Analysis of Heterogeneity of Treatment Effects for Patient-Centered Outcomes

Let us help you understand your drug’s potential for personalized medicine.

Drug research focuses on treatment differences and disease differences—much to the exclusion of individual patient differences. In fact, by design, randomized clinical trials aim to factor out (through randomization) individual differences so that you can determine the average effect of your medicine in any particular controlled group. However, some patients in your study will benefit more or less than the average. Who are they and can they be identified efficiently?

To support your clinical development AND your commercialization strategy, we can help you identify the people—based on varying comorbidities, genetics, age, gender, physiology, geography, standards of care, or even expectations and preferences—for which your drug is highly effective. And the people for which it isn’t. This is called heterogeneity of treatment effects.  More and more regulatory and reimbursement agencies are requiring a thorough exploration and understanding of subgroups who benefit more from treatment.

Understanding subgroup differences—or differential responders—can help you throughout your drug development pipeline. Stage-by-stage, this can help you:

Stage Benefits
Phase I - Identify and define unique responders
Identify patient-reported outcome items/subscales
Power Phase II and recruit appropriate patients
Phase II - Refine patient exclusion/inclusion criteria
- Refine responder definitions
- Power Phase III and recruit appropriate patients
Phase III - Identify subpopulations for market segmentation
- Provide evidence for possible risk-sharing plans for reimbursement
Regulatory Approval - Identify evidence for patient population definitions
- Identify the potential for additional labelling claims
- Maximize reimbursement for hyper-responsive patients
- Identify evidence for  HTA submissions
Post-launch Phase IV - Identify additional product value messaging
- Identify the potential for additional labelling claims
- Inform safety studies and reduce risks of adverse events
Abandoned Drugs - Rescue, repurpose, or resurrect compounds that may have promising subgroup effects

Methods & Applications

Using innovative analytical methods based on structural equation modeling (SEM), such as Latent Growth Models (LGMs) and Growth Mixture Models (GMMs), we can help you uncover and understand heterogeneity in treatment response among your patients. These efficient methods allow patterns within heterogeneous data to emerge so subgroups can be examined separately to demonstrate treatment effects and move closer to patient-centered outcomes.

These analytical methods can be used across therapeutic areas to support outcomes research, health economics, market access, pricing and reimbursement, and epidemiology. And, we have experience working with data sources from clinical trials, observational studies, and registries.

Recent Publications

Spelman T, Herring WL, Zhang Y, Tempest M, Pearson I, Freudensprung U, Acosta C, Dort T, Hyde R, Havrdova E, Horakova D, Trojano M, De Luca G, Lugaresi A, Izquierdo G, Grammond P, Duquette P, Alroughani R, Pucci E, Granella F, Lechner-Scott J, Sola P, Ferraro D, Grand’Maison F, Terzi M, Rozsa C, Boz C, Hupperts R, Van Pesch V, Oreja-Guevara C, van der Walt A, Jokubaitis VG, Kalincik T, Butzkueven H. Comparative effectiveness and cost-effectiveness of natalizumab and fingolimod in patients with inadequate response to disease-modifying therapies in relapsing-remitting multiple sclerosis in the United Kingdom. Pharmacoeconomics. 2021 Dec 18.