Purpose: To evaluate the association between patient–physician trust, treatment choice, patient-reported outcomes, and patient-reported experiences among patients with localized prostate cancer. Materials and Methods: We conducted a secondary analysis of data from a multicenter randomized controlled trial among patients with localized prostate cancer. Trust was measured at baseline, prior to treatment, using the Patient Trust–Wake Forest Physician Trust Scale (PTWF). Outcomes were assessed at baseline and at 3, 6, 12, and 24 mo. Treatments were robotic-assisted laparoscopic radical prostatectomy, radiation therapy, and active surveillance. The primary treatment outcome was receipt of active treatment (surgery or radiation therapy) versus active surveillance. Multivariable logistic regression and linear mixed-effects models were adjusted for age, race/ethnicity, marital status, education, employment, insurance, prostate cancer risk group, intervention assignment, and study site. Sensitivity analyses modeling trust as a continuous variable yielded findings that were materially consistent with the primary analyses. Results: The high-trust group had higher odds of receiving active treatment than the low-trust group did (adjusted odds ratio [OR] = 1.80, 95% confidence interval [CI] = 1.14–2.87; P = 0.013). Higher baseline trust was also consistently associated with better physical and mental health-related quality of life and higher satisfaction with care and treatment decision making across follow-up assessments through 24 mo. Sensitivity analyses using trust as a continuous measure produced similar findings. Conclusions: Higher patient–physician trust was independently associated with treatment choice, patient-reported outcomes, and patient-reported experience. These findings underscore the importance of patient–physician trust as a component of patient-centered cancer care and shared decision making. Future studies should evaluate interventions designed to strengthen trust and improve patient-centered outcomes across diverse clinical settings.
Background: Quality-adjusted life-years (QALYs) are commonly used as a measure for evaluating the value of cancer screening. However, it is often unclear what drives model-based QALY estimates. We examine factors influencing QALY estimates for cervical cancer screening using 4 independent Cancer Intervention and Surveillance Modeling Network (CISNET) models. Methods: We calculated QALYs gained for 5 recommended cervical screening strategies in the United States (varying in test used, start age, interval, and follow-up of screen positives) and compared with no screening. Each model used common US data, standardized inputs for health-related quality of life (HRQoL) weights, and duration of HRQoL losses. We calculated the percentage change in QALYs gained between screening strategies within each model, comparing the least to the most effective screening strategies. Furthermore, we isolated the impact on the total QALYs gained per stage in the screening process (primary tests, repeat tests, colposcopies, and cancer diagnoses). Results: The percentage change in QALYs gained between screening strategies within each model ranged from 47% to 120%. In contrast, life-years gained (LYG) ranged from 1% to 12%. The largest variation in QALY gains was driven by HRQoL assumptions for repeat tests. Sensitivity analyses showed that QALY gains are highly sensitive to the duration of HRQoL losses due to repeat tests and discounting. Conclusions: Despite standardizing disease burden and key inputs, QALYs remained highly sensitive to individual model assumptions. Comparative modelling and providing additional disaggregated outcomes improve transparency, enabling decision makers to more confidently interpret and use model-based evaluations of screening strategies.
BACKGROUND:The increasing use of real-world data in economic evaluation raises concerns about the potential for confounding bias. Methods to control for this bias have been developed, but there is still much to be understood about how confounding influences economic evaluations. OBJECTIVES:To illustrate the impact of unadjusted confounding variables in economic evaluations of observational studies. METHODS:We simulated the costs and effectiveness of 2 treatments across 9 possible confounding effect scenarios. We considered these scenarios in the context in which one treatment is more costly and more effective with mild correlation between confounders and outcomes. All scenarios examined the incremental cost and effectiveness of 400 randomly generated individuals, reflecting sample sizes commonly seen within observational economic evaluations. Results were illustrated with the use of cost-effectiveness planes and cost-effectiveness acceptability curves (CEAC). RESULTS:Our simulations illustrate that confounding bias can have a significant effect on incremental costs and incremental effectiveness estimates. These simulations also illustrate that confounders can affect the evaluation of uncertainty by causing a shift in the CEACs. Such results hint that inadequate consideration of confounding bias can potentially lead to flawed judgments about the cost-effectiveness of a treatment. DISCUSSION:Results of economic evaluations can be influenced by confounding when they are conducted in an observational setting. Efforts must be made to limit their impact to ensure an accurate assessment of the economic value of treatments and to prevent potential losses of population health.
