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BACKGROUND: Impulse control disorders (ICDs) in Parkinson's disease are associated with a heavy burden on patients and caretakers. While recovery can occur, ICDs persist in many patients despite optimal management. The basis for this interindividual variability in recovery is unclear and poses a major challenge to personalized health care. METHODS: We adopted a computational psychiatry approach and leveraged the longitudinal, prospective Personalized Parkinson Project (136 people with Parkinson's disease, within 5 years of diagnosis) to combine dopaminergic learning theory-informed functional magnetic resonance imaging with machine learning (at baseline) to predict ICD symptom recovery after 2 years of follow-up. We focused on change in Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease Rating Scale scores in the entire sample regardless of an ICD diagnosis. RESULTS: Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum and medial prefrontal cortex, and the behavioral accuracy score measured while on medication were associated with greater recovery from impulse control symptoms 2 years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use. CONCLUSIONS: Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. We showed that reinforcement learning modeling parameters predicted recovery from ICD symptoms in Parkinson's disease.

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OBJECTIVES: Value-based trials aim to maximize the expected net benefit by balancing technology adoption decisions and clinical trial costs. Adaptive trials offer additional efficiency. This article provides guidance on determining whether a value-based sequential design is the best option for an adaptive 2-arm trial, illustrated through a case study. METHODS: We outlined 4 steps for the value-based sequential approach. The case study re-evaluates the Big CACTUS trial design using pilot trial data and a model-based health economic analysis. Expected net benefit is computed for (1) original fixed design, (2) value-based design with fixed sample size, and (3) optimal value-based sequential design with adaptive stopping. We compare pretrial modeling with the actual Big CACTUS trial results. RESULTS: Over 10 years, the adoption decision would affect approximately 215 378 patients. Pretrial modeling shows that the expected net benefit minus costs are (1) £102 million for the original fixed design, (2) £107 million (+5.3% higher) for the value-based design with optimal fixed sample size, and (3) £109 million (+6.7% higher) for the optimal value-based sequential design with maximum sample size of 435 per arm. Post hoc analysis using actual Big CACTUS trial data indicates that the value-adaptive trial with a maximum sample size of 95 participant pairs would not have stopped early. Bootstrap simulations reveal a 9.76% probability of early completion with n = 95 pairs compared with 31.50% with n = 435 pairs. CONCLUSIONS: The 4-step approach to value-based sequential 2-arm design with adaptive stopping was successfully implemented. Further application of value-based adaptive approaches could be useful to assess the efficiency of alternative study designs.

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BACKGROUND: The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive, but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models. METHODS: Our method provides accurate EVSI estimates by approximating the conditional expectation of the benefit based on 2 steps. First, a Taylor series approximation is applied to estimate the conditional expectation of the benefit as a function of the conditional moments of the parameters of interest using a spline, which is fitted to the samples of the parameters and the corresponding benefits. Next, the conditional moments of parameters are approximated by the conventional GA and Fisher information. The proposed approach is applied to several data collection exercises involving non-Gaussian parameters and nonlinear decision models. Its performance is compared with the nested Monte Carlo method, the conventional GA approach, and the nonparametric regression-based method for EVSI calculation. RESULTS: The proposed approach provides accurate EVSI estimates across different sample sizes when the parameters of interest are non-Gaussian and the decision models are nonlinear. The computational cost of the proposed method is similar to that of other novel methods. CONCLUSIONS: The proposed approach can estimate EVSI across sample sizes accurately and efficiently, which may support researchers in determining an economically optimal study design using EVSI. HIGHLIGHTS: The Gaussian approximation method efficiently estimates the expected value of sample information (EVSI) for clinical trials with varying sample sizes, but it may introduce bias when health economic models have a nonlinear structure.We introduce the spline-based Taylor series approximation method and combine it with the original Gaussian approximation to correct the nonlinearity-induced bias in EVSI estimation.Our approach can provide more precise EVSI estimates for complex decision models without sacrificing computational efficiency, which can enhance the resource allocation strategies from the cost-effective perspective.

