Medical Decision Making[JOURNAL]
Sun
200 papers
Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent.
Goldhaber-Fiebert JD, Jalal H, Alarid-Escudero F
Med Decis Making · 2025 Feb · PMID 39720850 · Full text
PURPOSE: Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision unce...
PURPOSE: Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized. METHODS: We compare iSTM-produced estimates of decision uncertainty and VOI to corresponding cSTMs. For a 2-alternative decision and normally distributed incremental costs and benefits, we derive analytical expressions for the probability of being cost-effective and the expected value of perfect information (EVPI) for cSTMs and iSTMs, accounting for correlations in incremental outcomes at the population and individual levels. We use numerical simulations to illustrate our findings and explore the impact of relaxing normality assumptions or having >2 decision alternatives. RESULTS: iSTM estimates of decision uncertainty and VOI are biased but asymptotically consistent (i.e., bias approaches 0 as number of microsimulated individuals approaches infinity). Decision uncertainty depends on 1 tail of the INMB distribution (e.g., P[INMB <0]), which depends on estimated variance (larger with iSTMs given first-order noise). While iSTMs overestimate EVPI, their direction of bias for the probability of being cost-effective is ambiguous. Bias is larger when uncertainties in incremental costs and effects are negatively correlated since this increases INMB variance. CONCLUSIONS: iSTMs are useful for probabilistic economic evaluations. While more samples at the population uncertainty level are interchangeable with more microsimulations for estimating EINMB, minimizing iSTM bias in estimating decision uncertainty and VOI depends on sufficient microsimulations. Analysts should account for this when allocating their computational budgets and, at minimum, characterize such bias in their reported results. HIGHLIGHTS: Individual-level state-transition microsimulation models (iSTMs) produce biased but consistent estimates of the probability that interventions are cost-effective.iSTMs also produce biased but consistent estimates of the expected value of perfect information.The biases in these decision uncertainty and value-of-information measures are not reduced by more parameter sets being sampled from their population-level uncertainty distribution but rather by more individuals being microsimulated for each parameter set sampled.Analysts using iSTMs to quantify decision uncertainty and value of information should account for these biases when allocating their computational budgets and, at minimum, characterize such bias in their reported results.
Testing an HPV Vaccine Decision Aid for 27- to 45-Year-Old Adults in the United States: A Randomized Trial.
Thompson EL, Luningham J, Alkhatib SA … +5 more , Grace J, Akpan IN, Daley EM, Zimet GD, Wheldon CW
Med Decis Making · 2025 Feb · PMID 39717960 · Full text
BACKGROUND: In the United States, human papillomavirus (HPV) vaccination among 27- to 45-y-olds (mid-adults) is recommended based on shared clinical decision making with a health care provider. We developed a patient dec...
BACKGROUND: In the United States, human papillomavirus (HPV) vaccination among 27- to 45-y-olds (mid-adults) is recommended based on shared clinical decision making with a health care provider. We developed a patient decision aid tool to support the implementation of this mid-adult HPV vaccination guideline. The purpose of this study was to evaluate the effect of a patient decision aid tool for HPV vaccination, HPV DECIDE, compared with an information fact sheet among mid-adults who have not received the HPV vaccine. METHOD: Participants were recruited between December 2023 and January 2024. We used a randomized Solomon, 4-group, pretest/posttest design with mid-adults aged 27 to 45 y who were unvaccinated for HPV and balanced based on sex ( = 612). The primary outcome was decisional conflict. Intermediate outcomes included knowledge, behavioral expectancies, self-efficacy, and perceived risk. Variables were measured using validated scales. Pretest sensitization was not present; intervention and control groups were compared. Fixed-effects inverse-variance weighting was used to pool effect estimates and determine meta-analytic statistical significance across tests with and without pretest controls. RESULTS: Participants in the intervention group had significantly lower total decisional conflict scores (B = -3.58, = 0.007) compared with the control group. Compared with the control group, participants in the intervention group showed higher knowledge (B = 0.48, = 0.020), greater intention to receive (B = 0.196, = 0.049) and discuss the HPV vaccine (B = 0.324, ≤ 0.001), and greater self-efficacy about HPV vaccine decision making (B = 3.28, = 0.043). There were no statistically significant results for perceived risks of HPV infection. CONCLUSIONS: The HPV DECIDE tool for mid-adult HPV vaccination shows promise for immediate reductions in decisional conflict and improvement in knowledge, intentions, and self-efficacy about the HPV vaccine. Future studies are warranted to evaluate the effectiveness of this patient decision aid tool in real-world settings. HIGHLIGHTS: Shared clinical decision making is recommended for HPV vaccination with mid-adults.A patient decision aid for HPV vaccination reduced decisional conflict for mid-adults.The HPV vaccine patient decision aid was acceptable to mid-adults.
