Services

Services

Services

Analysis of COA Data

The nature of COA data analysis can be wide-ranging and may encompass:

Epidemiology/Natural History

Epidemiology/Natural History

Epidemiology/Natural History

Post-Hoc Analysis of Trial Data

Post-Hoc Analysis of Trial Data

Post-Hoc Analysis of Trial Data

Trial Rescue

Trial Rescue

Trial Rescue

Illuminating Therapeutic Benefit Post-Approval

Illuminating Therapeutic Benefit Post-Approval

Illuminating Therapeutic Benefit Post-Approval

The key to strong analytic solutions consists of two major components:

Experience, and lots of it

Experience, and lots of it

Understanding the unique complexities of the disease area under study

Understanding the unique complexities of the disease area under study

The key to strong analytic solutions consists of two major components:

Experience, and lots of it

Understanding the unique complexities of the disease area under study

At The Psychometrics Team, we have been analyzing data for over 25 years. That is a lot of experience. That teaches you how things break, why they break, and how to avoid or diagnose such events. It also teaches where the pitfalls are and where the bodies are buried. That enables us to be both precise and fast. Over this time, we have cultivated and codified sets of protocols that guarantee the safety of your data, the precision of your results, and the fidelity to the true patient voice hiding in the data. Our expertise, cultivated over this time includes the following areas useful in analysis of post-approval data:

Linear/Generalized Linear Models

Linear/Generalized Linear Models

  • Conditional & Marginal Models

  • Mixed Model Extensions

  • Pattern Mixture Models

  • Mediation and Moderation and both

Structural Equation Modelling

Structural Equation Modelling

  • Mediation 

  • Mediated Moderation

  • Moderated Mediation

  • Instrumental Variable Analysis

  • Multi-Trait Multi-Method Analysis

  • Latent Mixture Models

Time-To-Event Modelling

Time-To-Event Modelling

  • Single Terminal Event

  • Recurrent Event

  • Multi-State Transition Models

  • Joint Models (both terminal and recurrent or both)

Experience with the disease area, or an ability to gain understanding of the disease area is essential. We know this from experience.

The porous transition boundaries in Alzheimer’s Dementia and how this can make effect detection perilous.

Understanding how blood Phe levels fluctuate to elucidate relationships between blood Phe and PROs in Phenylketonuria.

Understanding how blood Phe levels fluctuate to elucidate relationships between blood Phe and PROs in Phenylketonuri.

Whether it is the fluctuating nature of migraine attacks and how these can contribute to effect suppression and placebo effects.

Photo of Sierra Mixteca, Oaxaca
Photo of Sierra Mixteca, Oaxaca
Photo of Sierra Mixteca, Oaxaca

Photo (© Serrano, D. Sierra Mixteca, Oaxaca)

Photo (© Serrano, D. Sierra Mixteca, Oaxaca)

Photo (© Serrano, D. Sierra Mixteca, Oaxaca)

Consideration of the disease area and careful application of the corresponding first principles is essential to yield sound results.

 Without an understanding of these components, analysis will go nowhere, or nowhere good.

The influence of recurrent deterioration and missing data for PROs in oncology and how these must be accounted for to detect long-undiscovered effects.

Or validating a legacy instrument previously rejected by DCOA as a fit-for-purpose endpoint and getting the endpoint into the label.

Have a chat with us

Got any questions? Contact us and we'll be happy to help.