Sensitivity Analysis Research Articles

Venture capital, innovation, and entrepreneurial investment in India: The moderating role of caste and tribal institutions

This study examines how venture capital (VC) availability drives entrepreneurial investment in India from 2000 to 2021. Using the national innovation system (NIS) framework, we use a comprehensive measure of VC availability to assess its impact on startup investments across Indian states. A novel aspect of this research is the inclusion of India's caste and tribal system, a distinctive cultural institution, as a key moderating factor. The analysis accounts for individual startup characteristics and broader economic, technological, and institutional influences at both the state and national levels. The findings demonstrate that VC availability significantly predicts entrepreneurial investment. Additionally, the caste and tribal system exerts both direct and indirect moderating effects on this relationship. Sensitivity and endogeneity analyses confirm the robustness of these results. Moreover, the caste system diminishes the positive mediating effects of financial literacy, linguistic diversity, and government initiatives on VC availability and entrepreneurial investment. These findings support the NIS theory, emphasizing the crucial role of VC availability and the impact of local cultural institutions on fostering entrepreneurship in large emerging economies.

Low-Carbon Power Technology Innovation: Addressing Environmental Protection, Land Use, and Community Rights

The rapid advancement of low-carbon technologies, such as wind and nuclear power, introduces critical ethical challenges, including conflicts between environmental protection, land use, and community rights. This study presents a comprehensive framework to address these conflicts through data-driven optimization and ethical analysis. First, a robust data collection and modeling process is established to quantify energy demand and renewable adoption trends. Multi-objective optimization using the Multi-Objective Particle Swarm Optimization (MOPSO) and Mixed-Integer Programming (MIP) methods is then applied to balance conflicting objectives. The results reveal significant improvements in energy efficiency, carbon reduction, and stakeholder satisfaction, with MOPSO demonstrating superior performance. Ethical considerations are integrated through an impact vs. satisfaction analysis, which highlights the positive correlation between ecological benefits and public acceptance. Finally, a sensitivity analysis validates the robustness of the proposed solutions under varying conditions. The findings emphasize the potential of combining advanced algorithms with ethical frameworks to design sustainable and socially equitable low-carbon energy systems.

Exploring the value of seasonal flow forecasts for drought management in South Korea

Abstract. Drought poses significant challenges across various water-dependent sectors. In the past few decades, numerous devastating droughts have been reported worldwide, including in South Korea. A recent drought in South Korea, which lasted from 2014 to 2016, led to significant consequences including water restrictions and nationwide crop failures. Historically, reservoirs have played a crucial role in mitigating hydrological droughts by increasing water supply stability. With exacerbating intensity and frequency of droughts, enhancing the operational efficiency of existing reservoirs becomes increasingly important. This study examines the value of seasonal flow forecasts (SFFs) in informing reservoir operations during three historical drought events, with a focus on two key reservoir systems in South Korea. For these events, we simulate what would have happened if the reservoir managers had optimized operations using SFFs. For comparison, we also simulate the effect of reservoir operations optimized using two deterministic scenarios (worst-case and 20-year-return-period drought) and another ensemble forecasts product (Ensemble Streamflow Prediction, ESP). We repeat our simulation experiments by varying the key choices in the experimental setup, i.e. the forecast lead time, decision-making time step, and method for selecting a compromise solution between conflicting objectives. We then propose a new, simple and intuitive method for measuring the value of the different scenarios/forecasts based on the frequency of outperforming (in a Pareto dominance sense) the historical operation across such experiments. Our findings indicate that while deterministic scenarios show higher accuracy, forecast-informed operations with ensemble forecasts tend to yield a greater value. This highlights the importance of considering forecast uncertainty in optimizing reservoir operations. Although SFFs generally show higher accuracy than ESP, the difference in value is small. Lastly, sensitivity analysis shows that the method used to select a compromise release schedule between competing operational objectives is a key control of forecast value, implying that the benefits of using seasonal forecasts may vary widely depending on how priorities between objectives are established.

