dataesr/scientific-paragraphs-categorization huggingface dataset:
| Long-run analysis The samples of the long-run analysis are constructed based on the main sample of the shortterm event study. We calculate BHARs for the post-event periods of 6, 12, 18 and 24 months. The number of observations in our sample diminishes with an increase in the time horizon (144 deals announced before 31 August 2020 and completed before 1 July 2021, for 12-month returns; 109 deals announced before 28 February 2020 and completed before 1 July 2021, for 18month returns; and 93 deals announced before 31 August 2019 and completed before 1 July 2021, for 24-month returns) due to the end of our investigation period on 1 September 2021. 13  To calculate the long-term performance of SPAC merger announcements and to be included in at least one of the subperiods, we require stock data to be available for the respective number of months after the announcement date.
In a further robustness test, we also use quarterly data for clustering to increase the number of clusters. The significance levels are not different from the ones reported for years. Note that we do not cluster for the two subsamples of ongoing deals and deals completed in 2020 and 2021 due to small variations in clusters. 13 Note that the sample size is not reduced due to delistings of SPACs but only due to the period of the announcement. opening price on the first business day (closing price on the day before the first business day) and the closing price on the last business day of the respective month. We define 1 month as equal to 21 business days. The 1st month begins on the first business day after the merger announcement date. We also calculate BHARs beginning from the deSPAC date, which is the closing date of the deal.
Study Design, Setting, and Population Data were collected from the French Renal Epidemiology and Information Network (REIN), an exhaustive national KRT registry. 15 The present cross-sectional study included all patients living in metropolitan France, who started KRT before the age of 20 years between January 1, 2002 and December 31, 2015. Patients with both (i) a kidney tumor or congenital nephrotic syndrome or bilateral nephrectomy and (ii) a missing or an estimated glomerular filtration rate higher than 30 ml/min per 1. 73 m 2 before KRT were excluded because they were not considered as having reached ESKD.
Outcomes We studied different indicators at KRT initiation, which are as follows: KRT initiation with dialysis (vs. pre-emptive transplantation), KRT initiation after preemptive registration on the kidney transplant waiting list (vs. not), dialysis initiation with HD (vs. PD), urgent initiation of dialysis (vs. planned), urgent HD initiation with a catheter (vs. other HD initiation), HD initiation with a catheter (vs. an arteriovenous fistula), and late referral (vs. not). The development of the variable late referral has been described elsewhere. 16 It was based on a linkage between the REIN registry and the French national health care administrative database, which have recorded all medical information since January 1, 2008. 16, 17 The date of referral was the earliest date found among the following 3 types of events: (i) the first nephrology-related prescription drug (any CKD-related drug); (ii) the first hospitalization with a nephrology diagnostic (CKD-compatible diagnosis, not acute kidney injury or pyelonephritis); or (iii) the first consultation with a nephrologist.
Social Deprivation Assessment In the REIN registry, information on the individual SES is not collected. Hence, we used a validated ecological index of deprivation, the 2011 French version of the EDI,  18  built from the 2011 French population census data and a 2006 European survey on social deprivation (European Union-Statistics on Income and Living). 19 DI is a continuous score taking into account individuals' cultural and social environments, based on the linear combination of the following 10 variables representing the proportion, in the neighborhood, of: (i) overcrowded housing, (ii) housing with no access to a bath or a shower, (iii) household without access to a car, (iv) no executive nor intermediate occupation, (v) single-parent household, (vi) household with $6 persons, (vii) unemployed people, (viii) household nonowners, (ix) persons with foreign nationality, and (x) persons with less than the first stage of secondary educational level.
