Medical Decision Making最新文献

Objectives: To examine awareness of disease incurability among patients with heart failure over 24 mo and its associations with patient characteristics and patient-reported outcomes (distress, emotional, and spiritual well-being).

Methods: This study analyzed 24-mo data from a prospective cohort study of 251 patients with heart failure (New York Heart Association class III/IV) recruited from inpatient wards in Singapore General Hospital and National Heart Centre Singapore. Patients were asked to report if their doctor told them they were receiving treatment to cure their condition. "No" responses were categorized as being aware of disease incurability, while "Yes" and "Uncertain" were categorized as being unaware and being uncertain about disease incurability, respectively. We used mixed-effects multinomial logistic regression to investigate the associations between awareness of disease incurability and patient characteristics and mixed-effects linear regressions to investigate associations with patient outcomes.

Results: The percentage of patients who were aware of disease incurability increased from 51.6% at baseline to 76.4% at 24-mo follow-up (P < 0.001). Compared with being unaware of disease incurability, being aware was associated with older age (relative risk ratio [RRR] = 1.04; P = 0.005), adequate self-care confidence (RRR = 5.06; P < 0.001), participation in treatment decision making (RRR = 2.13; P = 0.006), higher education (RRR = 2.00; P = 0.033), financial difficulty (RRR = 1.18; P = 0.020), symptom burden (RRR = 1.08; P = 0.001), and ethnicity (P < 0.05). Compared with being unaware of disease incurability, being aware was associated with higher emotional well-being (β = 0.76; P = 0.024), while being uncertain about disease incurability was associated with poorer spiritual well-being (β = -3.16; P = 0.006).

Conclusions: Our findings support the importance of being aware of disease incurability, addressing uncertainty around disease incurability among patients with heart failure, and helping patients make informed medical decisions. The findings are important to Asian and other cultures where the prognosis disclosure to terminally ill patients is generally low with an intention to "protect" patients.

Highlights: Our 24-mo study with heart failure patients showed an increase from 52% to 76% in patients being aware of disease incurability.Compared with being unaware of disease incurability, being aware was associated with higher emotional well-being, while uncertainty about disease incurability was associated with poorer spiritual well-being.

Purpose: We aim to assess the performance of methods for adjusting estimates of treatment effectiveness for patient nonadherence in the context of health technology assessment using simulation methods.

Methods: We simulated trial datasets with nonadherence, prognostic characteristics, and a time-to-event outcome. The simulated scenarios were based on a trial investigating immunosuppressive treatments for improving graft survival in patients who had had a kidney transplant. The primary estimand was the difference in restricted mean survival times in all patients had there been no nonadherence. We compared generalized methods (g-methods; marginal structural model with inverse probability of censoring weighting [IPCW], structural nested failure time model [SNFTM] with g-estimation) and simple methods (intention-to-treat [ITT] analysis, per-protocol [PP] analysis) in 90 scenarios each with 1,900 simulations. The methods' performance was primarily assessed according to bias.

Results: In implementation nonadherence scenarios, the average percentage bias was 20% (ranging from 7% to 37%) for IPCW, 20% (8%-38%) for SNFTM, 20% (8%-38%) for PP, and 40% (20%-75%) for ITT. In persistence nonadherence scenarios, the average percentage bias was 26% (9%-36%) for IPCW, 26% (14%-39%) for SNFTM, 26% (14%-36%) for PP, and 47% (16%-72%) for ITT. In initiation nonadherence scenarios, the percentage bias ranged from -29% to 110% for IPCW, -34% to 108% for SNFTM, -32% to 102% for PP, and between -18% and 200% for ITT.

Conclusion: In this study, g-methods and PP produced more accurate estimates of the treatment effect adjusted for nonadherence than the ITT analysis did. However, considerable bias remained in some scenarios.

Highlights: Randomized controlled trials are usually analyzed using the intention-to-treat (ITT) principle, which produces a valid estimate of effectiveness relating to the underlying trial, but when patient adherence to medications in the real world is known to differ from that observed in the trial, such estimates are likely to result in a biased representation of real-world effectiveness and cost-effectiveness.Our simulation study demonstrates that generalized methods (g-methods; IPCW, SNFTM) and per-protocol analysis provide more accurate estimates of the treatment effect than the ITT analysis does, when adjustment for nonadherence is required; however, even with these adjustment methods, considerable bias may remain in some scenarios.When real-world adherence is expected to differ from adherence observed in a trial, adjustment methods should be used to provide estimates of real-world effectiveness.