A Report of 3 Cases of Adrenal Suppression Due to Glucocorticoid Hypersensitivity to Inhaled Corticosteroids

Abstract Children with moderate to severe asthma are treated with inhaled corticosteroids (ICS). Altered glucocorticoid sensitivity can cause suppression of the hypothalamic-pituitary-adrenal (HPA) axis in patients on ICS therapy. Our objective is to study glucocorticoid sensitivity in patients with asthma with suppressed HPA axes on ICS. We report 3 patients with suppressed HPA axes on ICS treatment and healthy controls. All subjects and controls underwent glucocorticoid sensitivity analysis using an in vitro fluorescein labeled–dexamethasone (F-DEX) mononuclear cell binding assay. Glucocorticoid sensitivity index (GCSI) was calculated as the area under the curve of the F-DEX assay results. Patients with GCSI ≤264 were classified as glucocorticoid resistant and those with GCSI ≥386 were classified as having increased glucocorticoid sensitivity. All 3 patients were found to have increased glucocorticoid sensitivity with GCSI of 424.9, 720.6, and 699.2 respectively. In conclusion, ICS therapy can lead to HPA axis suppression with varying degrees of adrenal insufficiency and therefore increased glucocorticoid sensitivity should be considered in such cases.

Quantifying cascading power outages during climate extremes considering renewable energy integration

Climate extremes, such as hurricanes, combined with large-scale integration of environment-sensitive renewables, could exacerbate the risk of widespread power outages. We introduce a coupled climate-energy model for cascading power outages, which comprehensively captures the impacts of climate extremes on renewable generation, and transmission and distribution networks. The model is validated with the 2022 Puerto Rico catastrophic blackout during Hurricane Fiona – a unique system-wide blackout event with complete records of weather-induced outages. The model reveals a resilience pattern that was not captured by the previous models: early failure of certain critical components enhances overall system resilience. Sensitivity analysis on various scenarios of behind-the-meter solar integration demonstrates that lower integration levels (below 45%, including the current level) exhibit minimal impact on system resilience in this event. However, surpassing this critical level without pairing it with energy storage can exacerbate the probability of catastrophic blackouts.

Estimation of Daily Photovoltaic Power One Day Ahead With Hybrid Deep Learning and Machine Learning Models

ABSTRACTIn this study, hybrid LSTM‐SVM and hybrid LSTM‐KNN models were developed to predict hourly PV power one day ahead. The performances of these hybrid models were compared with K‐nearest neighbors (KNN), long short‐term memory (LSTM), and support vector machine (SVM) models. The input data of these models were pressure, cloudiness, humidity, temperature, and solar intensity, while the output data was the daily photovoltaic (PV) power one day ahead. The performances of the models were evaluated using mean square error (MSE), root mean square error (RMSE), normalized root mean square error (NRMSE), and peak signal‐to‐noise ratio (PSNR). The prediction accuracies of hybrid LSTM‐KNN, LSTM, KNN, hybrid LSTM‐SVM, and SVM were 98.72%, 95.8%, 90.25%, 76.3%, and 48.87%, respectively. Hybrid LSTM‐KNN predicted the daily PV power of the day ahead with higher accuracy than LSTM, KNN, SVM, and hybrid LSTM‐SVM. The effect of input variables on output variables was examined with sensitivity analysis. Sensitivity analyses showed that the most important meteorological data affecting the daily PV power one day ahead was solar intensity with a rate of 95%.

Pattern Mixture Sensitivity Analyses via Multiple Imputations for Non-Ignorable Dropout in Joint Modeling of Cognition and Risk of Dementia.