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BACKGROUND: Individual differences in commitment to lifelong learning, a process aimed at seizing opportunities for self-development, have not been extensively studied. OBJECTIVE: Our aim is to provide a comprehensive understanding of the decision-making mechanisms involved in pursuing learning for self-development. METHOD: We conducted a literature review on the taxing nature of cognitive exertion and its impact on the inclination to engage in cognitively demanding tasks for learning, as well as individual differences in sensitivity to aversive or rewarding outcomes inherent in the learning process. RESULTS: Our findings indicate that the Expected Value of Control (EVC) theory can elucidate the former, while research on approach-avoidance motivation can shed light on the latter. CONCLUSION: We propose and develop an integrated framework that incorporates both lines of research. This framework holds relevance for neuropsychology, experimental psychology, and education psychology, offering theoretical guidance for tailoring learning experiences to enhance engagement and commitment to self-development.

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Numerous studies have established a correlation between social anxiety and poor cognitive control. However, little is known about the cognitive control pattern of individuals with high social anxiety (HSAs) and the underlying mechanisms. Based on the Dual Mechanisms of Control framework and the Expected Value of Control theory, this study explored whether HSAs have an impaired cognitive control pattern (Experiment 1) and whether motivational deficiencies underlie the impaired control pattern (Experiment 2). In Experiment 1, 21 individuals with low social anxiety (LSAs) and 21 HSAs completed an AX-Continuous Performance Task. Results showed that HSAs had a smaller P3b amplitude than LSAs, indicating their weakened proactive control in the cue processing stage, but a larger contingent negative variation (CNV) on cue B as compensation for the negative effects of anxiety in the response preparation stage. No group difference was found in N2 and P3a amplitude on probes, suggesting that reactive control in HSAs was not affected compared to LSAs. In Experiment 2, 21 LSAs and 21 HSAs completed a cued-flanker task, where the likelihood of proactive control engagement was manipulated. The results revealed that HSAs exhibited motivation deficiencies in engaging in proactive control, as evidenced by P3b, CNV amplitude, and response times. These findings shed light on the impaired cognitive control pattern of HSAs and suggest that motivational deficiencies may be the crucial underlying factor.

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At present, the commonly used clinical protocols of oral cosmetic restoration are mostly based on the oral aesthetic indexes proposed by Western developed countries (referred to as Western aesthetics), which are different from the oral aesthetic indexes unique to Chinese people (referred to as Chinese aesthetics). In the design of restoration schemes and the evaluation of restoration effects, these differences have a large or small effect on the doctor-patient-technology triad. Improper handling could directly weaken the cooperation efficiency of the three parties, reduce patient satisfaction, and even lead to medical disputes in serious cases. From doing a good job of oral cosmetic restoration in China, the connotative characteristics of Chinese oral aesthetics are introduced in this paper, and the value of oral aesthetic analysis in diagnosis and treatment is discussed. The process and method of aesthetic analysis and assessment through the modified Chinese psychosocial impact of dental aesthetics questionnaire and the evaluation ruler of the expected value of oral cosmetic restoration are further introduced in detail.

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BACKGROUND: Early adverse experiences are assumed to affect fundamental processes of reward learning and decision making. However, computational neuroimaging studies investigating these circuits in the context of adversity are sparse and limited to studies conducted in adolescent samples, leaving the long-term effects unexplored. METHODS: Using data from a longitudinal birth cohort study (n = 156; 87 female), we investigated associations between adversities and computational markers of reward learning (i.e., expected value, prediction errors). At age 33 years, all participants completed a functional magnetic resonance imaging-based passive avoidance task. Psychopathology measures were collected at the time of functional magnetic resonance imaging investigation and during the COVID-19 pandemic. We applied a principal component analysis to capture common variations across 7 adversity measures. The resulting adversity factors (factor 1: postnatal psychosocial adversities and prenatal maternal smoking; factor 2: prenatal maternal stress and obstetric adversity; factor 3: lower maternal stimulation) were linked with psychopathology and neural responses in the core reward network using multiple regression analysis. RESULTS: We found that the adversity dimension primarily informed by lower maternal stimulation was linked to lower expected value representation in the right putamen, right nucleus accumbens, and anterior cingulate cortex. Expected value encoding in the right nucleus accumbens further mediated the relationship between this adversity dimension and psychopathology and predicted higher withdrawn symptoms during the COVID-19 pandemic. CONCLUSIONS: Our results suggested that early adverse experiences in caregiver context might have a long-term disruptive effect on reward learning in reward-related brain regions, which can be associated with suboptimal decision making and thereby may increase the vulnerability of developing psychopathology.

