dataesr/scientific-paragraphs-categorization huggingface dataset:
Exploratory Evaluation of Extrapolation Methods Seven CE opinions (52-58) had the required ≥18 months of extended follow-up to be included in Part 2 of the study. The time horizon for extrapolation was 10 years in all cases with the exception of the study of pembrolizumab in urothelial carcinoma (5 years). Four reports concerned studies of nivolumab, and the remaining three pembrolizumab. Overall, extrapolated OS was underestimated in 71 percent of the assessment reports. Only one projection overestimated the long-term OS (pembrolizumab in urothelial carcinoma; KeyNote 045). Overall, the relative difference in OS between the extrapolated and observed values was at least of 10 percent. No single model consistently provided the best fit to the observed data in the extended follow-up RCT, although the generalized gamma model performed the best in three of the seven (43 percent) data sets evaluated (Table  2 ).
Discussion This is the first study assessing the accuracy of predicted OS in French CE assessment reports for ICIs. We found that predicted OS was underestimated in 73 percent of these assessment reports, with a mean difference of 13 percent. This mismatch between predicted and observed OS was even more pronounced when the extended follow-up duration was ≥3 years. In most assessment reports, the long-term benefit of ICIs does not seem to be adequately captured by the models used to extrapolate data from RCTs with limited follow-up available at the time of submission. The clinical development of ICIs has resulted in an increased interest in the use of models with a greater degree of flexibility than standard parametric models. These models include piecewise, spline-based models, mixture models, cure models, and landmark models  (12).
The objective of these models is to reflect appropriately the complex hazard function of ICIs due to potential delayed responses to treatment and the existence of long-term survivors  (18). In the selected CE assessment reports, only standard parametric, piecewise, and spline-based models were tested. Only one CE assessment report (see study  (10) ) used external data to directly inform the extrapolation model. This raises questions on the reasons why other modeling approaches (59) were not considered. One possible explanation is that the acceptability of these new approaches for HTAs undertaken by the French health authorities is currently unknown, as their recommendations on extrapolation of survival data are limited  (14). The National Institute for Health and Care Excellence (NICE) technical support document on flexible methods for survival analysis may provide the impetus to address this need  (18).
From our perspective, relative differences are more appropriate, as they take into account the differences in survival prognosis between different cancer types, and in the amount of additional long-term follow-up data available as opposed to absolute differences. Another study evaluated the CE assessment reports of ICIs by the Norwegian Medical Agency (n = 7) and the Dental and Pharmaceutical Benefits Board (n = 7)  (60). Overall, long-term OS under ICI treatment was again underestimated in 79 percent of the assessment reports (n = 11/14). In the second part of this study, we assessed the performance of a variety of extrapolation models in predicting long-term survival for ICIs. The models providing the best fit were those that could reflect nonmonotonic hazards (i. e. , generalized-gamma, log logistic, and lognormal).
These models are able to fit survival data for treatments that exhibit unimodal hazard functions (i. e. , hazards that initially increase, but at some point of time gradually decrease). In addition, flexible parametric spline models also provided a suitable fit to the observed data. Model selection can have a considerable impact on the lifetime survival benefit, as highlighted in the difference in RMST between the best-fit model and the extrapolated OS used in the assessment report. In two thirds of the CE assessment reports (n = 4/7), the OS was underestimated by at least 5 months (and up to 17 mo) compared to the best-fit estimate over the predefined time horizon. Such an underestimate of the survival benefit is likely to have a significant impact on the assessment of CE. A number of studies have compared the accuracy of different extrapolation approaches in modeling long-term survival of cancer patients receiving ICIs.
All extrapolation methods underestimated long-term OS compared to observed data with the exception of mixture models. However, a log-logistic function performed well across all database locks, which is also consistent with the results observed in this study. The main limitation of this study is the limited sample size. However, this study constitutes a comprehensive review of the French CE assessment reports of ICIs at the advanced or metastatic stage of the cancer. The number of CE reports evaluating ICIs in the adjuvant treatment setting was extremely limited, and since conclusions may differ according to the treatment setting, they were not selected. Although we compared the performance of a range of extrapolation models, we did not cover all types of models currently available. For some of these, such as landmark models, IPD is necessary and access to such data is challenging.
For piecewise models, there is currently no consensus on how to determine the switching point, so we did not apply such approaches. However, we did consider the majority of models that have been used in CE assessment reports to date. We acknowledge that testing mixture models, cure models, and landmark models would be of interest, in the light of the recent NICE technical support document  (18). There are also several emerging extrapolation models that show promise. A recent simulation study suggests that dynamic survival models and general additive models worth consideration, particularly when longer-term follow-up data are available and/or the patient sample size is reasonable  (64). Fitting extrapolation models to immature survival data is inherently fraught with uncertainty. If data are poor, there is a danger that no model will provide reliable predictions of long-term survival.