BACKGROUND:Length and quality of life are frequently combined in health technology assessments to derive a single, generic measure of health improvement due to a treatment or medicine. One such measure is given by quality-adjusted life-years (QALYs), typically calculated assuming discrete health states and quality-of-life values. The accurate estimation of QALYs is crucial for informed decision making in health care policy and resource allocation; however, traditional methods often rely on assumptions that are sometimes biologically and statistically inappropriate. This study aims to develop a framework for estimating QALYs in populations where survival data are collected that addresses these limitations. METHODS:A framework for estimating QALYs in continuous time is introduced, based on joint longitudinal-survival models fitted using maximum likelihood; the proposed framework requires patient-level data, including longitudinal health utility values and overall survival. In contrast to the conventional approach, which involves dichotomising health states and separate models, this method allows the estimation of QALYs from a single model while accounting for all the statistical and biological intricacies of the data, providing a more appropriate estimate for cost-effectiveness modelling. The joint modelling approach is validated using Monte Carlo simulation under realistic data-generating mechanisms. RESULTS:Simulations showed that the joint longitudinal-survival modelling approach could recover the true QALYs without bias. Conversely, a comparison method based on calculating QALYs directly from the health utility trajectories, ignoring the survival process, was biased under most scenarios. CONCLUSIONS:A new methodology for estimating QALYs within a unified, flexible and extensible framework has been developed. This approach can improve QALY estimation and their use in cost-effectiveness analyses in practice, enabling more timely and robust health technology assessments. User-friendly Stata software is provided.
BACKGROUND:Best-worst scaling (BWS) has been increasingly used as a values clarification method to help patients make value-concordant decisions. However, evidence on its use remains limited. OBJECTIVE:To examine 1) the association between BWS-derived treatment preference scores and patients' stated and real-world treatment choices and 2) whether BWS-identified best-match treatments were concordant with treatment choices among older adults with end-stage kidney disease (ESKD). METHODS:We conducted a prospective study among patients aged ≥70 y with incident ESKD in Singapore at the time of decision making. During a renal counseling session with a decision aid, participants completed 2 BWS exercises to clarify values related to treatment choices (dialysis vs kidney supportive care [KSC] and hemodialysis vs peritoneal dialysis). BWS responses were scored by assigning preference weights to treatment-related attributes and aggregating them into continuous treatment preference scores for each treatment option. Logistic regression assessed associations between dialysis preference scores and postcounseling preference for dialysis and dialysis initiation at 6 mo. Concordance was assessed by comparing the BWS-derived best-match treatment, postcounseling preferred treatment, and real-world treatment choice at 6 mo using the Stuart-Maxwell test. RESULTS:Twenty-three patients were enrolled (mean age 77.9 ± 4.7 y; 65% male). Higher BWS scores favoring dialysis were associated with greater odds of preferring dialysis after counseling (odds ratio = 1.31; P = 0.018) and initiating dialysis at 6 mo (odds ratio = 1.41; P = 0.012). For the dialysis and vs decision, the Stuart-Maxwell test indicated no significant differences; 70% selected (P = 0.70) and 65% initiated (P = 0.26) a treatment concordant with the BWS-derived best-match option. CONCLUSIONS:This study provides preliminary evidence that BWS-based values clarification is associated with treatment preferences and subsequent real-world treatment initiation among older adults with ESKD.