Motivated by the Swedish Betula study, we consider the joint modeling of longitudinal memory assessments and the hazard of dementia. In the Betula data, the time-to-dementia onset or its absence is available for all participants, while some memory measurements are missing. In longitudinal studies of aging, one cannot rule out the possibility of dropout due to health issues resulting in missing not at random longitudinal measurements. We, therefore, propose a pattern-mixture sensitivity analysis for missing not-at-random data in the joint modeling framework. The sensitivity analysis is implemented via multiple imputation as follows: (i) multiply impute missing not at random longitudinal measurements under a set of plausible pattern-mixture imputation models that allow for acceleration of memory decline after dropout, (ii) fit the joint model to each imputed longitudinal memory and time-to-dementia dataset, and (iii) combine the results of step (ii). Our work illustrates that sensitivity analyses via multiple imputations are an accessible, pragmatic method to evaluate the consequences of missing not at-random data on inference and prediction. This flexible approach can accommodate a range of models for the longitudinal and event-time processes. In particular, the pattern-mixture modeling approach provides an accessible way to frame plausible missing not at random assumptions for different missing data patterns. Applying our approach to the Betula study shows that worse memory levels and steeper memory decline were associated with a higher risk of dementia for all considered scenarios.

Methodological Approaches for Incorporating Marginalized Populations into HPV Vaccine Modeling: A Systematic Review.

Background. Delineation of historically marginalized populations in decision models can identify strategies to improve equity but requires assumptions in both model structure and stratification of input data. Purpose. We sought to characterize alternative methodological approaches for incorporating marginalized populations into human papillomavirus (HPV) vaccine decision-support models. Data Sources. We conducted a systematic search of PubMed, CINAHL, Scopus, and Embase from January 2006 through June 2022. Study Selection. We identified simulation models of HPV vaccination that refine any model input to specifically reflect a marginalized population. Data Extraction. We extracted data on key methodological decisions across modeling approaches to incorporate marginalized populations, including stratification of inputs, model structure, attribution of prevaccine disparities, calibration, validation, and sensitivity analyses. Data Synthesis. We identified 30 models that stratified inputs by sexual behavior (i.e., men who have sex with men), HIV infection status, race, ethnicity, income, rurality, or combinations of these. We identified 5 common approaches used to incorporate marginalized groups. These included models based primarily on differences in sexual behavior (k = 6), HPV cancer incidence (k = 10), cancer screening and care access (k = 4), and HPV natural history (through either direct incorporation of data [k = 10] or calibration [k = 5]). Few models evaluated sensitivity around their conceptualization of the marginalized group, and only 5 models validated outcomes for the marginalized group. Limitations. Evaluated studies reflected a variety of settings and research questions, making it difficult to evaluate the implications of differences across modeling approaches. Conclusions. Modelers should be explicit about the assumptions and theory driving their model structure and input parameters specific to key marginalized populations, such as the causes of prevaccination differences in outcomes. More emphasis is needed on model validation and rigorous sensitivity analysis.HighlightsWe identified 30 unique HPV vaccination models that incorporated marginalized populations, including populations living with HIV, low-income or rural populations, and individuals of a marginalized race, ethnicity, or sexual behavior.Methods for incorporating these populations, as well as the assumptions inherent in the modeling structure and parameter selections, varied substantially, with models explicitly or implicitly attributing prevaccine differences to alternative combinations of biological, behavioral, and societal mechanisms.Modelers seeking to incorporate marginalized populations should be transparent about assumptions underlying model structure and data and examine these assumptions in sensitivity analysis when possible.

Economic returns on investing in early childhood development in Vietnam: a cost-benefit analysis

