dataesr/scientific-paragraphs-categorization huggingface dataset:
Elements in Eqs (  3 ) and (  4 ) are defined in S1D Appendix in S1 Appendix. Readers can refer to S1E Appendix in S1 Appendix for the interpretation of STE, in combination with R 2 trial and decision-making as suggested by the German Institute for Quality and Efficiency in Health Care  [21]  Available functions in the frailtypack R package for surrogacy evaluation In this section, we introduce the new R functions, used to estimate model  (1). Functions for data generation and simulation studies are also described.
times = TRUE, print. iter = FALSE) The mandatory argument of this function is data, the dataset to use for the estimations. Argument data refers to a dataframe including at least 7 variables: patientID, trialID, timeS, statusS, timeT, status and trt. The description of these variables, like other arguments of the function, can be found in S2A Appendix in S2 Appendix, or via the R command help(jointSurroPenal). The rest of the arguments can be set to their default values. In addition, details on the required arguments/values are given in the illustration section. The jointSurroPenal object. The function jointSurroPenal() returns an object of class 'jointSurroPenal', if the joint surrogate model has been estimated. We describe in S2A Appendix in S2 Appendix some of the relevant returned values, as well as the functions which can be applied to this object.
Data generation using the R function jointSurrSimul() For data generation purposes, we implemented the algorithm described in Sofeu et al. [17]  in the R function jointSurrSimul(). The generation procedure is based on model  (1). A variant of this algorithm is to base generation on a model that includes just a shared frailty term at the individual level as described by Rondeau et al. [28]. This function is defined as follows: jointSurrSimul(n. obs = 600, n. trial = 30, cens. adm = 549. 24, alpha = 1. 5, jointSurroTKendall(object = NULL, theta, gamma, alpha = 1, zeta = 1, int. method = 0, sigma. v = matrix(rep(0, 4), 2, 2), nb. gh = 32, nb. MC. kendall = 10000, random. generator = 1, random. nb. sim = 0, random = 0, seed = 0, ui = 1).
Computational details and package installation Estimations in the proposed functions are based on Fortran programs, with parallel computing using OpenMP, to speed up calculations. Thus, we used R as an interface between the user and the Fortran compiler. The stable version of frailtypack is available on the Comprehensive R Archive Network (CRAN)  [29]. Furthermore, the ongoing version can be found on GitHub at https://github. com/socale/frailtypack. A list of other models implemented in frailtypack  [23]  can be found in S1 Fig. The results in this paper were obtained using R version 3. 5. 2 and the frailtypack package version 3. 0. 3, using a processor Intel (R) Xeon(R) CPU E5-2690 v2 @ 3. 00GHz including 40 cores and a Read Only Memory (RAM) of 378 Gb. A standard laptop and a desktop PC under recent versions of R can be used to fit the model.
The results will be the same, but with longer computing time. For example, using a standard desktop PC in the application, the fit took around 1 hours compared to 9 min with a server including 40 cores and a RAM of 378 Go. The frailtypack package can be installed in any R session using the install. packages command as follows: Installation via GitHub is possible thanks to the devtools package. All dependencies required by frailtypack must be installed first. The installation commands are: Finally, frailtypack must be loaded using the command:.
Data source We illustrate the use of the developed functions with the individual patient data of the Ovarian Cancer Meta-Analysis Project  [24]  and a generated dataset based on model  (1). We also describe the simulation studies at the end of this section. Description of dataOvarian dataset. The dataOvarian dataset combines data that were collected in four double-blind randomized clinical trials in advanced ovarian cancer. In install. packages ("frailtypack", dependencies = T, type = "source", repos = "https://cloud. r-project. org") install. packages (c("survC1", "doBy","statmod"), repos = "https://cloud. r-project. org") devtools::install github ("socale/frailtypack", ref = "surrogacy submetted 3-0-3") library (frailtypack) the first two trials of this study, data were available on the centers in which patients were treated, and each of the two trials were considered as a homogeneous group according to the investigators.
Finally, the statistical unit in the first two trials was center and it was trial for the last two trials. Therefore, a total of 50 units were available for surrogacy evaluation. The objective in these studies was to examine the efficacy of cyclophosphamide plus cisplatin (CP) versus cyclophosphamide plus adriamycin plus cisplatin (CAP) to treat advanced ovarian cancer. The candidate surrogate endpoint S was progression-free survival time (PFS), defined as the time (in years) from randomization to clinical progression of the disease or death. The true endpoint T was survival time, defined as the time (in years) from randomization to death from any cause. The dataset includes 1192 subjects with 82% of PFS-related events at a median survival time of 78. 7 days [Interquartile range (IQR): 36.
6-202. 5], and 79. 8% of deaths at a median survival time of 111. 4 days [IQR: 56. 0-275. 9]. Data can be loaded as follows: By displaying the structure of this dataset, we can find the same structure as in the function jointSurroPenal(), with 7 variables. The column trialID here refers to the analysis unit. Generated dataset. In the example below, we generate a meta-analysis including 600 subjects in 30 trials. Arguments α, θ, z and γ are fixed to obtain a Kendall's τ of 0. 61, which is obtained using the jointSurroTKendall() function as follows: jointSurroTKendall (theta = 3. 5, gamma = 2. 5, alpha = 1. 5, zeta = 1) [1] 0. 6062975 Otherwise, the trial level surrogacy, R 2 trial is fixed to 0. 8. This could correspond to simulation design including high trial level and high individual level surrogacy.
