The Role of Qualitative Research Methods in Discrete Choice Experiments

Background. The use of qualitative research (QR) methods is recommended as good practice in discrete choice experiments (DCEs). This study investigated the use and reporting of QR to inform the design and/or interpretation of healthcare-related DCEs and explored the perceived usefulness of such methods. Methods. DCEs were identified from a systematic search of the MEDLINE database. Studies were classified by the quantity of QR reported (none, basic, or extensive). Authors ( n = 91) of papers reporting the use of QR were invited to complete an online survey eliciting their views about using the methods. Results. A total of 254 healthcare DCEs were included in the review; of these, 111 (44%) did not report using any qualitative methods; 114 (45%) reported “basic” information; and 29 (11%) reported or cited “extensive” use of qualitative methods. Studies reporting the use of qualitative methods used them to select attributes and/or levels ( n = 95; 66%) and/or pilot the DCE survey ( n = 26; 18%). Popular qualitative methods included focus groups ( n = 63; 44%) and interviews ( n = 109; 76%). Forty-four studies (31%) reported the analytical approach, with content ( n = 10; 7%) and framework analysis ( n = 5; 4%) most commonly reported. The survey identified that all responding authors ( n = 50; 100%) found that qualitative methods added value to their DCE study, but many ( n = 22; 44%) reported that journals were uninterested in the reporting of QR results. Conclusions. Despite recommendations that QR methods be used alongside DCEs, the use of QR methods is not consistently reported. The lack of reporting risks the inference that QR methods are of little use in DCE research, contradicting practitioners’ assessments. Explicit guidelines would enable more clarity and consistency in reporting, and journals should facilitate such reporting via online supplementary materials.

Numerical Techniques for Approximating Lyapunov Exponents and Their Implementation

The algorithms behind a toolbox for approximating Lyapunov exponents of nonlinear differential systems by QR methods are described. The basic solvers perform integration of the trajectory and approximation of the Lyapunov exponents simultaneously. That is, they integrate for the trajectory at the same time, and with the same underlying schemes, as is carried out for integration of the Lyapunov exponents. Separate computational procedures solve small systems for which the Jacobian matrix can be computed and stored, and for large systems for which the Jacobian cannot be stored, and may not even be explicitly known. If it is known, the user has the option to provide the action of the Jacobian on a vector. An alternative strategy is also presented in which one may want to approximate the trajectory with a specialized solver, linearize around the computed trajectory, and then carry out the approximation of the Lyapunov exponents using techniques for linear problems.

Codes for Approximating Lyapunov Exponents

We present a suite of codes for approximating Lyapunov exponents of nonlinear differential systems by so-called QR methods. The basic solvers perform integration of the trajectory and approximation of the Lyapunov exponents simultaneously. That is, they integrate for the trajectory at the same time, and with the same underlying schemes, as integration for the Lyapunov exponents is carried out. Separate codes solve small systems for which we can compute and store the Jacobian matrix, and for large systems for which the Jacobian matrix cannot be stored, and it may not even be explicitly known. If it is known, the user has the option to provide its action on a vector. An alternative strategy is also presented in which one may want to approximate the trajectory with a specialized solver, linearize around the computed trajectory, and then carry out the approximation of the Lyapunov exponents using codes for linear problems.