In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Date: Tuesday 12th April 2022
Time: 14:00-15:00 BST | 15:00-16:00 CEST
Speaker: Tim Holland-Letz
Who is this event intended for? Statisticians in Pharma.
What is the benefit of attending? The chance to learn about new study designs.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = Free of charge
To register for the session, please click here.
In practical dose response trials, especially in a cell culture context, experimental designs to establish functional relationships between dose and effect of a substance are generally chosen based on simple rules of thumb. These designs are generally reasonably effective, but not optimal.
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Finally, we present an R-Shiny application which allows construction of optimal designs in most of these contexts with a minimum of theoretical knowledge.
|
Speaker |
Biography |
|
Tim Holland-Letz |
Tim Holland-Letz studied Statistics at the Technical University of Dortmund. In 2010, he completed his PhD in the same subject in cooperation with the Department of Mathematics of the Ruhr-University Bochum, focusing on the topic of optimal experimental design. At the same time he was employed at the Department of Medical Informatics and Biometry also at the Ruhr-University, working mainly on clinical studies and general applied statistics in medical research. In 2011, he moved to the Biostatistics Division within the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg. From this moment on, he has concentrated more on preclinical studies, with a special focus on optimal experimental designs and dose-response analysis. However, he still works on more or less all kinds of statistical problems arising in research and preclinical studies. In 2019 he completed his Habilitation in Medical Biostatistics at the University of Heidelberg. |
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Date: Tuesday 12th April 2022
Time: 14:00-15:00 BST | 15:00-16:00 CEST
Speaker: Tim Holland-Letz
Who is this event intended for? Statisticians in Pharma.
What is the benefit of attending? The chance to learn about new study designs.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = Free of charge
To register for the session, please click here.
In practical dose response trials, especially in a cell culture context, experimental designs to establish functional relationships between dose and effect of a substance are generally chosen based on simple rules of thumb. These designs are generally reasonably effective, but not optimal.
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Finally, we present an R-Shiny application which allows construction of optimal designs in most of these contexts with a minimum of theoretical knowledge.
|
Speaker |
Biography |
|
Tim Holland-Letz |
Tim Holland-Letz studied Statistics at the Technical University of Dortmund. In 2010, he completed his PhD in the same subject in cooperation with the Department of Mathematics of the Ruhr-University Bochum, focusing on the topic of optimal experimental design. At the same time he was employed at the Department of Medical Informatics and Biometry also at the Ruhr-University, working mainly on clinical studies and general applied statistics in medical research. In 2011, he moved to the Biostatistics Division within the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg. From this moment on, he has concentrated more on preclinical studies, with a special focus on optimal experimental designs and dose-response analysis. However, he still works on more or less all kinds of statistical problems arising in research and preclinical studies. In 2019 he completed his Habilitation in Medical Biostatistics at the University of Heidelberg. |
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Date: Tuesday 12th April 2022
Time: 14:00-15:00 BST | 15:00-16:00 CEST
Speaker: Tim Holland-Letz
Who is this event intended for? Statisticians in Pharma.
What is the benefit of attending? The chance to learn about new study designs.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = Free of charge
To register for the session, please click here.
In practical dose response trials, especially in a cell culture context, experimental designs to establish functional relationships between dose and effect of a substance are generally chosen based on simple rules of thumb. These designs are generally reasonably effective, but not optimal.
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Finally, we present an R-Shiny application which allows construction of optimal designs in most of these contexts with a minimum of theoretical knowledge.
|
Speaker |
Biography |
|
Tim Holland-Letz |
Tim Holland-Letz studied Statistics at the Technical University of Dortmund. In 2010, he completed his PhD in the same subject in cooperation with the Department of Mathematics of the Ruhr-University Bochum, focusing on the topic of optimal experimental design. At the same time he was employed at the Department of Medical Informatics and Biometry also at the Ruhr-University, working mainly on clinical studies and general applied statistics in medical research. In 2011, he moved to the Biostatistics Division within the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg. From this moment on, he has concentrated more on preclinical studies, with a special focus on optimal experimental designs and dose-response analysis. However, he still works on more or less all kinds of statistical problems arising in research and preclinical studies. In 2019 he completed his Habilitation in Medical Biostatistics at the University of Heidelberg. |
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Date: Tuesday 12th April 2022
Time: 14:00-15:00 BST | 15:00-16:00 CEST
Speaker: Tim Holland-Letz
Who is this event intended for? Statisticians in Pharma.