BackgroundEconomic evidence on the long-term benefits of investing in early childhood development is limited. This study aimed to estimate the potential long-term economic benefits of an early childhood development intervention ‘Learning Clubs’ in Vietnam.MethodsWe conducted a cost-benefit analysis to estimate the costs and benefits of the intervention compared to the standard of care from a limited societal perspective. The intervention cost and child cognitive development outcome were derived from the published ‘Learning Clubs’ trial-based cost-effectiveness analysis. Benefits were monetised based on the gains in wages associated with improved cognitive development over a lifetime at the population level, using a life-table model. The benefit-cost ratio was estimated as the benefits in wages divided by the intervention cost with a 3% discount rate, assuming nationwide scale up to a hypothetical national birth cohort. Sensitivity, scenario, and threshold analyses were conducted to examine the uncertainty around the model.ResultsThe benefit-cost ratio was 5.52, indicating that the expected benefit for each US$1 invested would be US$5.52. The intervention would generate economic benefits of US$1,566 per child over their lifetime. Upon nationwide scale-up, the total benefit would amount to US$2.28 billion per national annual birth cohort. Probabilistic sensitivity analyses estimated the benefit-cost ratio to be 5.90 (95%CI 2.66 to 11.12). The findings were relatively robust as the benefit-cost ratios remained above 1 in all sensitivity and scenario analyses.ConclusionsOur findings support greater investments in early childhood development. The Excel-based model is available for further use and adaption to other settings.

Reduced anticoagulation targets in extracorporeal life support (RATE): protocol for a pre-planned secondary Bayesian analysis of the rate trial

BackgroundThe RATE trial is a three-arm non-inferiority randomized controlled trial in adult patients treated with extracorporeal membrane oxygenation (ECMO) on the effect of anticoagulation levels on mortality, hemorrhagic, and thrombotic complications. The current protocol presents the rationale and analysis plan for evaluating the primary and secondary outcomes under the Bayesian framework.MethodsThis protocol was drafted and submitted before study completion and, thus, the primary analysis. The primary outcome of the Bayesian analysis is mortality at 6 months. The secondary outcomes are severe hemorrhagic and thrombotic complications. We will use an uninformative prior for the primary analysis. Sensitivity analyses will be performed using a skeptical prior and an evidence-based informative prior.ConclusionThe proposed secondary, pre-planned Bayesian analysis of the RATE trial will provide additional information on the effect of different anticoagulation strategies during ECMO on complication rates. This additional Bayesian analysis will likely increase the validity of our results and complement the interpretation of the primary and several secondary outcomes.Trial registrationThis trial is registered at https://clinicaltrials.gov/ (NCT04536272), registration date September 2, 2020. This trial is also registered at the Dutch trial register (NL7976).

Cost-effectiveness analysis of combination therapies involving novel agents for first/second-relapse patients with multiple myeloma: a Markov model approach with calibration techniques

BackgroundAs the number of randomized clinical trials (RCTs) demonstrating the survival benefits of combination therapies in previously treated multiple myeloma (MM) patients increases, it is essential to determine the most cost-effective treatment through robust economic evaluation. This study aims to assess the cost-effectiveness of combination therapies for first/second-relapse MM patients from the perspective of the Chinese healthcare system.MethodsA Markov model was developed to evaluate three combination therapy groups based on primary drugs (bortezomib, lenalidomide, and carfilzomib). The economic evaluation was conducted within each group individually, rather than across different groups. Clinical inputs for the model were derived from RCT reports, while healthcare costs were sourced from the Zhejiang Province bidding database and a retrospective analysis. Utility values were obtained through an on-site survey using the Chinese version of the EuroQoL Five-dimensional Five-level Questionnaire. One-way and probabilistic sensitivity analyses were performed to assess the robustness of the base-case results.ResultsIn the bortezomib group, bortezomib-dexamethasone (Vd) yielded 2.42 quality-adjusted life years (QALYs) at a cost of ¥783,775. With a willingness-to-pay (WTP) threshold of three times the 2023 per capita GDP in China (¥258,074), pomalidomide-bortezomib-dexamethasone was the most cost-effective therapy (¥86,129/QALY) in this group. In the lenalidomide group, lenalidomide-dexamethasone (Rd) resulted in 3.06 QALYs at a cost of ¥840,509. Compared to Rd, the incremental cost-effectiveness ratios (ICERs) of elotuzumab-lenalidomide-dexamethasone (¥5,095,300/QALY), ixazomib-lenalidomide-dexamethasone (¥1,605,712/QALY), carfilzomib-lenalidomide-dexamethasone (¥955,255/QALY), and daratumumab-lenalidomide-dexamethasone (¥851,933/QALY) all exceeded the WTP threshold. In the carfilzomib group, carfilzomib-dexamethasone (Kd) resulted in 3.19 QALYs at a cost of ¥1,961,624. Compared to Kd, the ICERs of daratumumab-carfilzomib-dexamethasone (¥2,250,821/QALY) and isatuximab-carfilzomib-dexamethasone (¥4,977,964/QALY) also exceeded the WTP. Sensitivity analyses confirmed the robustness of the base-case results.ConclusionsAlthough this study did not fully account for the heterogeneity of subsequent treatment regimens among first/second-relapse MM patients, it highlights that the substantial financial burden associated with combination therapies involving novel agents poses a significant challenge in justifying their economic value.