Background: Multi-indication cancer drugs receive licensing extensions to include additional indications, as trial evidence on treatment effectiveness accumulates. We investigate how sharing information across indications can strengthen the inferences supporting health technology assessment (HTA).

Methods: We applied meta-analytic methods to randomized trial data on bevacizumab, to share information across oncology indications on the treatment effect on overall survival (OS) or progression-free survival (PFS) and on the surrogate relationship between effects on PFS and OS. Common or random indication-level parameters were used to facilitate information sharing, and the further flexibility of mixture models was also explored.

Results: Treatment effects on OS lacked precision when pooling data available at present day within each indication separately, particularly for indications with few trials. There was no suggestion of heterogeneity across indications. Sharing information across indications provided more precise estimates of treatment effects and surrogacy parameters, with the strength of sharing depending on the model. When a surrogate relationship was used to predict treatment effects on OS, uncertainty was reduced only when sharing effects on PFS in addition to surrogacy parameters. Corresponding analyses using the earlier, sparser (within and across indications) evidence available for particular HTAs showed that sharing on both surrogacy and PFS effects did not notably reduce uncertainty in OS predictions. Little heterogeneity across indications meant limited added value of the mixture models.

Conclusions: Meta-analysis methods can be usefully applied to share information on treatment effectiveness across indications in an HTA context, to increase the precision of target indication estimates. Sharing on surrogate relationships requires caution, as meaningful precision gains in predictions will likely require a substantial evidence base and clear support for surrogacy from other indications.

Highlights: We investigated how sharing information across indications can strengthen inferences on the effectiveness of multi-indication treatments in the context of health technology assessment (HTA).Multi-indication meta-analysis methods can provide more precise estimates of an effect on a final outcome or of the parameters describing the relationship between effects on a surrogate endpoint and a final outcome.Precision of the predicted effect on the final outcome based on an effect on the surrogate endpoint will depend on the precision of the effect on the surrogate endpoint and the strength of evidence of a surrogate relationship across indications.Multi-indication meta-analysis methods can be usefully applied to predict an effect on the final outcome, particularly where there is limited evidence in the indication of interest.

Background: Evidence is lacking on the most effective values clarification methods (VCMs) in patient decision aids (PtDAs). We tested the effects of an adaptive conjoint analysis (ACA)-based VCM compared with a ranking-based VCM and no VCM on several decision-related outcomes, with the decisional conflict and its subscale "perceived values clarity" as primary outcomes.

Design: Online experimental study with 3 conditions: no VCM versus ranking-based VCM versus ACA-based VCM (N = 282; M_age = 63.11 y, s = 12.12), with the latter 2 conditions including attributes important for a lung cancer treatment decision. We assessed 1) decisional conflict, 2) perceived values clarity (decisional conflict subscale), 3) perceived cognitive load, 4) anticipated regret, 5) ambivalence, 6) preparedness for decision making, 7) hypothetical treatment preference, and 8) values congruence (proxy). We performed analysis of covariance and linear regression. Age and level of deliberation were included as potential moderators, and we controlled for subjective numeracy (covariate). We exploratively tested the moderating effects of subjective numeracy and health literacy (without covariates).

Results: We found no significant effect of type of VCM on overall decisional conflict or perceived values clarity. Age had a moderating effect: in younger participants, no VCM (v. ranking-based VCM) led to more values clarity, while in older participants, a ranking-based VCM (v. no VCM) led to more values clarity. Completing the ACA-based VCM, compared with no VCM, resulted in more values congruence.

Limitations: The hypothetical choice situation might have induced lower levels of cognitive/affective involvement in the decision.

Conclusions: This study found mixed effects of an ACA-based VCM. It did not decrease decisional conflict or increase perceived values clarity, yet it did improve values congruence.

Implications: Completion of an ACA-based VCM in a PtDA may increase values congruence.