A Parsimonious Approach to Remediate Concerns about QALY-Based Discrimination.
Braithwaite RS
Med Decis Making · 2025 Feb · PMID 39707829 · Publisher ↗
Important barriers to the use of QALYs in the United States include concerns about disability and age discrimination.Modifications to the utility function underlying QALYs have been proposed to mitigate these concerns, b...
Important barriers to the use of QALYs in the United States include concerns about disability and age discrimination.Modifications to the utility function underlying QALYs have been proposed to mitigate these concerns, but some find them challenging to consider and/or to apply.Unrelated to these concerns, QALYs have been adapted within the framework of distributional cost-effectiveness analysis to allow consideration of inequality as well as efficiency.I outline how this framework can also remediate concerns about disability and age discrimination.
Physician-Patient Communication about Novel Drugs and High-Risk Medical Devices.
Dhruva SS, Kesselheim AS, Woloshin S … +4 more , Ji RZ, Lu Z, Darrow JJ, Redberg RF
Med Decis Making · 2025 Feb · PMID 39707817 · Full text
BACKGROUND: After a new drug or medical device is approved by the US Food and Drug Administration (FDA), physician-patient communication about benefits and risks is critical, including whether the product was approved th...
BACKGROUND: After a new drug or medical device is approved by the US Food and Drug Administration (FDA), physician-patient communication about benefits and risks is critical, including whether the product was approved through an expedited pathway based on limited evidence. In addition, physician reporting of drug- and device-related adverse events in real-world use is necessary to have a complete safety profile. We studied physician-reported communication and safety-reporting practices related to drugs and devices. METHODS: We surveyed a random national sample of American Board of Internal Medicine-certified internists, cardiologists, and oncologists between October 2021 and September 2022 about the sources of information used to prescribe a drug or medical device, details of communication with patients, and reporting of adverse events. RESULTS: Among 509 respondents (39% response rate), 387 (76%) reported that FDA approval influenced their decision "a lot" to prescribe a new drug or recommend use of a medical device. Half (122; 50%) of the 244 physicians randomized to receive a question about their own communication of trial endpoints reported "usually" telling patients when products were approved based on surrogate measures and 126 (52%) "usually" reported telling patients if a postapproval trial was required to evaluate safety and effectiveness. Two-thirds (165) said they were likely to report drug- or device-related adverse events to FDA. CONCLUSIONS: Physician self-reporting of communication with patients about drugs and devices suggests that half include characteristics of the pivotal trials such as use of clinically meaningful endpoints or continued requirement for evidence generation. IMPLICATIONS: More consistent discussions with patients about the quality of evidence supporting new drugs and devices and increased reporting of adverse events could ensure optimal use of these products in clinical practice. HIGHLIGHTS: Among 509 board-certified internists, cardiologists, and oncologists, half reported telling patients when drugs or medical devices were approved based on surrogate measures and when there was an FDA-mandated postapproval trial to further evaluate safety and effectiveness.As drugs and medical devices are increasingly approved by the FDA through expedited pathways based on data with lingering uncertainties, discussion with patients about issues such as the nature of the endpoints assessed and existence of postapproval testing requirements can help inform patient decision making.
Communicating on Vaccine Benefit-Risk Ratios: A Discrete-Choice Experiment among Health Care Professionals and the General Population in France.
Chaveron LA, Sicsic J, Olivier C … +3 more , Pellissier G, Bouvet E, Mueller JE
Med Decis Making · 2025 Feb · PMID 39692261 · Publisher ↗
BACKGROUND: We explored preferences around the benefit-risk ratio (BRR) of vaccination among the general adult population and health care sector workers (HCSWs). We estimated preference weights and expected vaccine uptak...