Highway non-recurrent congestion prediction using a multi-step spatio-temporal deep learning approach

ABSTRACT Accurate forecasting of highway Non-Recurrent Congestion (NRC) is critical for modern transportation systems. However, this research remains in its early stages and is frequently constrained to single-step temporal prediction. To address this limitation, this research presents a novel approach that leverages the Dual-Stream Autoencoder Sequence-to-Sequence model (DS-AE-Seq2Seq) to create an accurate tool for quantitative, spatio-temporal, and multi-step prediction of highway NRC. The proposed model innovatively integrates an Autoencoder and a Seq2Seq encoder to process static and time-series data, respectively. The decoder generates spatio-temporal predictions using the outcomes of both streams. The model is tested with data collected from highway I-5 and I-405, USA. Results show that it not only outperforms benchmarks but also exhibits high prediction accuracy under extreme traffic conditions, including severe injury incidents and various levels of service. Additionally, the model demonstrated reliability, through a sensitivity analysis, across different distances and prediction horizons.

Bayesian Calibration of Stochastic Agent Based Model via Random Forest.

Agent-based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology by explicitly accounting for diverse individual interactions and environments. However, these models are usually stochastic and highly parametrized, requiring precise calibration for predictive performance. When considering realistic numbers of agents and properly accounting for stochasticity, this high-dimensional calibration can be computationally prohibitive. This paper presents a random forest-based surrogate modeling technique to accelerate the evaluation of ABMs and demonstrates its use to calibrate an epidemiological ABM named CityCOVID via Markov chain Monte Carlo (MCMC). The technique is first outlined in the context of CityCOVID's quantities of interest, namely hospitalizations and deaths, by exploring dimensionality reduction via temporal decomposition with principal component analysis (PCA) and via sensitivity analysis. The calibration problem is then presented, and samples are generated to best match COVID-19 hospitalization and death numbers in Chicago from March to June in 2020. These results are compared with previous approximate Bayesian calibration (IMABC) results, and their predictive performance is analyzed, showing improved performance with a reduction in computation.

A systematic review and meta-analysis of the association of human papilloma virus infections with ocular surface squamous neoplasia.

A systematic review and meta-analysis of the association of human papilloma virus infections with ocular surface squamous neoplasia.

Assessing the impact of Lithuania’s 2018 alcohol marketing ban on adolescent alcohol use by comparing trends with five EU control countries: a study protocol for a secondary data analysis

IntroductionAlcohol consumption poses a significant health risk, contributing to 10% of deaths in the WHO European Region. To combat this, the WHO recommends the implementation of its ‘best buy’ policies—three...

DNA Methylation-Regulated ZDHHC13 Promotes the Progression of Parkinson's Disease.

