=20, age 6 yrs) has fewer samples (3) per subject whereas the adult study (n=50, median age 20 yrs) has 12 samples per subject. A two-compartment model best describes the data for each data set. Although a two-compartment model best describes the combined data, the individual parameter estimates in pediatric population are different compared to those obtained using with pediatric data alone. Note that the parameter estimates in adults were not significantly altered with either combined or adult data alone. Body weight is the only covariate included in the model with allometric exponents fixed to 0.75 on CL and 1 on V1. I would like to hear your thoughts on this and any suggestions on how to proceed with modeling combined data from pediatric and adult studies. Regards, - Chandra
Re: [NMusers] Sparse (pediatric) and rich (adult) data
Chandra, Pediatric data alone may not be able to support (with 3 samples per patient) a two compartment model. So combined adult/pediatric model is more appropriate. You may also want to scale peripheral compartment parameters (Q as CL, V2 as V, K12and K21 as CL/V ~ 1/WT^0.25). Remaining dependence of CL on WT (if any is noticeable) for very young kids could be attributed to maturation and explained by AGE covariate Leonid -- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web:www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel:(301) 767 5566 Chandrasekhar Udata wrote: Hi, I am working on a pop PK model to estimate PK parameters in pediatric and adult patients. Pediatric study (n=20, age 6 yrs) has fewer samples (3) per subject whereas the adult study (n=50, median age 20 yrs) has 12 samples per subject. A two-compartment model best describes the data for each data set. Although a two-compartment model best describes the combined data, the individual parameter estimates in pediatric population are different compared to those obtained using with pediatric data alone. Note that the parameter estimates in adults were not significantly altered with either combined or adult data alone. Body weight is the only covariate included in the model with allometric exponents fixed to 0.75 on CL and 1 on V1. I would like to hear your thoughts on this and any suggestions on how to proceed with modeling combined data from pediatric and adult studies. Regards, - Chandra
Re: [NMusers] Sparse (pediatric) and rich (adult) data
Chandra, With such a small sample its hard to learn much about differences between adults and children. Your principled approach using allometric scaling is a reasonable way to bridge the gap in recognizing that adults and children are all the same species (see reference below). Children are just small adults I would not be too worried about individual parameter estimate in children being different. With only 3 samples per child and a 2 cmt model requiring at least 4 parameters you will always get different results if you use different assumptions. Nick Anderson BJ, Holford NH. Mechanism-Based Concepts of Size and Maturity in Pharmacokinetics. Annu Rev Pharmacol Toxicol. 2008;48:303-32. Chandrasekhar Udata wrote: Hi, I am working on a pop PK model to estimate PK parameters in pediatric and adult patients. Pediatric study (n=20, age 6 yrs) has fewer samples (3) per subject whereas the adult study (n=50, median age 20 yrs) has 12 samples per subject. A two-compartment model best describes the data for each data set. Although a two-compartment model best describes the combined data, the individual parameter estimates in pediatric population are different compared to those obtained using with pediatric data alone. Note that the parameter estimates in adults were not significantly altered with either combined or adult data alone. Body weight is the only covariate included in the model with allometric exponents fixed to 0.75 on CL and 1 on V1. I would like to hear your thoughts on this and any suggestions on how to proceed with modeling combined data from pediatric and adult studies. Regards, - Chandra -- Nick Holford, Dept Pharmacology Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand [EMAIL PROTECTED] tel:+64(9)373-7599x86730 fax:+64(9)373-7090 www.health.auckland.ac.nz/pharmacology/staff/nholford
Re: [NMusers] Sparse (pediatric) and rich (adult) data
Thank you Nick and Leonid for your comments. Follow-up question: I do understand that 3 samples per subject may not support 4 parameters model. However, historically, the compound showed bi-phasic characteristics (in adults) and I do like to use the same model in pediatrics. Also, the model (ADVAN3, TRANS4) did converge with no issues/errors (with pediatric data alone). Is there something I am missing? or is TRANS5 (AOB, ALPHA, BETA) an alternative for such limited data? Regards, - Chandra Nick Holford [EMAIL PROTECTED] 5/28/2008 1:38:07 PM Chandra, With such a small sample its hard to learn much about differences between adults and children. Your principled approach using allometric scaling is a reasonable way to bridge the gap in recognizing that adults and children are all the same species (see reference below). Children are just small adults I would not be too worried about individual parameter estimate in children being different. With only 3 samples per child and a 2 cmt model requiring at least 4 parameters you will always get different results if you use different assumptions. Nick Anderson BJ, Holford NH. Mechanism-Based Concepts of Size and Maturity in Pharmacokinetics. Annu Rev Pharmacol Toxicol. 