BACKGROUND: We explored preferences around the benefit-risk ratio (BRR) of vaccination among the general adult population and health care sector workers (HCSWs). We estimated preference weights and expected vaccine uptake for different BRR levels for a vaccine recommended during an infectious disease emergence. In addition, we explored how far qualitative information about disease severity, epidemiological context, and indirect protection interacts with these preferences. METHODOLOGY: This was a cross-sectional study, using a self-administered online questionnaire containing a single-profile discrete choice experiment among HCSWs and the general population in France (quasi-representative sample). The questionnaire was available from January 12 to April 27, 2023, for HCSWs and from April 17 to May 3, 2023, for the general population. BRR is represented as the number of vaccine-prevented disease events for 1 event related to a vaccine side effect. Results are reported in 4 groups: general population sample, non-HCSWs, non-university-degree HCSWs, and university-degree HCSWs. RESULTS: Among the 1,869 participants, 1,038 (55.5%) varied their vaccine decision among the different vaccine scenarios. Hypothetical vaccine acceptance among university-degree HCSWs increased when the vaccination BRR was 100:1, while non-university-degree HCSWs and non-HCSWs were more sensitive to qualitative information about the vaccine BRR than quantitative indicators. Among participants in the general population sample with varied decisions, expected acceptance increased by 40% sample if disease risk was high. Among serial vaccine nondemanders, high disease risk decreased their certitude to refuse hypothetical vaccination. CONCLUSION: Our results suggest that only university-degree HCSWs are sensitive to the notion of BRR, but not the general public. Given that previous research found speaking about BRR might reduce vaccine acceptance, this notion should be avoided in vaccine promotion. HIGHLIGHTS: The notion of benefit-risk ratio (BRR) of vaccination appears to be taken into account in vaccine decisions by university-degree HCSWs, but not by the general public. Mentioning a favorable BRR could imply that the vaccine is not safe and reduce vaccine motivation.Mentioning qualitative attributes of BRR surrounding disease frequency and severity, and indirect protection effects, strongly affected theoretical vaccine decisions in all participants, irrespective of professional categories.Expected vaccine acceptance increased by 40% among the general population sample if disease risk was presented as high, and expected vaccine coverage exceeded 50% in scenarios with high disease risk.Among those refusing vaccination in all vaccine scenarios, only a high risk of developing the disease decreased their certitude to refuse vaccination. This further underlines the importance of disease risk perception on vaccine decision making, including among persons who a priori are unlikely to accept vaccination.
How Inclusive Are Patient Decision Aids for People with Limited Health Literacy? An Analysis of Understandability Criteria and the Communication about Options and Probabilities.
Richter R, Jansen J, van der Kraan J … +6 more , Abbaspoor W, Bongaerts I, Pouwels F, Vilters C, Rademakers J, van der Weijden T
Med Decis Making · 2025 Feb · PMID 39673417 · Full text
OBJECTIVE: Patient decision aids (PtDAs) can support shared decision making. We aimed to explore how inclusive PtDAs are for people with limited health literacy (LHL) by analyzing 1) the understandability of PtDAs using...
OBJECTIVE: Patient decision aids (PtDAs) can support shared decision making. We aimed to explore how inclusive PtDAs are for people with limited health literacy (LHL) by analyzing 1) the understandability of PtDAs using established criteria, 2) how options and probabilities of outcomes are communicated, and 3) the extent to which risk communication (RC) guidelines are followed. METHODS: In a descriptive document analysis, we analyzed Dutch PtDAs available in 2021 that met the International Patient Decision Aid Standards. We developed and pilot tested a data extraction form based on key RC and health literacy literature. RESULTS: Most PtDAs (151/198) met most of the understandability criteria on layout (7-8 out of 8 items) such as font size but not on content aspects (121/198 PtDAs scored 5-7 out of 12 items) such as defining medical terms. Only 31 of 198 PtDAs used a short and simple sentence structure. Most PtDAs presented 2 to 4 treatment options. Many followed RC recommendations such as the use of numerical RC strategies such as percentages or natural frequencies (160/198) and visual formats such as icon arrays (91/198). Only 10 used neutral framing (10/198). When presented, uncertainty was presented verbally (134/198) or in ranges (58/198). Four PtDAs were co-created together with patients with LHL and used only verbal RC or no RC. CONCLUSION: Most PtDAs met most of the understandability criteria on layout, but content aspects and adherence to RC strategies can be improved. Many PtDAs used long sentences and mostly verbal RC and are therefore likely to be inappropriate for patients with LHL. Further research is needed on PtDA characteristics and RC strategies suitable for people with LHL. HIGHLIGHTS: Despite meeting most criteria for understandability, many of the Dutch PtDAs use long sentences, which likely impede comprehension for patients with LHL.Most of the Dutch PtDAs follow established recommendations for risk communication, with room for improvement for some strategies such as framing and a clear reference to the time frame.Overall, more research is needed to tailor PtDAs to the needs of people with LHL.