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In recent years, deep learning methods have been widely used in combination with control charts to improve the monitoring efficiency of complete data. However, due to time and cost constraints, data obtained from reliability life tests are often type-I right censored. Traditional control charts become inefficient for monitoring this type of data. Thus, researchers have proposed various control charts with conditional expected values (CEV) or conditional median (CM) to improve efficiency for right-censored data under normal and non-normal conditions. This study combines the exponentially weighted moving average (EWMA) CEV and CM chart with deep learning methods to increase efficiency for gamma type-I right-censored data. A statistical simulation and a real-world case are presented to assess the proposed method, which outperforms the traditional EWMA charts with CEV and CM in various skewness coefficient values and censoring rates for gamma type-I right-censored data.

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Risky choice is associated with maladaptive behaviors, particularly substance use disorders. Current animal models of risky choice are often confounded by other constructs like behavioral flexibility and suboptimal choice. The purpose of the current experiment was to determine if the psychostimulant methamphetamine, a drug whose popularity has increased in recent years, increases risky choice in an equivalent expected value (EEV) task. In the EEV task, rats are given a choice between two reinforcer alternatives that differ in magnitude and probability of delivery, but have equivalent expected value. Forty-eight Sprague Dawley rats were tested in three versions of the EEV task. In the first version of the EEV task, both reinforcer magnitude and probability were adjusted across blocks of trials for both alternatives. In the second and the third versions of the EEV task, reinforcer magnitude was held constant across each block of trials (either 1 vs. 2 pellets or 4 vs. 5 pellets). We found that male rats preferred the "riskier" option, except when reinforcer magnitudes were held constant at 4 and 5 pellets across each block of trials. Methamphetamine (0.5 mg/kg) increased preference for the risky option in both males and females, but only when both reinforcer magnitude and probability were manipulated across blocks of trials for each alternative. The current results demonstrate that both magnitude of reinforcement and probability of reinforcement interact to influence risky choice. Overall, this study provides additional support for using reinforcers with expected value to measure risky choice.

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A Value of Information (VOI) analysis can play a key role in decision-making for adopting new approach methodologies (NAMs). We applied EPA's recently developed VOI framework to the Threshold of Toxicological Concern (TTC). Obtaining/deriving a TTC value for use as a toxicity reference value (TRV) for substances with limited toxicity data was shown to provide equivalent or greater health protection, immense return on investment (ROI), greater net benefit, and substantially lower costs of delay (CoD) compared with TRVs derived from either traditional human health assessment (THHA) chronic toxicity testing in lab animals or the 5-day in vivo EPA Transcriptomic Assessment Product (ETAP). For all nine exposure scenarios examined, the TTC was more economical terms of CoD and ROI than the ETAP or the THHA; expected net benefit was similar for the TTC and ETAP with both of these more economical than the THHA The TTC ROI was immensely greater (5,000,000-fold on average) than the ROI for THHA and the ETAP ROI (100,000-fold on average). These results support the use of the TTC for substances within its domain of applicability to waive requiring certain in vivo tests, or at a minimum, as an initial screening step before conducting either the ETAP or THHA in vivo studies.