In this study, there was no external validation to help inform the choice of model, which is a frequent issue in CE assessment reports  (14). The use of external survival data from mature early phase trials, cancer registries, or expert opinion is a critical part of the extrapolation model selection and validation process. This is important since models that provide a good visual and statistical fit to the observed data do not necessarily provide reliable estimates of long-term survival, as found in this study. However, it is also accepted that at the time of submission to HTA agencies, external data for the intervention are often limited, and the generalizability of any long-term survival data found for the comparator must be carefully considered. To improve the robustness of these findings, it will be relevant to perform a similar analysis on progression-free survival and OS once longer follow-up data become available for all the RCTs, and to consider a higher number of CE assessment reports.
This complementary objective to the present research would also provide an opportunity to integrate the comparator arm from the RCTs, to assess relative differences in survival and the impact of underestimating betweengroup differences on the CE assessment. To evaluate the real impact of such survival differences on the CE ratio, the changes in RMST should translate into a difference in quality-adjusted life-year. An additional area of research would be to assess which of the survival functions we examined provided the best predictive accuracy when fitted to the data available at the time of the CE assessment. Finally, it would also be interesting to compare the performance of the selected models in assessment reports from other HTA agencies. For example, agencies in England and Wales, Sweden, and Norway have used different models to estimate long-term survival following treatment of RCC with nivolumab, although the data source was identical (CheckMate 025)  (63).
[Insert Figure  1  about here] Our empirical analysis employs a quasi-natural experiment around the 147 staggered changes in corporate income tax rates levied by the United States from 1988 to 2015 (47 tax increases in 24 States and 100 tax decreases in 27 States). We match these tax-rate changes with loan-level data from DealScan and firm-level and bank-level data from Compustat. Our key outcome variable is the all-in spread drawn (AISD), which is the loan spread over LIBOR plus any facility fee. Our empirical analysis proceeds in two steps. We first identify the baseline responses of loan spreads to changes in corporate tax rates and find that tax decreases bear a statistically and economically significant negative effect on loan spreads. According to our baseline results, a one-point decrease in the corporate tax rate shaves approximately 6 basis points from the AISD.
Data We obtain data from three sources. First, syndicated loan deals (at the facility level) for 1988-2015 are derived from DealScan, which includes the most comprehensive and historical loandeal information available on the U. S. syndicated loan market. Second, we identify all state corporate income tax changes in the U. S. by extending the dataset of  Heider and Ljungqvist (2015)  and  Farre-Mensa and Ljungqvist (2016). Third, we match the resulting dataset with bank-specific and firm-specific characteristics from Compustat. Table  1  defines all variables used in our empirical analysis and Table  2  reports summary statistics. The number of loan facilities in our baseline specifications ranges from 20,369 to 37,234, depending on the controls and the set of fixed effects used. These 37,234 loans are granted by 726 lead lenders headquartered in 24 states to 6,352 borrowers in 51 states.
This bias arises because two-way fixed effects (TWFE) DID regressions may not be appropriate in settings with multiple treatment periods or where homogeneous treatment effects cannot be assumed, unless there is a relatively small percentage of never-treated units. In our context, the possibility of this bias is small for two reasons. First, we do not employ a panel dataset; the unit of our analysis is at the loan level, as firms often receive more than one loan per year (i. e. , we do not follow the same loan over time). Second, we have a large group of never-treated units as controls, that places less weight on the DID estimator  (Sun and Abraham, 2021) : from a total of 6,352 borrowers in our sample, 4,502 are not subject to a tax change (never-treated units). To ensure that our inferences are robust, we implement an event study where we group all loans in a (-2, +2) year window around the corporate tax change (either increase or decrease).
Baseline results Table  5  reports the results (coefficient estimates and t-statistics) from the estimation of equation 1. We cluster standard errors by borrower's state (the cross-sectional unit of Tax increase and Tax decrease). We report the number of banks and firms from which we obtain identification in the corresponding estimations in the lower part of each table. As shown in each column, we sequentially introduce different fixed effects. Column 1 includes loan type, loan purpose, year, bank, firm, and borrower's state fixed effects. This 9 Baker, Larcker and Wang (2022) review the literature on the issue and highlight another two relevant solutions, from respective models by  Callaway and Sant'Anna (2021)  and  Sun and Abraham (2021). Both models cannot be effectively applied in our setting because they assume a panel dataset and only one change in the cross-section, while for some states (and thus treated firms), we observe more than one.