2008;48:303-32. Chandrasekhar Udata wrote: Hi, I am working on a pop PK model to estimate PK parameters in pediatric and adult patients. Pediatric study (n=20, age 6 yrs) has fewer samples (3) per subject whereas the adult study (n=50, median age 20 yrs) has 12 samples per subject. A two-compartment model best describes the data for each data set. Although a two-compartment model best describes the combined data, the individual parameter estimates in pediatric population are different compared to those obtained using with pediatric data alone. Note that the parameter estimates in adults were not significantly altered with either combined or adult data alone. Body weight is the only covariate included in the model with allometric exponents fixed to 0.75 on CL and 1 on V1. I would like to hear your thoughts on this and any suggestions on how to proceed with modeling combined data from pediatric and adult studies. Regards, - Chandra -- Nick Holford, Dept Pharmacology Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand [EMAIL PROTECTED] tel:+64(9)373-7599x86730 fax:+64(9)373-7090 www.health.auckland.ac.nz/pharmacology/staff/nholford
RE: [NMusers] Sparse (pediatric) and rich (adult) data
Chandra, Nick et al It is worth noting that while three samples won't support a 4 parameter model if all patients contribute these samples at exactly the same time (i.e. the patients are exchangeable from a design perspective) this is not necessarily the case if the design is optimized to learn about the PK. We have designed and conducted a number of studies where the number of samples is less than the number of parameters and achieved good results. Some of the issues that you need to consider are: 1) Your design will probably lead to some shrinkage in the empirical Bayes estimates which may be problematic if you intend to use the EBEs for inferential purposes. However if you're after the population estimates only (which is often the case) then this is not an issue. 2) Your design is unbalanced with respect to covariates. Adults are providing much more information about the model and parameter values than the children (even if the design in children was optimized) - which will affect your ability to identify some covariate relationships with accuracy. This can be assessed relatively easily using both optimal design and simulation based investigations. Regards Steve -- Professor Stephen Duffull Chair of Clinical Pharmacy School of Pharmacy University of Otago PO Box 913 Dunedin New Zealand E: [EMAIL PROTECTED] P: +64 3 479 5044 F: +64 3 479 7034 Design software: www.winpopt.com -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Chandrasekhar Udata Sent: Thursday, 29 May 2008 9:16 a.m. To: nmusers@globomaxnm.com Subject: Re: [NMusers] Sparse (pediatric) and rich (adult) data Thank you Nick and Leonid for your comments. Follow-up question: I do understand that 3 samples per subject may not support 4 parameters model. However, historically, the compound showed bi-phasic characteristics (in adults) and I do like to use the same model in pediatrics. Also, the model (ADVAN3, TRANS4) did converge with no issues/errors (with pediatric data alone). Is there something I am missing? or is TRANS5 (AOB, ALPHA, BETA) an alternative for such limited data? Regards, - Chandra Nick Holford [EMAIL PROTECTED] 5/28/2008 1:38:07 PM Chandra, With such a small sample its hard to learn much about differences between adults and children. Your principled approach using allometric scaling is a reasonable way to bridge the gap in recognizing that adults and children are all the same species (see reference below). Children are just small adults I would not be too worried about individual parameter estimate in children being different. With only 3 samples per child and a 2 cmt model requiring at least 4 parameters you will always get different results if you use different assumptions. Nick Anderson BJ, Holford NH. Mechanism-Based Concepts of Size and Maturity in Pharmacokinetics. Annu Rev Pharmacol Toxicol. 2008;48:303-32. Chandrasekhar Udata wrote: Hi, I am working on a pop PK model to estimate PK parameters in pediatric and adult patients. Pediatric study (n=20, age 6 yrs) has fewer samples (3) per subject whereas the adult study (n=50, median age 20 yrs) has 12 samples per subject. A two-compartment model best describes the data for each data set. Although a two-compartment model best describes the combined data, the individual parameter estimates in pediatric population are different compared to those obtained using with pediatric data alone. Note that the parameter estimates in adults were not significantly altered with either combined or adult data alone. Body weight is the only covariate included in the model with allometric exponents fixed to 0.75 on CL and 1 on V1. I would like to hear your thoughts on this and any suggestions on how to proceed with modeling combined data from pediatric and adult studies. Regards, - Chandra -- Nick Holford, Dept Pharmacology Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand [EMAIL PROTECTED] tel:+64(9)373-7599x86730 fax:+64(9)373-7090 www.health.auckland.ac.nz/pharmacology/staff/nholford