18 Geographic delimitation of the neighborhood was defined by a geographic unit called Ilots Regroupés pour l'Information Statistiques (IRIS), which has been developed to divide the country into units of equal size of approximately 2000 residents, to prepare population census. 20 or each child, we assigned the IRIS, and thus a quantitative value of EDI corresponding to his/her residential address collected at the time of registration on the transplant waiting list. Indeed, the exact addresses of patients at the time of dialysis initiation were collected in the REIN database from January 2016 only, thus after our inclusion period. Before 2016, only the postal code at dialysis initiation was available. For all children who were waitlisted between January 1, 2002 and December 31, 2016 (actually almost all children as described in the Results section), we thus used their address at registration and assumed that they did not move between the dialysis initiation and registration (median time of approximately 6 months).
Population Characteristics Among 1262 children and adolescents who started KRT between January 1, 2002 and December 31, 2015, and were living in metropolitan France, 31 were excluded because they were not considered having reached ESKD before KRT (Figure  1 ). Among the remaining 1231 children constituting our target population, 116 were further excluded from the main statistical analysis because of missing data on their reported residential address: 111 had an address not precise enough to identify the corresponding IRIS and 5 gave a temporary address at the hospital. A total of 1115 patients with ESKD were thus included in the main statistical analysis, including 1038 complete cases for adjusted analyses. Compared with the patients with ESKD living in metropolitan France (n ¼ 1231), the 1115 patients included in the main analysis and the 1038 patients contributing to the adjusted analyses had similar characteristics (Supplementary Table  S1 ).
Moreover, we implicitly discuss systemic risk through a topological-based network's resilience measure. As a further remarkable similarity between the quoted paper and ours, we also use overlapping portfolios to define the adjacency matrix. However, we are not able to apply the test proposed in  [28]  since we cannot evaluate the probability that the overlap between two funds is larger than the observed onesee Equation (  1 ) in  [28]. Furthermore, we point out that in our context, we do not study contagion risk. In this respect, the proposed resilience measure is grounded on the comparison between unshocked and shocked frameworks, without any insights on the possibility of having contagion channels. The methodological proposal is tested over different networks built using the dataset on funds provided by Morningstar Direct (MD) and Morningstar European Data Warehouse (EDW).
Empirical application In this section, we analyze four different networks. The nodes of the first two networks are funds that are High ranked for ESG compliance. The two networks differ from each other for the size of funds. The second two networks consist of nodes/funds which are Low ranked for ESG, and again they differ for the funds' size. We proceed first by introducing the dataset used to construct the networks. Then, we compute the resilience measure of the networks at hand, and we show that the network of the High ranked Small size funds is more resilient than the network of the Low ranked Small size funds. The same does not apply when comparing the networks of High and Low ranked Big size funds.
The dataset We consider the cross section of the open-end equity mutual funds quoted in June 2018, provided by Morningstar Direct (MD). MD reports all data at the fund share class level, including the funds' holdings that are common across different share classes. After eliminating funds without the ISIN, consistently with  [58] , we aggregate mutual-fundshare-class-level observations to one fund-level observation using the unique fund identifier (FundId) in MD. For such sub-sample, we have been then able to retrieve funds' holdings for 10421 funds from the European Data Warehouse (EDW) by Morningstar. Out of them, we keep only funds whose Total Net Asset value (TNA) is available. We also exclude from the sample the funds whose holdings sum to a number higher than one and those whose holdings sum to a number lower than 80%.
To receive a portfolio ESG score, at least 67% of the assets under management must have a company ESG score. Then funds are ranked according to 5 categories: High (H), Above Average (AA), Average (A), Below Average (BA), Low (L). High(Low)-ranked funds are those in the top(bottom) 10% of the score distribution. Below Average funds have a score that is between the 10-th percentile and the 32. 5-th percentile of the score distribution. The Average-ranked funds are those in the next 35% of the distribution. Above Average funds are ranked in the range between the 67. 5-th percentile and the 90-th percentile. 2 Out of 5898 mutual funds, 519 are ranked as Low, 1306 as Below Average, 2150 as Average, 1368 as Above Average and 555 funds are ranked as High. For the sake of clarity, we now proceed with a brief description of the entire dataset, even if -as we will see below -we will refer for the analysis to the Low and High ranked funds.