Recent studies suggest that palmitoylation may influence proteins involved in neurotransmission and neurodegenerative changes, such as pathological α-synuclein. However, the role of palmitoylation in Parkinson's disease (PD) has not been systematically investigated. Additionally, findings on DNA methylation changes and PD-associated gene expression remain inconsistent. This study aims to explore the causal relationship and mechanisms between palmitoylation genes, DNA methylation, and PD using Mendelian Randomization (MR). This study employed two-sample MR analysis, summary-data-based Mendelian Randomization (SMR) analysis, and mediation analysis, utilizing PD GWAS data from the Finngen database, palmitoylation genes data from the eQTLGen database, and DNA methylation data from the GoDMC database. First, a two-sample MR analysis was used to evaluate the causal relationship between the palmitoylation genes and PD. Then, the SMR method was applied to validate the association between gene expression and PD. Finally, mediation analysis was conducted to explore the mediating effect of DNA methylation on the relationship between expression of palmitoylation genes and PD. Our study found a significant positive correlation between high expression of the ZDHHC13 gene and increased PD risk. Each 1 standard deviation increase in ZDHHC13 expression was associated with a 24.20% increase in PD risk. Further DNA methylation analysis identified two key methylation sites (cg00161556 and cg27379915), which indirectly influenced the occurrence of PD by regulating the expression of the ZDHHC13 gene. Mediation effect analysis revealed that the methylation sites cg00161556 and cg27379915 indirectly promoted the development of PD by regulating the expression of the ZDHHC13 gene, with the mediation effect of ZDHHC13 gene expression accounting for 63.72% and 57.61% of the total effect, respectively. Sensitivity analysis and SMR analysis supported our findings, indicating high statistical robustness. This study highlights the role of DNA methylation in regulating ZDHHC13 during PD progression, suggesting potential clinical applications such as ZDHHC13 as a biomarker or therapeutic target for early PD diagnosis and treatment.

A stochastic model for aircraft line maintenance scheduling considering random delays

ABSTRACT Planning line maintenance schedules is a complex issue. It requires not only synchronization with the airline’s flight schedule but also preparedness for unforeseen events during maintenance, such as random delays. Traditionally, planning line maintenance schedules has been done manually. This method is not only time-consuming but may also lead to overly frequent maintenance. Hence, the purpose of this study is to establish a monthly schedule for routine line checks. We utilize a scenario optimization approach to build a robust scheduling model, accounting for random maintenance delays and related constraints. The model aims to minimize overall costs and is solved using the mathematical programming software CPLEX. A case study, based on real flight schedules and maintenance data from an airline, tests the model’s practicality. We also conducted schedule evaluation, sensitivity, and scenario analyses. The results indicate that the stochastic model can indeed reduce maintenance costs and planning time. The findings of this study can also serve as a valuable reference for aircraft maintenance planners in determining their aircraft line maintenance schedules.

Association between percent body fat and the risk of prediabetes among Chinese adults: a 5-years longitudinal cohort study

This study aimed to explore the influence of percent body fat (PBF) on the risk of developing prediabetes among Chinese individuals, given the limited evidence on this relationship. We conducted a retrospective cohort study involving 185,586 Chinese adults. We applied Cox proportional hazards regression models, cubic spline functions, and smooth curve fitting to analyze the relationship between initial PBF and the likelihood of prediabetes, focusing on its nonlinear connection. We conducted various sensitivity and subgroup analyses to strengthen our results. After adjusting for covariates, we found a positive correlation between PBF and the risk of prediabetes (HR = 1.13, 95% CI: 1.12–1.15, p < 0.0001). Moreover, a nonlinear correlation was identified between PBF and the likelihood of prediabetes, with a turning point at 29.5. On the left side of the turning point, the hazard ratio was 1.01 (95% CI: 0.99–1.03, p = 0.4128), while on the right side, it was 1.52 (95% CI: 1.45–1.59, p < 0.0001). Furthermore, sensitivity and subgroup analyses reaffirmed the robustness of these findings. Our research identified a nonlinear relationship between PBF and the development of prediabetes in the Chinese population, marked by a turning point at 29.5. Lowering PBF below 29.5 may reduce the risk of developing prediabetes.