Re: [NMusers] Sparse (pediatric) and rich (adult) data
Steve, I hope that you do not dispute that in this particular case you need to use adult data (50 full profiles) rather than discard them and use only kids data (3 sample per subject, 20 subjects)? While optimal design can be used to extract more information from the same number of samples, it is not a substitute for the real data. Even with optimal design of the pediatric study (with the same 20 subjects, 3 optimal sample points) I bet you would gain by using adult data as well. Leonid -- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web:www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel:(301) 767 5566 Stephen Duffull wrote: Chandra, Nick et al It is worth noting that while three samples won't support a 4 parameter model if all patients contribute these samples at exactly the same time (i.e. the patients are exchangeable from a design perspective) this is not necessarily the case if the design is optimized to learn about the PK. We have designed and conducted a number of studies where the number of samples is less than the number of parameters and achieved good results. Some of the issues that you need to consider are: 1) Your design will probably lead to some shrinkage in the empirical Bayes estimates which may be problematic if you intend to use the EBEs for inferential purposes. However if you're after the population estimates only (which is often the case) then this is not an issue. 2) Your design is unbalanced with respect to covariates. Adults are providing much more information about the model and parameter values than the children (even if the design in children was optimized) - which will affect your ability to identify some covariate relationships with accuracy. This can be assessed relatively easily using both optimal design and simulation based investigations. Regards Steve -- Professor Stephen Duffull Chair of Clinical Pharmacy School of Pharmacy University of Otago PO Box 913 Dunedin New Zealand E: [EMAIL PROTECTED] P: +64 3 479 5044 F: +64 3 479 7034 Design software: www.winpopt.com -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Chandrasekhar Udata Sent: Thursday, 29 May 2008 9:16 a.m. To: nmusers@globomaxnm.com Subject: Re: [NMusers] Sparse (pediatric) and rich (adult) data Thank you Nick and Leonid for your comments. Follow-up question: I do understand that 3 samples per subject may not support 4 parameters model. However, historically, the compound showed bi-phasic characteristics (in adults) and I do like to use the same model in pediatrics. Also, the model (ADVAN3, TRANS4) did converge with no issues/errors (with pediatric data alone). Is there something I am missing? or is TRANS5 (AOB, ALPHA, BETA) an alternative for such limited data? Regards, - Chandra Nick Holford [EMAIL PROTECTED] 5/28/2008 1:38:07 PM Chandra, With such a small sample its hard to learn much about differences between adults and children. Your principled approach using allometric scaling is a reasonable way to bridge the gap in recognizing that adults and children are all the same species (see reference below). Children are just small adults I would not be too worried about individual parameter estimate in children being different. With only 3 samples per child and a 2 cmt model requiring at least 4 parameters you will always get different results if you use different assumptions. Nick Anderson BJ, Holford NH. Mechanism-Based Concepts of Size and Maturity in Pharmacokinetics. Annu Rev Pharmacol Toxicol. 2008;48:303-32. Chandrasekhar Udata wrote: Hi, I am working on a pop PK model to estimate PK parameters in pediatric and adult patients. Pediatric study (n=20, age 6 yrs) has fewer samples (3) per subject whereas the adult study (n=50, median age 20 yrs) has 12 samples per subject. A two-compartment model best describes the data for each data set. Although a two-compartment model best describes the combined data, the individual parameter estimates in pediatric population are different compared to those obtained using with pediatric data alone. Note that the parameter estimates in adults were not significantly altered with either combined or adult data alone. Body weight is the only covariate included in the model with allometric exponents fixed to 0.75 on CL and 1 on V1. I would like to hear your thoughts on this and any suggestions on how to proceed with modeling combined data from pediatric and adult studies. Regards, - Chandra -- Nick Holford, Dept Pharmacology Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand [EMAIL PROTECTED] tel:+64(9)373-7599x86730 fax:+64(9)373-7090 www.health.auckland.ac.nz/pharmacology/staff/nholford