Veterans' Lung Cancer Risk Conceptualizations versus Lung Cancer Screening Shared Decision-Making Conversations with Clinicians: A Qualitative Study.
Boudreau JH, Bolton RE, Núñez ER … +6 more , Caverly TJ, Kearney L, Sliwinski S, Herbst AN, Slatore CG, Wiener RS
Med Decis Making · 2025 Jan · PMID 39575828 · Publisher ↗
BACKGROUND: The Veterans Health Administration (VA) recommends lung cancer screening (LCS), including shared decision making between clinicians and veteran patients. We sought to characterize 1) veteran conceptualization...
BACKGROUND: The Veterans Health Administration (VA) recommends lung cancer screening (LCS), including shared decision making between clinicians and veteran patients. We sought to characterize 1) veteran conceptualization of lung cancer risk and 2) veteran and clinician accounts of shared decision-making discussions about LCS to assess whether they reflect veteran concerns. METHODS: We conducted qualitative interviews at 6 VA sites, with 48 clinicians and 34 veterans offered LCS in the previous 6 mo. We thematically analyzed transcripts, focusing on lung cancer risk perceptions, LCS decision making, and patient-clinician conversations. RESULTS: Three themes emerged. 1) Veterans' lung cancer risk conceptualizations incorporated smoking, occupational hazards, and family history, whereas clinicians focused on smoking as the primary risk factor. 2) Veterans' risk perceptions were influenced by symptoms, recency of exposures, and anecdotes about smoking, cancer, and lung disease, leading some veterans to believe other risk factors outweighed smoking in increasing lung cancer risk. 3) Both veterans and clinicians described LCS conversations centered on smoking, with little mention of other risks. LIMITATIONS: Our findings may not reflect non-VA settings; for example, veterans may be more concerned about airborne hazards. CONCLUSIONS: While airborne hazards strongly influenced veterans' lung cancer risk conceptualizations, clinicians seldom addressed this risk factor during LCS shared decision making, instead focusing on smoking. IMPLICATIONS: In 2022, the US Congress highlighted the link between military toxic exposures and lung cancer risk, requiring VA clinicians to discuss these exposures and conferring automatic VA benefits to exposed veterans with cancer. There is a time-sensitive need for tools to support VA clinicians in discussing military hazards as a lung cancer risk factor, which may result in more engaging, less stigmatizing LCS shared decision-making conversations. HIGHLIGHTS: Veterans' conceptualizations of their lung cancer risk were multifactorial and sometimes ranked exposure to occupational airborne hazards and family history above smoking in increasing lung cancer risk.However, patient-clinician lung cancer screening (LCS) conversations were typically brief and focused on smoking, which could stigmatize patients and failed to engage veterans in discussing what mattered most to them in thinking about their lung cancer risk.These findings are of heightened importance in light of the 2022 Sergeant First Class Heath Robinson Honoring our Promise to Address Comprehensive Toxics (PACT) Act, which requires VA clinicians to discuss toxic military exposures and their relationship to lung cancer and other health conditions.Tools that help clinicians assess and incorporate multiple risk factors into discussions about lung cancer may better address patients' concerns and beliefs and lead to more engaging, less stigmatizing shared decision-making conversations about LCS.
Unclear Trajectory and Uncertain Benefit: Creating a Lexicon for Clinical Uncertainty in Patients with Critical or Advanced Illness Using a Delphi Consensus Process.
McGowan SK, Corrales-Martinez MJ, Brender T … +7 more , Smith AK, Kim S, Harrison KL, Mills H, Lee A, Bamman D, Cobert J
Med Decis Making · 2025 Jan · PMID 39559986 · Full text
BACKGROUND: Clinical uncertainty is associated with increased resource utilization, worsened health-related quality of life for patients, and provider burnout, particularly during critical illness. Existing data are limi...