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Objective:To explore the brain activation intensity changes of depressed patients in the phase of expected value (EV), positive prediction error (+ PE) and negative prediction error (-PE) under uncertain (risky, ambiguous) decision-making.Methods:From July 2018 to February 2021, a total of 48 depressed patients in the Affiliated Hospital of Qingdao University were collected (depression group), and 69 sex-, age-, and educational level-matched healthy people were recruited as the control group. All participants completed risky and ambiguous decision-making tasks under the E-Prime system.SA-9800 brain functional audio-visual stimulation system and GE3.0 T functional magnetic resonance imaging (fMRI) scanners were used to conduct synchronous scanning and data acquisition. Using Xjview software to analyze the activation intensity of related brain areas to compare the activity intensity of the two groups.SPSS 16.0 software was used for chi square test, independent sample t-test. Results:Under risky decision-making, compared with the control group, the brain areas with reduced activation during EV phase in depression group were bilateral prefrontal cortex (PFC)(MNI coordinate: left x=-45, y=21, z=-6; right x=0, y=69, z=-3), left para hippocampal gyrus(PHG)(MNI coordinate: x=-9, y=0, z=-22), bilateral occipital lobe(OL)(MNI coordinate: left x=-51, y=-81, z=-3; right x=48, y=-84, z=-9)( P<0.05). The brain areas with reduced activation during + PE phase were bilateral PFC, left hippocampus (HIP), bilateral temporal lobe (TL), left middle occipital gyrus( P<0.05). The brain areas with reduced activation were bilateral PFC, right putamen, bilateral TL( P<0.05) during -PE phase. Under ambiguous decision-making, compared with the control group, the brain areas with reduced activation during EV phase in depression group were bilateral PFC, right OL( P<0.05); the brain areas with reduced activation during + PE phase were bilateral PFC, right putamen and hippocampus, bilateral TL, bilateral OL( P<0.05); and the brain areas with reduced activation were bilateral PFC, bilateral TL( P<0.05) during -PE phase. Conclusion:The study shows that the activities of reward brain areas such as PFC, limbic system and OL system are reduced during EV and PE phase under uncertain decision-making in depressed patients.

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First-degree relatives of patients with bipolar disorder are at heightened risk of mood episodes, which may be attributed to the existence of endophenotypes i.e., heritable (neuro)biological changes present in patients and their unaffected relatives (UR). In this longitudinal MRI study, we aim to investigate the trajectories of aberrant reward-related functional changes identified in UR vs healthy controls (HC). Sixty-eight UR and 65 HC of similar age and gender distribution underwent MRI at baseline while performing a card guessing task. Of these, 29 UR and 36 HC were investigated with the same protocol following a 16-month period in average. We first identified brain regions showing group differences in the neural response to expected value (EV) and reward prediction error (PE) at baseline and analyzed how the reward-related response in these regions changed over time in UR vs HC. Relative to HC at baseline, UR showed lower EV signal in the right ventrolateral prefrontal cortex (vlPFC) and paracingulate gyrus and lower PE signal in the left vlPFC and dorsomedial PFC. The trajectories of these abnormalities in UR showed a normalization of the prefrontal EV signals, whereas the PE signals which correlated with depressive symptoms remained stable over time. While the UR showed both blunted EV and PE signals, none of these abnormalities increased over time, which is consistent with the observed stable mood symptoms.

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BACKGROUND: Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement learning, the process by which feedback from the environment is used to adjust behavior. AIMS: It is poorly understood whether impairments in flexibility differ between individuals with cocaine use and gambling disorders, and how this is instantiated by the brain. We applied computational modelling methods to gain a deeper mechanistic explanation of the latent processes underlying cognitive flexibility across two disorders of compulsivity. METHOD: We present a re-analysis of probabilistic reversal data from individuals with either gambling disorder (n = 18) or cocaine use disorder (n = 20) and control participants (n = 18), using a hierarchical Bayesian approach. Furthermore, we relate behavioural findings to their underlying neural substrates through an analysis of task-based functional magnetic resonanceimaging (fMRI) data. RESULTS: We observed lower 'stimulus stickiness' in gambling disorder, and report differences in tracking expected values in individuals with gambling disorder compared to controls, with greater activity during reward expected value tracking in the cingulate gyrus and amygdala. In cocaine use disorder, we observed lower responses to positive punishment prediction errors and greater activity following negative punishment prediction errors in the superior frontal gyrus compared to controls. CONCLUSIONS: Using a computational approach, we show that individuals with gambling disorder and cocaine use disorder differed in their perseverative tendencies and in how they tracked value neurally, which has implications for psychiatric classification.

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Self-directed exploration in childhood appears driven by a desire to resolve uncertainties in order to learn more about the world. However, in adult decision-making, the choice to explore new information rather than exploit what is already known takes many factors beyond uncertainty (such as expected utilities and costs) into account. The evidence for whether young children are sensitive to complex, contextual factors in making exploration decisions is limited and mixed. Here, we investigate whether modifying uncertain options influences explore-exploit behavior in preschool-aged children (48-68 months). Over the course of three experiments, we manipulate uncertain options' ambiguity, expected value, and potential to improve epistemic state for future exploration in a novel forced-choice design. We find evidence that young children are influenced by each of these factors, suggesting that early, self-directed exploration involves sophisticated, context-sensitive decision-making under uncertainty.