Highlights: An adaptive conjoint analysis or a ranking-based values clarification method did not decrease analog patients' decisional conflict nor did it increase their perceived values clarity.In younger participants, no VCM (v. ranking-based VCM) led to more values clarity, while in older participants, a ranking-based VCM (v. no VCM) led to more values clarity.An adaptive conjoint analysis task for values clarification resulted in more values congruence.

Background: Estimating change in health-related quality of life (HRQOL) from pre- to poststroke is challenging because HRQOL is rarely collected prior to stroke. Leveraging HRQOL data collected both before and after stroke, we sought to estimate the change in HRQOL from prestroke to early poststroke.

Methods: Stroke survivors completed the Patient-Reported Outcomes Measurement Information System Global Health (PROMIS-GH) scale at both pre- and early poststroke. Patient characteristics were compared for those who did and did not complete the PROMIS-GH. The mean change in PROMIS-GH T-score was estimated using complete case analysis, multiple imputation, and multiple imputation with delta adjustment.

Results: A total of 4,473 stroke survivors were included (mean age 63.1 ± 14.1 y, 47.5% female, 82.6% ischemic stroke). A total of 993 (22.2%) patients completed the PROMIS-GH at prestroke while 2,298 (51.4%) completed it early poststroke. Compared with those without PROMIS-GH, patients with PROMIS-GH prestroke had worse comorbidity burden. Patients who completed PROMIS-GH early poststroke had better early poststroke clinician-rated function and shorter hospital length of stay. Complete case analysis and multiple imputation revealed patients' PROMIS-GH T-scores worsened by 2 to 3 points. Multiple imputation with delta adjustment revealed patients' PROMIS-GH T-scores worsened by 4 to 10 points, depending on delta values chosen.

Conclusions: Systematic differences in patients who completed the PROMIS-GH at both pre- and early poststroke suggest that missing PROMIS-GH scores may be missing not at random (MNAR). Multiple imputation with delta adjustment, which is better suited for MNAR data, may be a preferable method for analysis of change in HRQOL from pre- to poststroke. Given our study's large proportion of missing HRQOL data, future studies with less missing HRQOL data are necessary to verify our results.

Highlights: Estimating the change in health-related quality of life from pre- to poststroke is challenging because health-related quality-of-life data are rarely collected prior to stroke. Previously used methods to assess the burden of stroke on health-related quality of life suffer from recall bias and selection bias.Using health-related quality-of-life data collected both before and after stroke, we sought to estimate the change in health-related quality of life after stroke using statistical methods that account for missing data.Comparisons of patients who did and did not complete health-related quality-of-life scales at both pre- and poststroke suggested that missing data may be missing not at random.Statistical methods that account for data that are missing not at random revealed more worsening in health-related quality of life after stroke than traditional methods such as complete case analysis or multiple imputation.

Background: A theoretical interpretation of factors influencing time tradeoff (TTO) scores is lacking. In this conceptual study, we use a sociopsychological theory, terror management theory (TMT), to explain how death thoughts may play a role in the TTO method. TMT describes how respondents suppress death thoughts by invoking psychological defenses, such as self-esteem, and by bolstering cultural values.

Research question: What is the relation between TMT and TTO scores?

Methods: A framework is developed to link TMT to TTO scores. Predictions of the framework pertain to the directionality of relations between characteristics (e.g., being religious) affecting TTO scores. These predictions are then tested against the findings in the literature.

Results: The value "prolonging life" can be used as a linking pin between TTO and TMT as it is relevant for both TMT and TTO. It is argued that the value "prolonging life" is related to TTO scores but also to TMT defense strengths. Thus, TMT defense strengths become associated with trading. Directionality predictions of the framework were confirmed in 34 out of 39 retrospective tests (P < 0.001).

Conclusion: Directionalities of relations between TTO scores and socioeconomic characteristics, euthanasia, subjective life expectancy, and religion were explained in terms of TMT defense strengths. Our framework offers a theory-based and parsimonious framework to think about characteristics influencing TTO scores.

Highlights: Terror management theory (TMT) is a sociopsychological theory about death thoughts.Several factors are known to influence TTO scores.A new framework applies TMT to TTO scores to interpret such factors.Our framework is mostly of importance to health economists studying the TTO.

