Introduction to Adaptive Trials in Oncology: What makes oncology different from other therapeutic areas

In our first blog post in 2014, CROS NT is featuring the first part in a series of articles on Adaptive Trial Design in oncology written by Thomas Zwingers, Senior Director for Consultancy Services and Senior Biostatistician for CROS NT. In the introductory article, Thomas describes the complexities of oncology trials and what makes them unique.

Adaptive Designs have been increasingly gaining attention during the past years. These designs provide greater flexibility for Investigators/pharmaceutical companies by using an iterative process of:
– Collecting Data
– Analyzing the collected data
– Making decisions on the study design parameters according to the outcome of the analysis; and then
– Collecting additional data

More and more, Investigators and pharmaceutical companies use the advantages of adaptive designs over traditional, fixed designs in order to:
– Decrease the necessary sample size in trials
– Increase the probability of success in each trial
– To shorten the development process time of a new drug

The application of adaptive designs in oncology trials can prove challenging which is due to the nature of this medical field and its unique characteristics which makes it different from other therapeutic areas. It is not a single characteristic which makes oncology different, but rather the simultaneous occurrence of the following items:

- Long timelines to reach clinical endpoints The ultimate endpoint for registration of a new drug is still the “Overall Survival Time”. Surrogate endpoints like “response rates” or “time to progression” are often used but mostly as secondary endpoints or in earlier phases of the development process.

- The use of treatment combinations Many oncology indications are treated using a combination of either surgery or radiotherapy and chemotherapy where most chemotherapy treatments are composed of various drug combinations, e.g. in leukemia up to 8 different drugs are used.

- The large number of partially related diseases Although all cancers share the common trait of “abnormal growing cells”, the heterogeneity of histological and immunological features of each disease is extensive.

- The importance of disease sub-types and/or genotypes Even within each disease entity, different genotypes of immunologic surface makers can determine or rule out treatment success.

- Regimen modifications during treatment As anti-cancer drugs are highly toxic, treatment modifications due to adverse reactions is very common and make it difficult to create a population of homogenously treated patients. This also has an impact on the interpretation of the clinical outcome.

- The high impact of the disease on patient life

- The high costs of treatment The average cost of cancer treatments range from approximately $5,000 USD per year to over $100,000 USD per year for patients with brain tumors.

- Slow Recruitment Except for the most common tumor types – breast cancer, colorectal cancer and lung cancer – in most other indications, the number of patients is usually low within each center, thus resulting in slow recruitment of trials with a multitude of centers.

In this highly diversified area of drug development, adaptive designs offer features which enable Investigators to find effective drugs more quickly for the benefit of the patients.

This introduction will be followed by a series of articles that will present solutions for the application of adaptive designs in the oncology field with respect to the aforementioned issues. If you are interested in receiving these directly in your inbox, please sign up for our oncology mailing list on our website.

About Thomas Zwingers:
Thomas Zwingers is the Senior Director for Consultancy Services for CROS NT. In Thomas’ current role, he provides pharmaceutical, biotechnology and medical device companies with statistical methodology advice pertaining to trial design, conduct and reporting including regulatory submissions. Thomas has been working in the clinical trial environment since 1980 in project team management and statistical analysis. He has particular expertise in Adaptive Trial Design and Bayesian Framework, Meta-Analysis and Non-Inferiority Trials with therapeutic area expertise in Oncology, Respiratory and Dermatology studies.

Introduction to Adaptive Trials in Oncology: What makes oncology different from other therapeutic areas

In our first blog post in 2014, CROS NT is featuring the first part in a series of articles on Adaptive Trial Design in oncology written by Thomas Zwingers, Senior Director for Consultancy Services and Senior Biostatistician for CROS NT. In the introductory article, Thomas describes the complexities of oncology trials and what makes them unique.

Adaptive Designs have been increasingly gaining attention during the past years. These designs provide greater flexibility for Investigators/pharmaceutical companies by using an iterative process of:
– Collecting Data
– Analyzing the collected data
– Making decisions on the study design parameters according to the outcome of the analysis; and then
– Collecting additional data

More and more, Investigators and pharmaceutical companies use the advantages of adaptive designs over traditional, fixed designs in order to:
– Decrease the necessary sample size in trials
– Increase the probability of success in each trial
– To shorten the development process time of a new drug

The application of adaptive designs in oncology trials can prove challenging which is due to the nature of this medical field and its unique characteristics which makes it different from other therapeutic areas. It is not a single characteristic which makes oncology different, but rather the simultaneous occurrence of the following items:

- Long timelines to reach clinical endpoints The ultimate endpoint for registration of a new drug is still the “Overall Survival Time”. Surrogate endpoints like “response rates” or “time to progression” are often used but mostly as secondary endpoints or in earlier phases of the development process.