Conclusions This paper proposes a new concept of resilience measure based on the destabilizing effect of the shock on the community structure of a network, measured through the clustering coefficient. The theoretical proposal is validated through an empirical analysis performed on a high-quality dataset, which is particularly appropriate for testing the methodology. Specifically, we take into account the interrelation between funds as measured by the share of assets in common and analyze how an external shock impacts it. Using a worldwide cross-section of investment funds, we find that the networks of Small funds tilting their portfolio through more responsible assets present a higher degree of resilience to different severity and occurrence probability of exogenous shocks compared to the network of Small funds investing in less responsible assets.
Acknowledgments: This research is partially funded by Morningstar, Italy (contract. n. OPP635470), Etica Sgr, Italy (ref. n. R01-2019), Ministero dell'Istruzione, dell'Università e della Ricerca, Italy (project n. SIFI6_00055), Fondazione Cassa di Risparmio di Perugia, Italy (ref. n. 2017. 0226. 021), and the University of Perugia, Italy (Fondo Ricerca di Base 2018).
✩ Acknowledgments: This research is partially funded by Morningstar, Italy (contract. n. OPP635470), Etica Sgr, Italy (ref. n. R01-2019), Ministero dell'Istruzione, dell'Università e della Ricerca, Italy (project n. SIFI6_00055), Fondazione Cassa di Risparmio di Perugia, Italy (ref. n. 2017. 0226. 021), and the University of Perugia, Italy (Fondo Ricerca di Base 2018).
Each thermal retrofitting service (installation of heat pumps, wall insulation, etc. ) was modelled using the monetary input-output (industry x industry) from EXIOBASE (version 3. 3. 15) [15]. EXIOBASE is a multi-regional input-output (MRIO) database that was initially developed from 2007 to 2011 under the 7 th framework program of the European Commission, more precisely as a result of the EXIOPOL project  [16]. As well as equipment and insulation materials, the energy sources were modelled using the monetary input-output table of EXIOBASE (version 3. 3. 15). For that, the physical flows (in TWh) from Table  2  were converted into monetary flows (in million euros) using the basic prices from EXIOBASE. In this case study, the main financial transactions are presented in Figure  2.
Objectives: Extrapolation is often required to inform cost-effectiveness (CE) evaluations of immune-checkpoint inhibitors (ICIs) since survival data from pivotal clinical trials are seldom complete. The objectives of this study were to evaluate the accuracy of estimates of long-term overall survival (OS) predicted in French CE assessment reports of ICIs, and to identify models presenting the best fit to the observed long-term survival data. Methods: A systematic review of French assessment reports of ICIs in the metastatic setting since inception until May 2020 was performed. A targeted literature review was conducted to collect associated extended follow-up of randomized controlled trials (RCTs) used in the CE assessment reports. Difference between projected and observed OS was calculated.
A range of standard parametric and spline-based models were applied to the extended follow-up data from the RCT to determine the best-fitting survival models. Results: Of the 121 CE assessment reports published, 11 reports met the inclusion criteria. OS was underestimated in 73 percent of the CE assessment reports. The mean relative difference between each source was À13 percent (median: À15 percent; IQR: À0. 4 to 26 percent). Models providing the best fit were those that could reflect nonmonotonic hazards. Conclusions: Based on the available data at the time of submission, longer-term survival of ICIs was not fully captured by the extrapolation models used in CE assessments. Standard and flexible parametric models which can capture nonmonotonic hazard functions provided the best fit to the extended follow-up data.
The publication of extended follow-up data from RCTs is an opportunity to assess retrospectively the accuracy of OS predictions in CE assessments and to explore differences in bestfitting models between the CE assessments and medium-or longterm data from RCTs of ICIs. The objectives of this study were twofold. The first objective was to evaluate the accuracy of estimates of long-term OS predicted in French CE assessment reports for ICIs. This involved comparison of OS extrapolated from RCT data available at the time of submission to actual OS data collected during extended follow-up of these same RCTs. The second objective was to identify models providing the best fit to the observed survival data from extended follow-up and to compare the difference in restricted mean survival time (RMST) of these projections with the ones selected in the CE assessments.