BACKGROUND: Clinical uncertainty is associated with increased resource utilization, worsened health-related quality of life for patients, and provider burnout, particularly during critical illness. Existing data are limited, because determining uncertainty from notes typically requires manual, qualitative review. We sought to develop a consensus list of descriptors of clinical uncertainty and then, using a thematic analysis approach, describe how respondents consider their use in intensive care unit (ICU) notes, such that future work can extract uncertainty data at scale. DESIGN: We conducted a Delphi consensus study with physicians across multiple institutions nationally who care for critically ill patients or patients with advanced illnesses. Participants were given a definition for clinical uncertainty and collaborated through multiple rounds to determine which words represent uncertainty in clinician notes. We also administered surveys that included open-ended questions to participants about clinical uncertainty. Following derivation of a consensus list, we analyzed participant responses using thematic analysis to understand the role of uncertainty in clinical documentation. RESULTS: Nineteen physicians participated in at least 2 of the Delphi rounds. Consensus was achieved for 44 words or phrases over 5 rounds of the Delphi process. Clinicians described comfort with using uncertainty terms and used them in a variety of ways: documenting and processing the diagnostic thinking process, enlisting help, identifying incomplete information, and practicing transparency to reflect uncertainty that was present. CONCLUSIONS: Using a consensus process, we created an uncertainty lexicon that can be used for uncertainty data extraction from the medical record. We demonstrate that physicians, particularly in the ICU, are comfortable with uncertainty and document uncertainty terms frequently to convey the complexity and ambiguity that is pervasive in critical illness. HIGHLIGHTS: Question: What words do physicians caring for critically ill patients use to document clinical uncertainty, and why?Findings: A consensus list of 44 words or phrases was identified by a group of experts. Physicians expressed comfort with using these words in the electronic health record.Meaning: Physicians are comfortable with uncertainty words and document them frequently to convey the complexity and ambiguity that is pervasive in critical illness.
Multi-indication Evidence Synthesis in Oncology Health Technology Assessment: Meta-analysis Methods and Their Application to a Case Study of Bevacizumab.
Singh J, Anwer S, Palmer S … +5 more , Saramago P, Thomas A, Dias S, Soares MO, Bujkiewicz S
Med Decis Making · 2025 Jan · PMID 39555661 · Full text
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 indication...
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.
Use of Adaptive Conjoint Analysis-Based Values Clarification in a Patient Decision Aid Is Not Associated with Better Perceived Values Clarity or Reduced Decisional Conflict but Enhances Values Congruence.
Yılmaz NG, Pieterse AH, Timmermans DRM … +3 more , Becker A, Witte-Lissenberg B, Damman OC
Med Decis Making · 2025 Jan · PMID 39555657 · Full text
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-b...
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 -based VCM ( = 282; = 63.11 y, = 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.
A Longitudinal Study of the Association of Awareness of Disease Incurability with Patient-Reported Outcomes in Heart Failure.
Lee JJ, Malhotra C, Sim KLD … +3 more , Yeo KK, Finkelstein E, Ozdemir S
Med Decis Making · 2025 Jan · PMID 39545423 · Publisher ↗
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 we...
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 ( < 0.001). Compared with being unaware of disease incurability, being aware was associated with older age (relative risk ratio [RRR] = 1.04; = 0.005), adequate self-care confidence (RRR = 5.06; < 0.001), participation in treatment decision making (RRR = 2.13; = 0.006), higher education (RRR = 2.00; = 0.033), financial difficulty (RRR = 1.18; = 0.020), symptom burden (RRR = 1.08; = 0.001), and ethnicity ( < 0.05). Compared with being unaware of disease incurability, being aware was associated with higher emotional well-being (β = 0.76; = 0.024), while being uncertain about disease incurability was associated with poorer spiritual well-being (β = -3.16; = 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.
A Sequential Calibration Approach to Address Challenges of Repeated Calibration of a COVID-19 Model.
Enns EA, Li Z, McKearnan SB … +6 more , Kao SZ, Sanstead EC, Simon AB, Mink PJ, Gildemeister S, Kuntz KM
Med Decis Making · 2025 Jan · PMID 39545378 · Publisher ↗
BACKGROUND: Mathematical models served a critical role in COVID-19 decision making throughout the pandemic. Model calibration is an essential, but often computationally burdensome, step in model development that provides...