Background: Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop an approach that combines clinical judgment with ML to generate appropriate comparative effectiveness evidence for informing decision making.

Methods: We motivate this approach in evaluating the effectiveness of nonemergency surgery (NES) strategies, such as antibiotic therapy, for people with acute appendicitis who have multiple long-term conditions (MLTCs) compared with emergency surgery (ES). Our 4-stage approach 1) draws on clinical judgment about which patient characteristics and morbidities modify the relative effectiveness of NES; 2) selects additional covariates from a high-dimensional covariate space (P > 500) by applying an ML approach, least absolute shrinkage and selection operator (LASSO), to large-scale administrative data (N = 24,312); 3) generates estimates of comparative effectiveness for relevant subgroups; and 4) presents evidence in a suitable form for decision making.

Results: This approach provides useful evidence for clinically relevant subgroups. We found that overall NES strategies led to increases in the mean number of days alive and out-of-hospital compared with ES, but estimates differed across subgroups, ranging from 21.2 (95% confidence interval: 1.8 to 40.5) for patients with chronic heart failure and chronic kidney disease to -10.4 (-29.8 to 9.1) for patients with cancer and hypertension. Our interactive tool for visualizing ML output allows for findings to be customized according to the specific needs of the clinical decision maker.

Conclusions: This principled approach of combining clinical judgment with an ML approach can improve trust, relevance, and usefulness of the evidence generated for clinical decision making.

Highlights: Machine learning (ML) methods have many potential applications in medical decision making, but the lack of model interpretability and usability constitutes an important barrier for the wider adoption of ML evidence in practice.We develop a 4-stage approach for integrating clinical judgment into the way an ML approach is used to estimate and report comparative effectiveness.We illustrate the approach in undertaking an evaluation of nonemergency surgery (NES) strategies for acute appendicitis in patients with multiple long-term conditions and find that NES strategies lead to better outcomes compared with emergency surgery and that the effects differ across subgroups.We develop an interactive tool for visualizing the results of this study that allows findings to be customized according to the user's preferences.

Background: Nudges have been proposed as a method of influencing prescribing decisions.

Purpose: The purpose of this article is to 1) investigate associations between nudges' characteristics and effectiveness, 2) assess the quality of the literature, 3) assess cost-effectiveness, and 4) create a synthesis with policy recommendations.

Methods: We searched health and social science databases. We included studies that targeted prescribing decisions, included a nudge, and used prescribing behavior as the outcome. We recorded study characteristics, effect size of the primary outcomes, and information on cost-effectiveness. We performed a meta-analysis on the standardized mean difference of the studies' primary outcomes, tested for associations between effect size and key intervention characteristics, and created a funnel plot evaluating publication bias.

Synthesis: We identified 21 studies containing 25 nudges. In total, 62 of 85 (73%) outcomes showed a statistically significant effect. The average effect size was -0.22 standardized mean difference. No studies included heterogeneity analyses. We found no associations between effects and selected study characteristics. Study quality varied and correlated with study design. A total of 7 of 21 (33%) studies included an evaluation of costs. These studies suggested that the interventions were cost-effective but considered only direct effects. We found evidence of publication bias.

Limitations: Heterogeneity and few studies limit the possibilities of statistical inference about effectiveness.

Conclusions: Nudges may be effective at directing prescribing decisions, but effects are small and health effects and cost-effectiveness are unclear. Future nudge studies should contain a rationale for the chosen nudge, prioritize the use of high-quality study designs, and include evaluations of heterogeneity, cost-effectiveness, and health outcomes to inform decision makers. Moreover, preregistration of the protocol is warranted to limit publication bias.

Highlights: Nudging as a method to improve prescribing decisions has gained popularity during the past decade.We find that nudging can improve prescribing decisions, but effect sizes are mostly small, and the size of derived health outcomes is unclear.Most studies use feedback and error-stopping nudges to target excessive opioid or antibiotic prescribing, making heterogeneity analyses across nudge types difficult.Further research on the cost-effectiveness of nudges and generalizability is needed to guide decision makers considering nudging as a tool to guide prescribing decisions.