Identification and Selection of CE Assessment Reports The HAS Web site was searched for all published assessments of pharmaceutical products by the economic evaluation committee (CEESP) of the HAS since inception (2013) until 15 May 2020 (cutoff)  (21). Only assessments related to ICIs indicated for the treatment of advanced or metastatic cancer were retained. In a second step, the clinicaltrial. gov Web site, PubMed database, and conference proceedings for the American and European Societies for Clinical and Medical Oncology were searched for publications of longer-term follow-up data from the RCTs used in the CE assessment. The search algorithm is presented in Supplementary Material 2. For the first phase of the study, RCTs were retained if at least 6 months of extended follow-up data since those used in the CE assessment report were available to identify potential differences between projected and observed OS.
Data Extraction The following information from the RCT used in the CE assessment reports was extracted: nonproprietary name of the ICI, the indication, RCT identifier, the duration of follow-up for OS, and the maturity of data, defined as the proportion of deaths across all patients included in the ICI arm at the time of the data cutoff. The type of model selected to extrapolate OS, the selected time horizon, and OS at specific landmarks were retrieved from the French CE reports. In the published reports of extended follow-up of the RCT, duration of follow-up and survival probabilities with confidence intervals at similar time points to those selected in the assessment report were extracted. OS were documented at the most robust available follow-up date, which was identified as either the one reported in the publication or, if not relevant, an earlier date where at least 10 percent of the patients were still at risk  (22).
Data Analysis Extrapolated survival curves obtained from French CE assessments were digitized and then plotted in R software to evaluate the reliability of the digitization and obtain long-term survival predictions. Kaplan-Meier curves for OS for the ICIs in the identified clinical trials with extended-follow-up were digitized using Engauge Digitizer v3. 0. We then applied the Guyot algorithm on Kaplan-Meier curves from clinical trials to obtain pseudo-individual patient data (IPD)  (23). The accuracy of digitization was validated by comparing median OS between the RCTs and the pseudo-IPD and with visual inspection between the generated and published Kaplan-Meier curves. Part 1: Descriptive Comparison of OS between Extrapolated and Actual Data from Extended Follow-up of RCTs Difference in OS was calculated between the most robust follow-up time points in the extended follow-up of RCT and the same time points of the extrapolated OS curves in the CE assessment reports.
The relative difference was expressed as a percentage of the observed survival probability, and the absolute difference was expressed as a percentage point difference. In the absence of a robust method to quantify the precision of these differences, they were assigned to one of four classes, considered negligible (≤AE5 percent), minor (>AE5 percent and <AE11 percent), moderate (≥AE11 percent and <AE20 percent), and major (≥AE20 percent). Relative differences results were presented according to the cancer type, the extended follow-up available, the model used for extrapolating OS in the CE assessment report, and the maturity of data at the time of the submission.
Part 2: Evaluation of Extrapolation Methods Different extrapolation models were applied to the pseudo-IPD generated from the OS Kaplan-Meier curves with at least 18 months of extended follow-up. Methods for extrapolation of survival data used approaches recommended in the published literature as directed in the HAS methods guide  (13;14;18;24). Standard parametric models (Gompertz, Weibull, log-logistic, lognormal, gamma, and generalized gamma) and spline-based models (normal, odds, and hazard with one and two knots) were tested. For one-knot spline model, the knot was located at 50 percent survival. For two-knot spline model, these knots were placed at 33 percent and 67 percent survival, the flexsurv R package default  (24). The number of knots was limited to avoid being too specific to the curve.
