Values and Preferences of Patients with Small Renal Masses: Integrating Evidence and Patient Perspectives

Abstract

Small renal masses present patients and clinicians with preference-sensitive choices among partial nephrectomy, percutaneous thermal ablation, and active surveillance. This thesis set out to generate decision-ready evidence on both short-term burdens and longer-term oncologic outcomes for each option, and to explore how patients value the trade-offs involved. We conducted coordinated prognosis-focused systematic reviews and meta-analyses to estimate strategy-specific probabilities for peri-operative outcomes (major complications, blood loss, hospital stay, procedure time) and for five-year oncologic endpoints (overall mortality, cancer-specific mortality, local recurrence, metastasis). Methods emphasized applicability to decisions: QUIPS for risk of bias; GRADE adapted for prognosis (including a decision-threshold approach to imprecision); ICEMAN to guard against over-interpretation of subgroup signals; and improved conversions of medians/IQRs to means/SDs to avoid distorted weights in single-arm syntheses. We then built and piloted a bilingual decision aid and used structured interviews with a probability trade-off task to elicit values and risk thresholds. Across strategies, short-term burdens differed in ways patients care about, while five-year cancer-specific outcomes were generally favourable; estimates are presented as descriptive prognosis for each option rather than comparative effects, with certainty ratings that make remaining uncertainty explicit. The pilot demonstrated the feasibility and acceptability of eliciting preferences and showed meaningful variation in how patients balance avoiding procedures against small differences in oncologic risk. By integrating rigorous prognosis evidence with empirical preference elicitation, this thesis provides a transparent foundation for shared decision-making and supports conditional, values-sensitive recommendations in guidelines. It also offers practical methods, both statistical and procedural, for future decision-aid development in SRMs and similar preference-sensitive conditions.