SERVIER CAMPUS

Patient Preference Studies with Discrete Choice Experiments (M/F)


Published on
29 Oct2020
Duration

6 mois

Reference

2020-10-29 DCE-PEX-MVD

Remuneration

By profile

Location

Suresnes

Start date

As soon as possible


Job description

The incorporation of the patients’ perspectives in drug developments and assessments is receiving increasing

recognition. Both regulators and industries understand that patients and care-partners who live with a disease on a

daily basis have developed their own knowledge, views and preferences on the benefits and risks of the medicine they

receive, or they could receive.

Discrete Choice Experiments (DCEs) are recognized gold standard tools to explore patients’ preferences in health care.

They offer a systematic method for eliciting preferences and quantifying both the relative importance of treatment

attributes in the choice of treatments and the tradeoffs patients are willing to make between the benefits and risks of

treatments. They are conducted as surveys with many simple questions (direct pairwise choices) in a large sample of

patients. A statistical model is then fitted to the individual responses, and the parameter estimates could be interpreted

as preference weights, which represent how the treatment characteristics contribute to the choice of one treatment

over another by the patients. Several experimental designs (number of questions asked to each patient, order of these

questions etc.) have been proposed in the literature, with advantages and drawbacks in terms of feasibility, variability

and bias of the parameters. The sample size if often determined empirically from previous studies, and no analytical

formula have been proposed for this purpose yet.

Missions

  • Review the literature on DCEs’ experimental designs, statistical analysis models and sample size determinations, evaluating their pros and cons in health care
  • Conduct a simulation study (in R) in order to compare the performances of the most promising and/or popular experimental designs and statistical analysis models
  • Application to real case studies
  • If feasible, develop a method for sample size calculation for DCEs

Required profile

Student in last year of MSc with major in biostatistics, statistics,

or relevant field.

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