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Dopaminergic medication is well established to boost reward- versus punishment-based learning in Parkinson's disease. However, there is tremendous variability in dopaminergic medication effects across different individuals, with some patients exhibiting much greater cognitive sensitivity to medication than others. We aimed to unravel the mechanisms underlying this individual variability in a large heterogeneous sample of early-stage patients with Parkinson's disease as a function of comorbid neuropsychiatric symptomatology, in particular impulse control disorders and depression. One hundred and ninety-nine patients with Parkinson's disease (138 ON medication and 61 OFF medication) and 59 healthy controls were scanned with functional MRI while they performed an established probabilistic instrumental learning task. Reinforcement learning model-based analyses revealed medication group differences in learning from gains versus losses, but only in patients with impulse control disorders. Furthermore, expected-value related brain signalling in the ventromedial prefrontal cortex was increased in patients with impulse control disorders ON medication compared with those OFF medication, while striatal reward prediction error signalling remained unaltered. These data substantiate the hypothesis that dopamine's effects on reinforcement learning in Parkinson's disease vary with individual differences in comorbid impulse control disorder and suggest they reflect deficient computation of value in medial frontal cortex, rather than deficient reward prediction error signalling in striatum. See Michael Browning (https://doi.org/10.1093/brain/awad248) for a scientific commentary on this article.

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BACKGROUND: Thumb carpometacarpal (CMC) joint arthritis is one of the most prevalent arthritic conditions commonly treated with trapeziectomy alone or trapeziectomy with ligament reconstruction and tendon interposition (LRTI). We evaluate the cost-effectiveness and value of perfect and sample information of trapeziectomy alone, LRTI, and non-operative treatment. METHODS: A societal perspective decision tree was modeled. To understand the value of future research in comparing quality-of-life after trapeziectomy, LRTI, and non-operative management we characterized uncertainty by fitting distributions to EQ-5D utility data published from the United Kingdom hand surgery registry. We used Monte Carlo simulation for the probabilistic sensitivity analysis and to evaluate the value of perfect and sample information. RESULTS: Both trapeziectomy alone and LRTI were cost-effective compared to non-operative management ($2,540 and $3,511/QALY respectively). Trapeziectomy alone (base case total cost $8,251, QALY 14.08) was dominant compared to LRTI (base case total cost $8,798, QALY 13.34). However, probabilistic sensitivity analysis suggested there is a 12.5% chance LRTI may be preferred at a willingness-to-pay of $50,000/QALY. Sensitivity analysis revealed postoperative utilities are the most influential factors in determining cost-effectiveness. The value of perfect information was approximately $1,503/person. A study evaluating the quality-of-life of 1,000 patients in each arm undergoing trapeziectomy alone or LRTI could provide an expected $1,117 of information value. With approximately 40,000 CMC arthroplasties performed each year in the U.S., the annual value is close to $45 million. CONCLUSIONS: Trapeziectomy without LRTI appears to be the most cost-effective procedure in treating late-stage CMC arthritis and should be considered as first-line surgical treatment. There is substantial societal value in conducting additional research to better understand the relative quality-of-life improvements gained from these two common hand surgeries.

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OBJECTIVE: The goal of the current study was to determine the extent to which atypical neural responsiveness during retaliation is associated with observed aggression in youth in residential care. METHOD: This functional magnetic resonance imaging study involved 83 adolescents (56 male and 27 female; mean age, 16.18 years) in residential care performing a retaliation task. Of the 83 adolescents, 42 displayed aggressive behavior within the first 3 months of residential care, whereas 41 did not. During the retaliation task, participants were offered either fair or unfair divisions of $20 pots (allocation phase) and could either accept the offer or reject it, and, by spending $1, $2, or $3, punish the partner (retaliation phase). RESULTS: The study's main findings were that aggressive adolescent showed the following: reduced down-regulation of activity within regions involved in representing the expected value of choice options (left ventromedial prefrontal cortex and left posterior cingulate cortex) as a function of offer unfairness and retaliation level; and reduced recruitment of regions implicated in response control (right inferior frontal gyrus and bilateral anterior insular cortex) and associated fronto-parietal regions as a function of retaliation level. The aggressive adolescents were also significantly more likely to have been aggressive prior to residential care and showed a strong trend for increased retaliation on the task. CONCLUSION: We suggest that individuals with a greater propensity for aggression show reduced representation of the negative consequences of retaliation and associated reduced recruitment of regions potentially involved in over-ruling these negative consequences to engage in retaliation. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure sex balance in the selection of non-human subjects. We actively worked to promote sex and gender balance in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group.