BACKGROUND: Mathematical models served a critical role in COVID-19 decision making throughout the pandemic. Model calibration is an essential, but often computationally burdensome, step in model development that provides estimates for difficult-to-measure parameters and establishes an up-to-date modeling platform for scenario analysis. In the evolving COVID-19 pandemic, frequent recalibration was necessary to provide ongoing support to decision makers. In this study, we address the computational challenges of frequent recalibration with a new calibration approach. METHODS: We calibrated and recalibrated an age-stratified dynamic compartmental model of COVID-19 in Minnesota to statewide COVID-19 cumulative mortality and prevalent age-specific hospitalizations from March 22, 2020 through August 20, 2021. This period was divided into 10 calibration periods, reflecting significant changes in policies, messaging, and/or epidemiological conditions in Minnesota. When recalibrating the model from one period to the next, we employed a sequential calibration approach that leveraged calibration results from previous periods and adjusted only parameters most relevant to the calibration target data of the new calibration period to improve computational efficiency. We compared computational burden and performance of the sequential calibration approach to a more traditional calibration method, in which all parameters were readjusted with each recalibration. RESULTS: Both calibration methods identified parameter sets closely reproducing prevalent hospitalizations and cumulative deaths over time. By the last calibration period, both approaches converged to similar parameter values. However, the sequential calibration approach identified parameter sets that more tightly fit calibration targets and required substantially less computation time than traditional calibration. CONCLUSIONS: Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters and potentially identifies better-fitting parameter sets than traditional calibration. HIGHLIGHTS: This study used a sequential calibration approach, which takes advantage of previous calibration results to reduce the number of parameters to be estimated in each round of calibration, improving computational efficiency and algorithm convergence to best-fitting parameter values.Both sequential and traditional calibration approaches were able to identify parameter sets that closely reproduced calibration targets. However, the sequential calibration approach generated parameter sets that yielded tighter fits and was less computationally burdensome.Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters.
Awareness of Disease Incurability Moderates the Association between Patients' Health Status and Their Treatment Preferences.
Poco LC, Balasubramanian I, Chaudhry I … +1 more , Malhotra C
Med Decis Making · 2025 Jan · PMID 39520110 · Publisher ↗
BACKGROUND: With advancing illness, some patients with heart failure (HF) opt to receive life-extending treatments despite their high costs, while others choose to forgo these treatments, emphasizing cost containment. We...
BACKGROUND: With advancing illness, some patients with heart failure (HF) opt to receive life-extending treatments despite their high costs, while others choose to forgo these treatments, emphasizing cost containment. We examined the association between patients' health status and their preferences for treatment cost containment versus life extension and whether their patients' awareness of disease incurability moderated this association. METHODS: In a prospective cohort of patients ( = 231) with advanced HF in Singapore, we assessed patients' awareness of disease incurability, health status, and treatment preferences every 4 mo for up to 4 y (up to 13 surveys). Using random effects multinomial logistic regression models, we assessed whether patients' awareness of disease incurability moderated the association between their health status and treatment preferences. RESULTS: About half of the patients in our study lacked awareness of HF's incurability. Results from regression analyses showed that patients with better health status, as indicated by lower distress scores (odds ratio [OR] [95% confidence interval {CI}]: 0.862 [0.754, 0.985]) and greater physical well-being (1.12 [1.03, 1.21]); and who lacked awareness of their disease's incurability were more likely to prefer higher cost containment/minimal life extension treatments compared with lower cost containment/maximal life extension. CONCLUSIONS: This study underscores the significance of patients' awareness in disease incurability in shaping the relationship between their health status and treatment preferences. Our findings emphasize the need to incorporate illness education during goals-of-care conversations with patients and the importance of revisiting these conversations frequently to accommodate changing treatment preferences. HIGHLIGHTS: The health status of patients with advanced heart failure was associated with their treatment preferences.Patients whose health status improved and who lacked awareness of their disease's incurability were more likely to prefer higher cost containment/minimal life extension treatments.
Assessing Methods for Adjusting Estimates of Treatment Effectiveness for Patient Nonadherence in the Context of Time-to-Event Outcomes and Health Technology Assessment: A Simulation Study.
Alshreef A, Latimer N, Tappenden P … +1 more , Dixon S
Med Decis Making · 2025 Jan · PMID 39520046 · Publisher ↗
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 simula...
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.
Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.
Dakin HA, Gao N, Leal J … +3 more , Holman RR, Tran-Duy A, Clarke P
Med Decis Making · 2025 Jan · PMID 39474832 · Full text
OBJECTIVES: (1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and...