A pan-cancer analysis reveals the oncogenic and immunological role of insulin-like growth factor 2 mRNA-binding protein family members

PurposeTo investigate the expression and clinical significance of insulin-like growth factor 2 mRNA-binding protein family members (IGF2BPs) in pan-cancer and evaluate their potential as targets for tumor immunotherapy.MethodsBased on data from the cancer genome atlas (TCGA) database, pan-cancer analysis was conducted to examine the clinical significance of IGF2BPs expression in twenty-two tumors.ResultsDifferential expression analysis showed high expression of IGF2BPs in most tumor tissues. Survival and mutation analyses suggested that the overexpression of IGF2BPs was associated with poor prognosis and mutation status of certain tumors. Methylation analysis revealed the methylation levels of IGF2BP1/2/3 in certain tumors were intricately linked to their mRNA expression, patient prognosis, and immune cell infiltration. Enrichment analysis indicated that abnormal expression of IGF2BPs was associated with various common tumor-related pathways in different tumors, including AMPK, Hippo, PI3K-Akt, EMT, and p53. In addition, immune correlation analysis revealed that IGF2BPs were closely related to immunotherapy-related indicators (immune cell infiltration, major histocompatibility complex (MHC), immune checkpoints, tumor mutation burden (TMB), and microsatellite instability (MSI)) in some tumors. Drug sensitivity analysis indicated that IGF2BPs were sensitive to some common chemotherapeutic drugs (alvocidib, dasatinib, trametinib, and selumetinib).ConclusionIGF2BPs exhibit significantly high expression in most tumors and are associated with prognosis, pathological stage, mutational status, methylation levels, and the relevant indicators of immunotherapy sensitivity in multiple tumors. Moreover, IGF2BPs may play an oncogenic role by activating common signaling pathways. Therefore, IGF2BPs may be potential prognostic markers for tumor therapy and targets for immunotherapy and drug therapy.

Indirect treatment comparison of oral sebetralstat and intravenous recombinant human C1 esterase inhibitor for on-demand treatment of hereditary angioedema attacks

BackgroundThe goal of on-demand treatment for hereditary angioedema attacks is to halt attack progression to minimize morbidity and mortality. Four on-demand treatments have been approved thus far (ecallantide, icatibant, recombinant human C1 esterase inhibitor [rhC1INH], and plasma-derived C1INH). Results from the sebetralstat phase 3 KONFIDENT trial (NCT05259917) have been reported. To put these results into context without head-to-head trials, an indirect treatment comparison (ITC) was conducted to facilitate comparisons of efficacy and safety across treatment options.MethodsBased on a systematic literature review and feasibility assessment, only the pivotal trial for intravenous rhC1INH (NCT01188564) reported necessary data for a comparable primary efficacy endpoint (time to beginning of symptom relief) to enable an ITC with oral sebetralstat. Bayesian fixed-effects network meta-analyses models were conducted to indirectly compare the efficacy and safety outcomes of sebetralstat and rhC1INH (NCT01188564, NCT00225147, NCT00262301). A matching-adjusted indirect comparison (MAIC) of efficacy was performed, adjusting for baseline attack severity and demographic characteristics.ResultsThe fixed-effects model found no significant differences in time to beginning of symptom relief between sebetralstat 300 mg and rhC1INH 50 IU/kg (hazard ratio [95% credible interval], 0.96 [0.42–2.15] to 1.19 [0.58–2.45]). After adjusting for baseline attack severity, the MAIC showed numerically favorable results with sebetralstat compared with rhC1INH, regardless of whether baseline demographics were matched. The fixed-effects model found no significant differences in treatment-related treatment-emergent adverse events. All sensitivity analyses returned consistent results.ConclusionsThis ITC found no significant differences in time to beginning of symptom relief and overall treatment-related treatment-emergent adverse events between sebetralstat and rhC1INH.