- The use of treatment combinations Many oncology indications are treated using a combination of either surgery or radiotherapy and chemotherapy where most chemotherapy treatments are composed of various drug combinations, e.g. in leukemia up to 8 different drugs are used.

- The large number of partially related diseases Although all cancers share the common trait of “abnormal growing cells”, the heterogeneity of histological and immunological features of each disease is extensive.

- The importance of disease sub-types and/or genotypes Even within each disease entity, different genotypes of immunologic surface makers can determine or rule out treatment success.

- Regimen modifications during treatment As anti-cancer drugs are highly toxic, treatment modifications due to adverse reactions is very common and make it difficult to create a population of homogenously treated patients. This also has an impact on the interpretation of the clinical outcome.

- The high impact of the disease on patient life

- The high costs of treatment The average cost of cancer treatments range from approximately $5,000 USD per year to over $100,000 USD per year for patients with brain tumors.

- Slow Recruitment Except for the most common tumor types – breast cancer, colorectal cancer and lung cancer – in most other indications, the number of patients is usually low within each center, thus resulting in slow recruitment of trials with a multitude of centers.

In this highly diversified area of drug development, adaptive designs offer features which enable Investigators to find effective drugs more quickly for the benefit of the patients.

This introduction will be followed by a series of articles that will present solutions for the application of adaptive designs in the oncology field with respect to the aforementioned issues. If you are interested in receiving these directly in your inbox, please sign up for our oncology mailing list on our website.

About Thomas Zwingers:
Thomas Zwingers is the Senior Director for Consultancy Services for CROS NT. In Thomas’ current role, he provides pharmaceutical, biotechnology and medical device companies with statistical methodology advice pertaining to trial design, conduct and reporting including regulatory submissions. Thomas has been working in the clinical trial environment since 1980 in project team management and statistical analysis. He has particular expertise in Adaptive Trial Design and Bayesian Framework, Meta-Analysis and Non-Inferiority Trials with therapeutic area expertise in Oncology, Respiratory and Dermatology studies.

Introduction to Adaptive Trials in Oncology: What makes oncology different from other therapeutic areas

In our first blog post in 2014, CROS NT is featuring the first part in a series of articles on Adaptive Trial Design in oncology written by Thomas Zwingers, Senior Director for Consultancy Services and Senior Biostatistician for CROS NT. In the introductory article, Thomas describes the complexities of oncology trials and what makes them unique.

Adaptive Designs have been increasingly gaining attention during the past years. These designs provide greater flexibility for Investigators/pharmaceutical companies by using an iterative process of:
– Collecting Data
– Analyzing the collected data
– Making decisions on the study design parameters according to the outcome of the analysis; and then
– Collecting additional data

More and more, Investigators and pharmaceutical companies use the advantages of adaptive designs over traditional, fixed designs in order to:
– Decrease the necessary sample size in trials
– Increase the probability of success in each trial
– To shorten the development process time of a new drug

The application of adaptive designs in oncology trials can prove challenging which is due to the nature of this medical field and its unique characteristics which makes it different from other therapeutic areas. It is not a single characteristic which makes oncology different, but rather the simultaneous occurrence of the following items:

- Long timelines to reach clinical endpoints The ultimate endpoint for registration of a new drug is still the “Overall Survival Time”. Surrogate endpoints like “response rates” or “time to progression” are often used but mostly as secondary endpoints or in earlier phases of the development process.

- The use of treatment combinations Many oncology indications are treated using a combination of either surgery or radiotherapy and chemotherapy where most chemotherapy treatments are composed of various drug combinations, e.g. in leukemia up to 8 different drugs are used.

- The large number of partially related diseases Although all cancers share the common trait of “abnormal growing cells”, the heterogeneity of histological and immunological features of each disease is extensive.

- The importance of disease sub-types and/or genotypes Even within each disease entity, different genotypes of immunologic surface makers can determine or rule out treatment success.

- Regimen modifications during treatment As anti-cancer drugs are highly toxic, treatment modifications due to adverse reactions is very common and make it difficult to create a population of homogenously treated patients. This also has an impact on the interpretation of the clinical outcome.

- The high impact of the disease on patient life

- The high costs of treatment The average cost of cancer treatments range from approximately $5,000 USD per year to over $100,000 USD per year for patients with brain tumors.