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BACKGROUND: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty by collecting additional data. EVSI calculations require simulating plausible data sets, typically achieved by evaluating quantile functions at random uniform numbers using standard inverse transform sampling (ITS). This is straightforward when closed-form expressions for the quantile function are available, such as for standard parametric survival models, but these are often unavailable when assuming treatment effect waning and for flexible survival models. In these circumstances, the standard ITS method could be implemented by numerically evaluating the quantile functions at each iteration in a probabilistic analysis, but this greatly increases the computational burden. Thus, our study aims to develop general-purpose methods that standardize and reduce the computational burden of the EVSI data-simulation step for survival data. METHODS: We developed a discrete sampling method and an interpolated ITS method for simulating survival data from a probabilistic sample of survival probabilities over discrete time units. We compared the general-purpose and standard ITS methods using an illustrative partitioned survival model with and without adjustment for treatment effect waning. RESULTS: The discrete sampling and interpolated ITS methods agree closely with the standard ITS method, with the added benefit of a greatly reduced computational cost in the scenario with adjustment for treatment effect waning. CONCLUSIONS: We present general-purpose methods for simulating survival data from a probabilistic sample of survival probabilities that greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can easily be automated from standard probabilistic decision analyses. HIGHLIGHTS: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty through a given data collection exercise, such as a randomized clinical trial. In this article, we address the problem of computing EVSI when we assume treatment effect waning or use flexible survival models, by developing general-purpose methods that standardize and reduce the computational burden of the EVSI data-generation step for survival data.We developed 2 methods for simulating survival data from a probabilistic sample of survival probabilities over discrete time units, a discrete sampling method and an interpolated inverse transform sampling method, which can be combined with a recently proposed nonparametric EVSI method to accurately estimate EVSI for collecting survival data.Our general-purpose data-simulation methods greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can therefore easily be automated from standard probabilistic decision analyses.

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BACKGROUND: Respiratory syncytial virus (RSV) imposes a substantial burden on pediatric hospital capacity in Europe. Promising prophylactic interventions against RSV including monoclonal antibodies (mAb) and maternal immunizations (MI) are close to licensure. Therefore, we aimed to evaluate the cost-effectiveness of potential mAb and MI interventions against RSV in infants, for six European countries. METHODS: We used a static cohort model to compare costs and health effects of four intervention programs to no program and to each other: year-round MI, year-round mAb, seasonal mAb (October to April), and seasonal mAb plus a catch-up program in October. Input parameters were obtained from national registries and literature. Influential input parameters were identified with the expected value of partial perfect information and extensive scenario analyses (including the impact of interventions on wheezing and asthma). RESULTS: From the health care payer perspective, and at a price of €50 per dose (mAb and MI), seasonal mAb plus catch-up was cost-saving in Scotland, and cost-effective for willingness-to-pay (WTP) values ≥€20,000 (England, Finland) or €30,000 (Denmark) per quality adjusted life-year (QALY) gained for all scenarios considered, except when using ICD-10 based hospitalization data. For the Netherlands, seasonal mAb was preferred (WTP value: €30,000-€90,000) for most scenarios. For Veneto region (Italy), either seasonal mAb with or without catch-up or MI was preferred, depending on the scenario and WTP value. From a full societal perspective (including leisure time lost), the seasonal mAb plus catch-up program was cost-saving for all countries except the Netherlands. CONCLUSION: The choice between a MI or mAb program depends on the level and duration of protection, price, availability, and feasibility of such programs, which should be based on the latest available evidence. Future research should focus on measuring accurately age-specific RSV-attributable hospitalizations in very young children.

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