Objectives: Eliciting preferences and trade-offs that patients may make to achieve important outcomes, can assist in developing patient-centred research and care. The pilot study aimed to test the feasibility of a case 2 best-worst scaling survey (BWS) to elicit recipient with kidney transplantation preferences after transplantation. Design: Preferences for graft survival and dying, cancer, cardiovascular disease, diabetes, infection and side effects (gastrointestinal, weight-gain and appearance) were assessed in recipients with transplantation using a BWS (20 scenarios of nine outcomes). Participants chose 'best' and 'worst' outcomes. Responses were analysed using a multinomial logit model. Selected participants were interviewed. Outcomes: Attribute coefficients and survey completion error rates. Results: 81 recipients with transplantation were approached, and 39 (48%), mean age 50.5 years, completed the BWS. 4 (10%) surveys were invalid with major errors and of 35 remaining, 7 of 1400 (0.5%) choices were missing. -23 (59%) took >20 min to complete the survey. 1 was unable to finish, and 1 did not understand the survey. 2 (5%) found it very hard and 14 (35%) moderately hard. Most attribute coefficients were significant (p<0.05) and showed face validity. Graft survival was most important with normalised coefficients from 1 (95% CI 0.89 to 1.11) to 0.06 (95% CI -0.03 to 0.16) for 30 and 1 year duration, respectively. Attribute level coefficients decreased with increasing risk of adverse outcomes. Error rates of 20% and 2% were estimated for dominant attributes '100% risk of dying' and '30 years graft survival', respectively. 7 participants were interviewed regarding counterintuitive selection of '100% risk of dying' as a 'best' outcome. Misunderstanding, not linking dying to graft survival and aversion to dialysis were reasons given. Conclusions: Recipients with transplant recipients successfully completed a complex case 2 BWS with attribute coefficients having face validity with respect to duration of graft survival and risk of adverse outcomes. Areas for refinement to reduce complexity in design have been identified.