Drivers of effectiveness: a case study in Hodgkin’s lymphoma


In practice, an efficacy-effectiveness gap (for a definition, see here) is likely to occur if a key characteristic (such as a patient-related or disease-related characteristic) is distributed differently in randomised controlled trials (RCTs) compared with real-life.

What was examined in this case study?

This case study used a systematic review to:

  • identify any efficacy effectiveness gap in the evidence for treating Hodgkin’s lymphoma
  • explore the drivers of effectiveness (the factors that could influence this gap – for a definition, see here) in the evidence.

The review was conducted using the PICOS-T framework (PICOS framework + T for time horizon) looking for studies published between 2000 and 2015. The literature review was expanded to the broader disease area of haematological malignancies to identify any factors not explored in the relatively limited studies expected for Hodgkin’s lymphoma but that may be informative for this condition.

What were the findings and conclusions?

11 studies on Hodgkin’s lymphoma were identified:

  • 5 studies reported information on an efficacy effectiveness gap
  • 9 studies gave information on drivers of effectiveness.

The main finding from the studies reporting on the efficacy effectiveness gap was that older patients are often excluded from trials (33% exclude patients >65 years and 39% exclude patients >70 years). However, older patients are better represented in observational studies (patients > 60 represent 5-10% of enrolled patients in clinical trials compared with 20-44% in observational studies).

Age was also found to be the most important driver of effectiveness – it was associated with shorter progression-free and overall survival, and less aggressive treatment due to comorbidities and physician reluctance. Age is also associated with two other important drivers of effectiveness: comorbidities and severe side effects.

Literature was lacking on the relative importance of drivers of effectiveness and also the distribution of different drivers of effectiveness according to the disease stage, which are likely to differ across stages.

The drivers of effectiveness found for other haematological malignancies were either similar to those found in the literature on Hodgkin’s lymphoma or not applicable to Hodgkin’s lymphoma, with the exception of male gender in non-Hodgkin’s lymphoma and acute myeloid leukemia.


  • Key areas were identified: key areas for further investigation into the efficacy-effectiveness gap and drivers of effectiveness were identified, such as age, treatment response, disease stage/severity, treatment toxicity.
  • Robust patient-level studies are needed: there is a need for patient-level data studies with robust statistical analysis to provide additional information on the weighting of drivers of effectiveness as a whole, but also through different disease stages, and to identify other drivers of effectiveness.
  • Other related disease areas may provide key information: the key driver of effectiveness within Hodgkin’s lymphoma may also be present throughout other areas of oncology and play a role in the difference between efficacy and effectiveness outcomes.
  • Understanding drivers of effectiveness can improve research and benefit patients: a more complete understanding of drivers of effectiveness for Hodgkins lymphoma and similar diseases can benefit patients who are disadvantaged by the efficacy effectiveness gap (in this instance, the elderly), can inform future trial design, and ultimately improve how oncological research is conducted.

Key contributor

Robert Olivares, Sanofi