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SOLUTIONS

Sample Size & Power Calculations
Calculate for a Variety of frequentist and Bayesian Design

Adaptive Design
Design and Analyze a Wide Range of Adaptive Designs

Milestone Prediction
Predict Interim Analysis Timing or Study Length

Randomization Lists
Generate and Save Lists for your Trial Design
CAPABILITIES

Group Sequential and Promising Zone Designs
Calculate Boundaries & Find Sample Size. Evaluate Interim Data & Re-estimate Sample Size

Sample Size for Bayesian Statistics
Probability of Success (Assurance), Credible Intervals, Bayes Factors and more

Early Stage and Complex Designs
Sample size & operating characteristics for Phase I, II & Seamless Designs (MAMS)
Pricing
Free Trial
SOLUTIONS

Sample Size & Power Calculations
Calculate for a Variety of frequentist and Bayesian Design

Adaptive Design
Design and Analyze a Wide Range of Adaptive Designs

Milestone Prediction
Predict Interim Analysis Timing or Study Length

Randomization Lists
Generate and Save Lists for your Trial Design
CAPABILITIES

Group Sequential and Promising Zone Designs
Calculate Boundaries & Find Sample Size. Evaluate Interim Data & Re-estimate Sample Size

Sample Size for Bayesian Statistics
Probability of Success (Assurance), Credible Intervals, Bayes Factors and more

Early Stage and Complex Designs
Sample size & operating characteristics for Phase I, II & Seamless Designs (MAMS)
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CASE STUDY

Adaptive Designs in nQuery


Effective and Efficient Drug Development

Challenge

Increased Cost, Risk, and Competition in the
Drug Development Processes

Dr. Luis Rojas, the Executive Director Head of Biostatistics at Target Health, is a subject matter expert in study design and sample size calculations with more than 30 years of industry experience.

He has worked in the leading CROs in the industry and has assisted pharmaceutical companies with the drug development process with clinical trials, programs, and portfolio designs. His experience extends from preclinical, phases I through IV, biosimilar, and bioequivalent using fixed, adaptive designs, stand-alone, and master protocols in multiple therapeutic areas.

Today this work presents new challenges. Drug pipelines are larger than ever before, while the cost of bringing new drugs to market has nearly doubled in the past decade. This trend has compressed drug development ROIs and increased competition

KEY CHALLENGES

  • Increasing trial complexity
  • Changing regulations
  • Spiraling costs
  • Patient access
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nQuery is a one-stop platform. It is 100% reliable for me.

 

“nQuery is a one-stop platform. It is 100% reliable for me,” said Dr Rojas. “If the user knows the study design that needs to be implemented, then sample size calculation is a question of just minutes. Trying to programmatically do the same calculations in SAS or R usually takes more than 20 times longer. nQuery’s documentation is very helpful and provides very clear guidance to understand and implement the calculations that you need to perform. Side tables in most sample size modules make the process even easier.”
Luis-Rojas-Profile-Pic-nQuery

Dr Luis Rojas,
Executive Director Head Of Biostatistics At Target Health

Solution
nQuery is a complete solution for Adaptive Trial Design

With increasing costs, competition, and risk, adaptive clinical trials have made rapid headway as a method to alleviate these challenges. Adaptive trials enable continual modification to the trial design based on interim data. This means that with adaptive trials, you have the opportunity to make changes to your trial, while it is still ongoing. Within nQuery, the world’s most trusted clinical trial design platform, there is a dedicated module for adaptive clinical trials that provide biostatisticians with a range of tools across various adaptive disciplines for sample size calculation.

That’s why Dr. Rojas is a proponent of nQuery to serve as a reliable digital tool for sample size calculations in adaptive and fixed trials.

Leveraging nQuery, Dr. Rojas has developed hundreds of clinical trials and programs.

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✔ Help achieve regulatory approval

✔ Optimize to reduce costs &
improve rates of success

✔ Increase flexibility for better
and earlier decision-making

✔ Design ethical and efficient
clinical trials by reducing the
risk to patients

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Further Case Studies

Case Study
Advancing patient care for Japans most common cancer_nQuery Case Study

 
 
Use nQuery to optimize clinical trial design
 
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