Data Collection A non-probabilistic sample of 213 Lebanese participants from cadres, managers, owners of companies, institutions, and entrepreneurs was used in the cross-sectional statistical descriptive study, of which 198 questionnaires were randomly sorted that met the study's requirements via social media. The information was gathered between March 2021 and the end of April that year. When creating the questionnaire and collecting data, all ethical principles were rigorously observed. Due to the COVID-19 pandemic's constraints, data was gathered through the electronic platform Google Forms. Some socio-demographic questions and others linked to the hypotheses being investigated were used to collect data. The following were the demographic characteristics: gender, age, occupation or field of study, marital status, educational achievement, employment status, and housing situation.
Financial Innovation-Blockchain Blockchain is a distributed ledger system that was first widely acknowledged as the core technology of Bitcoin (Satoshi Nakamoto) roughly twelve years ago (2009). Interacting and accepting new technologies are investigated in numerous theories that aimed to fill certain gaps. Among these gaps, the UTAUT model tried to explore the acceptance and use of technology in a consumer context  [12]. The concept of blockchain technology is similar to that of a database, with the difference that you interact with it in a different way  [13, 14]. A blockchain is a distributed ledger that stores transaction data in blocks. Cryptographic processes link each block to the one before it  [15]. The information is copied on each node in the network  [16]. As a result, a blockchain may be defined as a decentralized ledger on which transactions are validated by decentralized nodes.
Results and Findings The methods of data collection included some socio-demographic questions and others related to hypotheses related to the acceptance of technology (UTAUT constructs adapted for acceptance set of questions). The demographic characteristics were as follows: (gender, age, field of work or specialization, marital status, educational attainment, employment status, living environment, and knowledge of insurance in case of work interruption). Basic statistical methods were utilized to evaluate this data and apply it to statistical tests. The constructs were confirmed, and data processing was conducted using the IBM Statistics (SPSS) program. The data were collected between March 2021 and the end of April of the same year. The cross-sectional statistical descriptive study was conducted on a non-probabilistic sample of 213 Lebanese participants, including cadres, managers, business owners, lab directors, and entrepreneurs.
Several caveats associated with implementing the UN targets and indicators may hinder the utilisation of the results from comparative assessments of target fulfilment  [7, 38]. A well-known caveat is that many indicators suffer from data gaps and that existing data may be of poor quality, or lack timeliness, as data collection from all countries often takes several years  [10]. This article, however, focuses on a different caveat, namely the variation in the level of ambitiousness expressed in how the targets are presented. Substantial differences are illustrated in the formulation of targets for achieving Agenda 2030 goals in terms of their level of ambitiousness (see e. g. [59, 61, 64] ). For example, some targets are described as ultimate goals (e. g. target 2. 1 "End hunger by 2030"), whereas in some cases targets are just defined as changes in the direction of movement or specific trends (e.
Sustainable Development Report (SDR) The SDR is an annually published report providing detailed evaluations and profiles on national progress towards achieving the SDGs. It also calculates an SDG Index score for each country's overall performance and contains several analyses related, for example, to the development of different regions of the world. It includes data from 165 countries and an evaluation framework based on 121 indicators partially adopted from the official UN SDG indicator set and further complemented by additional global indicators adopted from other international and civil society organisations to ensure sufficient data robustness and coverage. The selection of indicators is based on five criteria: global relevance and applicability to a broad range of country settings, statistical adequacy, timeliness, data quality and coverage  [33].
Acknowledgements This work has been carried out as part of the  PEER-TRISD  project (" Tackling and managing risks with SDGs ") within the  Partnership for European Environmental Research (PEER) network  (https:// www. peer. eu/) consisting of eight member institutes:  UK Centre for Ecology & Hydrology (UKCEH), Oslo Centre for Interdisciplinary Environmental and Social Research (CIENS) ,  Danish Centre for Environment and Energy (DCE) at Aarhus University ,  European Commission-Joint Research Centre (JRC) ,  Finnish Environment Institute (SYKE) ,  Helmholtz Centre for Environmental Research (UFZ) ,  National Research Institute for Agriculture, Food  and the  Environment (INRAE)  and  Wageningen Environmental Research (WENR). Academy of Finland, Strategic Research Council  (Grant Number  327768 ) has supported the analysis.
Data The data used in this study are drawn from an online survey conducted in summer 2020 involving 648 consumers located in the United Kingdom (216 consumers), Spain (216 consumers) and France (216 consumers) using the online platform Qualtrics LLC (Provo, US). Consumers were recruited by Qualtrics using sampling quotas in terms of age (50 % between 18 and 46 years old and 50 % between 47 and 75 years old) and gender (50 % males and 50 % females). Only consumers who were at least 18 years old and who are responsible for food shopping in their household always or sometimes were included in the study. We obtained informed consent from all respondents in the study, and our study was approved by an institutional ethical clearance board. To ensure data quality, we took two steps. First, before presenting the series of choice tasks, we asked respondents whether they had 'devoted [their] full attention to the questions so far' and whether, in their honest opinion, they believed that we should use their responses for the study (see questionnaire in Appendix C).