What is the benefit of attending? The chance to learn about new study designs.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = Free of charge
To register for the session, please click here.
In practical dose response trials, especially in a cell culture context, experimental designs to establish functional relationships between dose and effect of a substance are generally chosen based on simple rules of thumb. These designs are generally reasonably effective, but not optimal.
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Finally, we present an R-Shiny application which allows construction of optimal designs in most of these contexts with a minimum of theoretical knowledge.
|
Speaker |
Biography |
|
Tim Holland-Letz |
Tim Holland-Letz studied Statistics at the Technical University of Dortmund. In 2010, he completed his PhD in the same subject in cooperation with the Department of Mathematics of the Ruhr-University Bochum, focusing on the topic of optimal experimental design. At the same time he was employed at the Department of Medical Informatics and Biometry also at the Ruhr-University, working mainly on clinical studies and general applied statistics in medical research. In 2011, he moved to the Biostatistics Division within the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg. From this moment on, he has concentrated more on preclinical studies, with a special focus on optimal experimental designs and dose-response analysis. However, he still works on more or less all kinds of statistical problems arising in research and preclinical studies. In 2019 he completed his Habilitation in Medical Biostatistics at the University of Heidelberg. |
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Date: Tuesday 12th April 2022
Time: 14:00-15:00 BST | 15:00-16:00 CEST
Speaker: Tim Holland-Letz
Who is this event intended for? Statisticians in Pharma.
What is the benefit of attending? The chance to learn about new study designs.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = Free of charge
To register for the session, please click here.
In practical dose response trials, especially in a cell culture context, experimental designs to establish functional relationships between dose and effect of a substance are generally chosen based on simple rules of thumb. These designs are generally reasonably effective, but not optimal.
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Finally, we present an R-Shiny application which allows construction of optimal designs in most of these contexts with a minimum of theoretical knowledge.
|
Speaker |
Biography |
|
Tim Holland-Letz |
Tim Holland-Letz studied Statistics at the Technical University of Dortmund. In 2010, he completed his PhD in the same subject in cooperation with the Department of Mathematics of the Ruhr-University Bochum, focusing on the topic of optimal experimental design. At the same time he was employed at the Department of Medical Informatics and Biometry also at the Ruhr-University, working mainly on clinical studies and general applied statistics in medical research. In 2011, he moved to the Biostatistics Division within the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg. From this moment on, he has concentrated more on preclinical studies, with a special focus on optimal experimental designs and dose-response analysis. However, he still works on more or less all kinds of statistical problems arising in research and preclinical studies. In 2019 he completed his Habilitation in Medical Biostatistics at the University of Heidelberg. |
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Date: Tuesday 12th April 2022
Time: 14:00-15:00 BST | 15:00-16:00 CEST
Speaker: Tim Holland-Letz
Who is this event intended for? Statisticians in Pharma.
What is the benefit of attending? The chance to learn about new study designs.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = Free of charge
To register for the session, please click here.
In practical dose response trials, especially in a cell culture context, experimental designs to establish functional relationships between dose and effect of a substance are generally chosen based on simple rules of thumb. These designs are generally reasonably effective, but not optimal.
In this webinar we give an overview over the statistical optimal design approach in this context. We show how optimal designs can be constructed, covering mainly all-purpose designs (D-optimality) but also more specific designs (c-optimality). We introduce a graphical representation. Next, we show how these concepts can be extended to trials aiming to estimate the interaction between two different substances given simultaneously.
Finally, we present an R-Shiny application which allows construction of optimal designs in most of these contexts with a minimum of theoretical knowledge.
|
Speaker |
Biography |
|
Tim Holland-Letz |
Tim Holland-Letz studied Statistics at the Technical University of Dortmund. In 2010, he completed his PhD in the same subject in cooperation with the Department of Mathematics of the Ruhr-University Bochum, focusing on the topic of optimal experimental design. At the same time he was employed at the Department of Medical Informatics and Biometry also at the Ruhr-University, working mainly on clinical studies and general applied statistics in medical research. In 2011, he moved to the Biostatistics Division within the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg. From this moment on, he has concentrated more on preclinical studies, with a special focus on optimal experimental designs and dose-response analysis. However, he still works on more or less all kinds of statistical problems arising in research and preclinical studies. In 2019 he completed his Habilitation in Medical Biostatistics at the University of Heidelberg. |