- Slow Recruitment Except for the most common tumor types – breast cancer, colorectal cancer and lung cancer – in most other indications, the number of patients is usually low within each center, thus resulting in slow recruitment of trials with a multitude of centers.

In this highly diversified area of drug development, adaptive designs offer features which enable Investigators to find effective drugs more quickly for the benefit of the patients.

This introduction will be followed by a series of articles that will present solutions for the application of adaptive designs in the oncology field with respect to the aforementioned issues. If you are interested in receiving these directly in your inbox, please sign up for our oncology mailing list on our website.

About Thomas Zwingers:
Thomas Zwingers is the Senior Director for Consultancy Services for CROS NT. In Thomas’ current role, he provides pharmaceutical, biotechnology and medical device companies with statistical methodology advice pertaining to trial design, conduct and reporting including regulatory submissions. Thomas has been working in the clinical trial environment since 1980 in project team management and statistical analysis. He has particular expertise in Adaptive Trial Design and Bayesian Framework, Meta-Analysis and Non-Inferiority Trials with therapeutic area expertise in Oncology, Respiratory and Dermatology studies.

Introduction to Adaptive Trials in Oncology: What makes oncology different from other therapeutic areas

In our first blog post in 2014, CROS NT is featuring the first part in a series of articles on Adaptive Trial Design in oncology written by Thomas Zwingers, Senior Director for Consultancy Services and Senior Biostatistician for CROS NT. In the introductory article, Thomas describes the complexities of oncology trials and what makes them unique.

Adaptive Designs have been increasingly gaining attention during the past years. These designs provide greater flexibility for Investigators/pharmaceutical companies by using an iterative process of:
– Collecting Data
– Analyzing the collected data
– Making decisions on the study design parameters according to the outcome of the analysis; and then
– Collecting additional data

More and more, Investigators and pharmaceutical companies use the advantages of adaptive designs over traditional, fixed designs in order to:
– Decrease the necessary sample size in trials
– Increase the probability of success in each trial
– To shorten the development process time of a new drug

The application of adaptive designs in oncology trials can prove challenging which is due to the nature of this medical field and its unique characteristics which makes it different from other therapeutic areas. It is not a single characteristic which makes oncology different, but rather the simultaneous occurrence of the following items:

- Long timelines to reach clinical endpoints The ultimate endpoint for registration of a new drug is still the “Overall Survival Time”. Surrogate endpoints like “response rates” or “time to progression” are often used but mostly as secondary endpoints or in earlier phases of the development process.

- The use of treatment combinations Many oncology indications are treated using a combination of either surgery or radiotherapy and chemotherapy where most chemotherapy treatments are composed of various drug combinations, e.g. in leukemia up to 8 different drugs are used.

- The large number of partially related diseases Although all cancers share the common trait of “abnormal growing cells”, the heterogeneity of histological and immunological features of each disease is extensive.

- The importance of disease sub-types and/or genotypes Even within each disease entity, different genotypes of immunologic surface makers can determine or rule out treatment success.

- Regimen modifications during treatment As anti-cancer drugs are highly toxic, treatment modifications due to adverse reactions is very common and make it difficult to create a population of homogenously treated patients. This also has an impact on the interpretation of the clinical outcome.

- The high impact of the disease on patient life

- The high costs of treatment The average cost of cancer treatments range from approximately $5,000 USD per year to over $100,000 USD per year for patients with brain tumors.

- Slow Recruitment Except for the most common tumor types – breast cancer, colorectal cancer and lung cancer – in most other indications, the number of patients is usually low within each center, thus resulting in slow recruitment of trials with a multitude of centers.

In this highly diversified area of drug development, adaptive designs offer features which enable Investigators to find effective drugs more quickly for the benefit of the patients.

This introduction will be followed by a series of articles that will present solutions for the application of adaptive designs in the oncology field with respect to the aforementioned issues. If you are interested in receiving these directly in your inbox, please sign up for our oncology mailing list on our website.

About Thomas Zwingers:
Thomas Zwingers is the Senior Director for Consultancy Services for CROS NT. In Thomas’ current role, he provides pharmaceutical, biotechnology and medical device companies with statistical methodology advice pertaining to trial design, conduct and reporting including regulatory submissions. Thomas has been working in the clinical trial environment since 1980 in project team management and statistical analysis. He has particular expertise in Adaptive Trial Design and Bayesian Framework, Meta-Analysis and Non-Inferiority Trials with therapeutic area expertise in Oncology, Respiratory and Dermatology studies.