This 'attention check' question has been shown by  Meade and Craig (2012)  to stimulate respondents to pay extra attention to the subsequent questions (it is not used to detect dishonest replies). We strategically placed this question right before the most important questions such as the choice tasks. Second, we included in the study only consumers who took more than one-third of the median time duration to complete the survey. The results show that the hypotheses of equality of means between socio-demographic characteristics across the three countries were not rejected at the 5 % significance level (see Table  D1  in appendix D) for gender, age, and income. Spanish consumers were more educated and have larger families than the British and French respondents. Given the quota sampling, the final samples in each country consisted of 50 % females and 50 % males, which is very similar to the most recent census data from the United Kingdom (Office for National Statistics, 2019), Spain  (INE, 2022)  and France (INSEE, 2020) (see Table  D2  in appendix D).
In this study, we used the mixed logit (MIXL) model with specification of the utility function in the WTP space, which provides estimates directly in WTP terms (i. e. currencies such as £ for United Kingdom, and € for Spain and France). We analyzed the data in three steps. First, we investigated consumers' marginal WTP (mWTP) for beef burgers, considering the design attributes' main effects only. As such, the specification of the utility (U) function in our study can be defined as follows: U njt = α n (ASC -PRICE njt + θ n1 PRODUCT njt + θ n2 CARBON njt + θ n3 ANTIBIOTICS njt ) + ∈ njt (2) where n refers to individual, j denotes each of the three alternatives available in the choice set, t is the number of choice occasions, and α n is the price scale parameter, which is assumed to be random and to follow a log-normal distribution.
The MIXL model in the WTP space was estimated using the Stata module mixlogitwtp. (Hole, 2007) We ran different MIXL models using different number of draws, both with correlated and not correlated variables. Based on logL, AIC and BIC parameters, the best model was five hundred Halton draws with correlated variables that were used in the simulations. Third, we investigated consumers' heterogeneity by calculating the distribution of the individual-level coefficients (i. e. mWTP) for PROD-UCT, CARBON and ANTIBIOTICS using the kernel density estimation across individuals with the kdensity command in Stata. Next, based on the results from the distribution of the individual-level coefficients, which indicate the presence of consumer clusters, we performed the latent class logit (LCL) model in preference space  (Greene and Hensher, 2003)  to identify consumer clusters.
To estimate the LCL model, we used the expectation-maximization (EM) algorithm, which allows for a good numerical stability and good performance in terms of runtime  (Train 2008)   (Bhat, 1997; Pacifico & Yoo, 2013; Train, 2008). The LCL model was estimated using the modules lclogit2, lclogitml2 and lclogitwtp (Hong Il, 2020) on Stata. All the models were estimated using Stata 16. 1 software (Stata-Corp  Asioli et al. Food Policy 112 (2022)   102376 6 LP, College Station, USA). U njt = α n (ASC -PRICE njt + θ n1 PRODUCT njt + θ n2 CARBON njt + θ n3 ANTIBIOTICS njt + θ 4 PRODUCT jt X GENDER n + θ 5 PRODUCT jt X AGE n +θ 6 PRODUCT jt X EDUCATION n + θ 7 PRODUCT jt X RELIGION n + θ 8 PRODUCT jt X HEARING n + θ 9 PRODUCT jt X AAS n +θ 10 PRODUCT jt XFTNS n + θ 11 PRODUCT jt X NEP n ) +∈ njt (3) D.
Overall, the results underline the importance of cross-platform shareholder structures in the study of the factors affecting the probability of conducting a successful first SECO campaign. This is consistent with studies such as  Cumming et al. (2019b) ,  Rossi and Vismara (2018)  and  Rossi et al. (2019)  that stress the importance of ECF platform differences for initial campaigns. The paper is organised as follows. Section 2 discusses the UK ECF setting for our study, the follow on funding literature, and the 1 Our sample finishing in 2018 does not reflect this trend as it excludes private or pre-emption SECOs where additional shares are offered to existing shareholders only. These are the SECO equivalent of rights issue SEOs. hypotheses to be tested. Section 3 presents our data and research design and Section 4 discusses the empirical results.
Their dataset includes 14 different ECF platforms and 426 firms that ran at least one successful campaign in Germany or the UK (using either Crowdcube or Seedrs platforms). First, they conclude that German firms which received ECF funding are more likely to fail compared to those in the UK. Second, the number of senior managers, successful follow-on ECF campaigns and the number of VC investors increase the probability of success for a private follow-up campaign, while firm age decreases the probability of success. Finally, they find that a successful private follow-up campaign decreases the probability of firm failure. Kleinert et al. (2019)  provide an interesting discussion of the role of prior financing (including ECF) as a certification effect on firm quality using a sample of 221 business plans and project descriptions for startups with ECF campaigns over April 2017-April 2018 on Crowdcube.