The RSMT, estimated as the area under the survival curve over a given time horizon  (29) , was calculated for both the best-fit models and the second best-fit models. Finally, the difference in RMST between the extrapolation from the CE assessment and the one selected in our analysis was calculated. All survival analyses were implemented on RStudio Software v4. 0. 0. Extrapolations with standard parametric or spline-based models were performed using flexsurv R package, (24;30), whereas hazard plots were produced with muhaz R package  (28).
Identification of CE Assessment Reports Overall, 121 CE assessment reports were available on the HAS Web site, but only seventeen were related to ICIs in advanced or metastatic cancer (Figure  1 ). Six of them were excluded from the analysis due to the lack of published 6-month extended follow-up data (n = 4), the analysis population difference between the assessment report and the extended follow-up of RCT (n = 1), or the CE assessment report was a resubmission (n = 1). The characteristics of the remaining eleven assessments studied in Part 1 are presented in Table  1. The seven assessments evaluated in Part 2 are presented in Table  2. Five assessment reports presented the CE of pembrolizumab as monotherapy or in combination with chemotherapy. The other reports were related to either nivolumab in monotherapy (n = 4), nivolumab in combination with ipilimumab (n = 1), or durvalumab (n = 1).
The selected CE reports referred to assessments in lung cancer (n = 6), melanoma (n = 2), renal cell carcinoma (RCC; n = 2), and urothelial cancer (n = 1). The median follow-up of RCT at the submission ranged from 8. 3 to 25. 9 months. For six of the CE assessment reports, the extrapolation was performed from randomization, whereas a piecewise approach was adopted in the remaining five reports. In the first case, a variety of statistical models were used for the extrapolation. For the piecewise approach, the initial section of the survival curve was modeled using the Kaplan-Meier function up to a predefined time point, whereafter an exponential (n = 3) or a log-logistic model (n = 1) was applied. For pembrolizumab in metastatic melanoma, an "atypical" approach was considered (Kaplan-Meier curve for the 60 first weeks and then OS data from pooled clinical trials and a registry).
Extended OS Follow-up Available For the eleven retained RCTs for which at least 6-month extended follow-up data were available, the mean additional duration of this extended follow-up period was 28. 2 months (range: 6-52 mo; Table  1 ). The available extended follow-up duration was less than 1 year in two case studies, and more than 3 years in four studies. Comparison of Extrapolated and Observed OS from Extended Follow-up of RCT Actual long-term OS was underestimated in 73 percent of the CE assessment reports (n = 8). Overall, the mean relative difference was À13 percent (range: À38 percent, þ10 percent [Figure  2 ]; median: À15 percent; IQR: À26 percent, 0. 4 percent). The mean absolute percentage point difference was À3 percent (range: À10 percent; þ6 percent; Supplementary Material 3).
In five cases, the extrapolated survival fell outside the 95 percent confidence interval of the OS observed in the extended follow-up. OS was under or overestimated by more than 5 percent in the majority of CE assessment reports (n = 8). In six cases, long-term OS was underestimated by >5 percent, including five cases where it was underestimated by greater than 20 percent (i. e. , major; Figure  2 ). Only two CE assessment reports overestimated the long-term OS with a difference greater than 5 percent (namely, durvalumab in nonsmall-cell lung cancer [NSCLC] and pembrolizumab in urothelial carcinoma). Negligible differences were observed in the remaining three CE opinions. Unreliable estimates of OS were observed across all types of ICIs and indications. Unreliable estimates greater than 5 percent of OS were observed whatever the model used to fit the data (Figure  2 ).
However, the discrepancy between estimated and observed OS was larger for RCTs with longer extended follow-up since the cutoff date for extrapolation used in the CE assessment report. This underestimation was most pronounced when the extended follow-up was greater than 3 years, with a mean relative difference of À25 percent. In addition, no trend was observed between the maturity of the data (number of events at the time of the submission) at the time of the CE assessment reports and the accuracy of the OS predictions (Figure  S9  in Supplementary Material 4).