RE: [NMusers] Sparse (pediatric) and rich (adult) data
Leonid I hope that you do not dispute that in this particular case you need to use adult data (50 full profiles) rather than discard them and use only kids data (3 sample per subject, 20 subjects)? I definitely do not dispute the need to have both adult and paediatric data in the analysis (so I agree :-) ). I see two reasons for this (perhaps more if I took more time). The first and most important reason is combining adult and paediatric data together is a great (only) way to learn how children differ pharmacokinetically from adults and how doses can be scaled to achieve equivalent exposures. Secondly, especially in this case, it is often helpful to combine data sets together to improve the informativeness of the overall design. This latter point, however was the point of my previous email. Some care must be taken to assess the accuracy of covariate effects given the unbalanced nature of the design. While optimal design can be used to extract more information from the same number of samples, it is not a substitute for the real data. Even with optimal design of the pediatric study (with the same 20 subjects, 3 optimal sample points) I bet you would gain by using adult data as well. You always gain by summing over data (unless the new data is negatively informative which is unlikely in any PK situation). So I don't exactly follow your point. The question to me is simply, what chance do I have of identifying a model that allows me to draw appropriately accurate conclusions. Optimal design is a way that allows investigators to improve the informativeness of data. Obviously, no data = no information. Steve -- Professor Stephen Duffull Chair of Clinical Pharmacy School of Pharmacy University of Otago PO Box 913 Dunedin New Zealand E: [EMAIL PROTECTED] P: +64 3 479 5044 F: +64 3 479 7034 Design software: www.winpopt.com
RE: [NMusers] Sparse (pediatric) and rich (adult) data
Leonid The original question was whether it is beneficial to add adult data to the pediatric (in the specific case under study). Your previous e-mail could be interpreted as the suggestion that one can estimate model parameters with the pediatric data alone if the pediatric study design is optimal. My previous email was more aimed at the comments about having fewer observations than parameters to estimate. I think one should use both datasets together (in the specific case that was described) and it looks like we are in agreement on this point. Yes we are. Steve --
Re: [NMusers] Sparse (pediatric) and rich (adult) data
Steve, The original question was whether it is beneficial to add adult data to the pediatric (in the specific case under study). Your previous e-mail could be interpreted as the suggestion that one can estimate model parameters with the pediatric data alone if the pediatric study design is optimal. I think one should use both datasets together (in the specific case that was described) and it looks like we are in agreement on this point. Regards, Leonid -- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web:www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel:(301) 767 5566 Stephen Duffull wrote: Leonid I hope that you do not dispute that in this particular case you need to use adult data (50 full profiles) rather than discard them and use only kids data (3 sample per subject, 20 subjects)? I definitely do not dispute the need to have both adult and paediatric data in the analysis (so I agree :-) ). I see two reasons for this (perhaps more if I took more time). The first and most important reason is combining adult and paediatric data together is a great (only) way to learn how children differ pharmacokinetically from adults and how doses can be scaled to achieve equivalent exposures. Secondly, especially in this case, it is often helpful to combine data sets together to improve the informativeness of the overall design. This latter point, however was the point of my previous email. Some care must be taken to assess the accuracy of covariate effects given the unbalanced nature of the design. While optimal design can be used to extract more information from the same number of samples, it is not a substitute for the real data. Even with optimal design of the pediatric study (with the same 20 subjects, 3 optimal sample points) I bet you would gain by using adult data as well. You always gain by summing over data (unless the new data is negatively informative which is unlikely in any PK situation). So I don't exactly follow your point. The question to me is simply, what chance do I have of identifying a model that allows me to draw appropriately accurate conclusions. Optimal design is a way that allows investigators to improve the informativeness of data. Obviously, no data = no information. Steve -- Professor Stephen Duffull Chair of Clinical Pharmacy School of Pharmacy University of Otago PO Box 913 Dunedin New Zealand E: [EMAIL PROTECTED] P: +64 3 479 5044 F: +64 3 479 7034 Design software: www.winpopt.com