OBJECTIVES: (1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q, the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and . METHODS: We simulated all EXSCEL trial participants ( = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period. RESULTS: UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q facilitated comparisons between subgroups and (unlike ) was lower for biased predictors. CONCLUSIONS: Q for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas. HIGHLIGHTS: Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q, the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.
An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions.
Moler-Zapata S, Hutchings A, Grieve R … +7 more , Hinchliffe R, Smart N, Moonesinghe SR, Bellingan G, Vohra R, Moug S, O'Neill S
Med Decis Making · 2024 Nov · PMID 39440442 · Full text
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...
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 ( > 500) by applying an ML approach, least absolute shrinkage and selection operator (LASSO), to large-scale administrative data ( = 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.
Shared Decision Making "Ought" to Be Done, but Definitions Need Simplicity: Response to "Reframing SDM Using Implementation Science: SDM Is the Intervention".
Matlock DD, Scherer L
Med Decis Making · 2024 Nov · PMID 39403872 · Publisher ↗
Abstract loading — click title to view on PubMed.
What Makes the Time Tradeoff Tick? A Sociopsychological Explanation.
Stalmeier PFM, Roudijk B
Med Decis Making · 2024 Nov · PMID 39403849 · Full text
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 th...
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 ( < 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.
Medical Maximizing Orientation and the Desire for Low-Value Screening: An Examination of Mediating Mechanisms.
Kim S
Med Decis Making · 2024 Nov · PMID 39377538 · Publisher ↗
BACKGROUND: Medical maximizing orientation is a stable, traitlike inclination to actively use health care, often associated with pursuing low-value care. Despite attempts to reduce the overuse of low-value care by target...
BACKGROUND: Medical maximizing orientation is a stable, traitlike inclination to actively use health care, often associated with pursuing low-value care. Despite attempts to reduce the overuse of low-value care by targeting this orientation directly, such interventions have not always been effective. To design effective interventions to reduce the overuse of low-value care, it is critical to understand the underlying mechanisms that govern the impact of medical maximizing orientation. OBJECTIVE: To examine whether risk perception (deliberative, affective, and experiential) and knowledge of the benefits and harms of low-value screening mediate the potential impact of medical maximizing orientation on attitudes toward screening uptake and screening decisions. METHODS: A secondary analysis was conducted on data from a Web-based experiment examining various communication tactics in an information booklet regarding low-value thyroid ultrasonography among South Korean women ( = 492). Multiple linear, zero-inflated negative binomial and multinomial logistic regressions were used to examine the relationships between medical maximizing orientation and other study variables. A mediation analysis was performed to test mediating mechanisms. RESULTS: Medical maximizing orientation was associated with an increased positive attitude toward screening uptake and a lower likelihood of deciding not to get screened or being uncertain regarding screening decisions (relative to deciding to get screened). Knowledge and affective risk perception partially mediated the relationship between medical maximizing orientation and positive attitudes. Knowledge, deliberative, and affective risk perceptions partially mediated the relationship between medical maximizing orientation and the screening decision. CONCLUSIONS: Interventions should prioritize targeting more amenable factors arising from medical maximizing orientation, such as inflated risk perceptions, particularly affective risk perception, and limited comprehension or acceptance of information about the benefits and risks associated with low-value care. HIGHLIGHTS: This study demonstrated that people's medical maximizing orientation can increase their positive attitudes toward the uptake of low-value screening and make them more likely to undergo it. This can happen both directly and indirectly by decreasing their understanding of the benefits and risks of screening and increasing their perception of disease risk.The study suggests that to effectively mitigate the excessive utilization of low-value care through patient-centered interventions, it is crucial to tackle 2 key issues associated with a medical maximizing mindset: inflated risk perceptions (specifically affective risk perception) and limited comprehension or acceptance of information about the benefits and risks of low-value care.This study contributes to developing a theoretical framework for interventions to improve evidence-based medical decision making by uncovering one mechanism by which medical maximizing orientation may affect the propensity to seek low-value care.
Estimating Change in Health-Related Quality of Life before and after Stroke: Challenges and Possible Solutions.
Thompson NR, Lapin BR, Katzan IL
Med Decis Making · 2024 Nov · PMID 39377510 · Publisher ↗
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 stro...
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.
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