Surrogacy evaluation In this section, we use the dataset previously described to illustrate the evaluation of the surrogate endpoints based on the one-step joint surrogate model  (1). Different arguments of the associated functions will be explored as the returned values. Model estimation based on the advanced ovarian cancer meta-analysis dataset. From a practical point of view, the most important arguments for using the jointSurroPenal() function beyond the standard argument (data) concern the following: the parametrization of the model (with arguments indicator. zeta and indicator. alpha), the method of integration and associated arguments (int. method, n. knots, nb. mc, nb. gh, nb. gh2, adaptatif), the smoothing parameters (init. kappa and kappa. use) and the scale of survival times (scale).
knots) by a numerical value between 6 and 10; providing new values for the smoothing parameters (init. kappa). Users can also set the arguments α or z to 1 (indicator. zeta = 1 or indicator. alpha = 1) to avoid estimating these parameters. We also recommend changing the integration method with the arguments int. method and adaptatif. For example, by using adaptatif = 1 for integration over the random effects at the individual level, one could use the pseudo-adaptive quadrature Gaussian-Hermite integration instead of the classical quadrature Gaussian-Hermite method. By changing the scale of the survival times (argument scale) and considering years instead of days, it is possible to solve some of the numerical issues. Using the default values based on the advanced ovarian cancer dataset, the model did not converge.
We also compute and display the surrogate threshold effect with the associated hazard risk. We obtain an acceptable value of STE (-0. 273, HR = 0. 761), which illustrates the high validity of the surrogate. As mentioned by  [22] , unrealistically large/small values of STE (e. g. , corresponding to a HR of less than 0. 5) would indicate too wide prediction limits and, consequently, poor validity of the surrogate. Therefore, as observed previously  [8] , PFS can be considered as a valid surrogate endpoint for OS when evaluating new treatments for advanced ovarian cancer. The last part of the results describes the convergence parameters. Model estimation based on generated dataset. Here, we estimate two joint surrogate models for the purpose of model comparison, based on the generated dataset data.
sim. Integrals are approximated using a combination of Monte Carlo and classical Gauss-Hermite in the first model and a combination of Monte Carlo and Pseudo-adaptive Gauss-Hermite integration in the second one. The codes for both models are described as follows: A relevant question in this case might be how to compare different models, or how to choose the optimal value of the number of knots for spline, the number of quadrature points, the number of samples for Monte-Carlo, or the optimal integration method. We propose in this package to base comparison on the approximated likelihood cross-validation criterion. The lower the value obtained for this parameter, the better the associated model will be. Choice of model based on LCV. The LCV for models joint. surro. sim. MCGH and joint. surro.
Convergence parameters Graphical representation of baseline hazard and survival functions. By using the generic function plot(), it is possible to plot the baseline hazard and survival functions for both surrogate and true endpoints. The definition of this function is shown below, and the associated arguments are described in S2E Appendix in S2 Appendix. plot(x, endpoint = 2, scale = 1, type. plot = "Hazard", xmin = 0, conf. bands = TRUE, xmax = NULL, ylim = c(0, 1), Xlab = "Time", pos. legend = "topright", main, cex. legend = 0. 7, Ylab = "Baseline hazard function") Fig  1  represents the baseline survival and hazard functions for model, for both the surrogate and the true endpoints using the advanced ovarian cancer meta-analysis dataset. We limit survival times to 8 months since after this threshold, the estimated survival probabilities are almost equal to 0.
Simulation results can be displayed using the S3 method summary(). This function allows argument R2boot to specify whether the confidence interval of R 2 trial will be computed using parametric bootstrapping (1) or the Delta method (0). In the first part of the results, we present a brief summary of simulation and estimation parameters, and the average number of iterations to reach convergence (n. iter = 14). The next part presents a table of simulation results. Each row of the table corresponds to a model parameter. The first column is the name of the parameter, followed by the value assigned to the parameter during simulation. The next three columns correspond to the average of the estimates observed for all the generated datasets, the empirical standard errors and the mean of the estimated standard error.
Discussion This paper presents new tools for validating candidate surrogate endpoints using data from multiple randomized clinical trials, with failure time endpoints. Since version 3. 0. 1, The R frailtypack package implements the joint-surrogate model, which is a more attractive approach than two-step approaches for evaluating surrogate endpoints based on a one-step analysis strategy. The joint-surrogate model demonstrated better performances than the two-step copula model or the one-step Poisson approach  [17]. Furthermore, the new model showed stable results even with a moderate trial size or number of trial as commonly encountered in practice, whereas the adjusted model estimated with the Bayesian framework showed unstable results  [11]  n. knots = 6 n. iter = 14 Rejected datasets: n(%) = 0(0).
Parameters By varying the values of the arguments in the jointSurroPenal function, convergence of the model is not always guaranteed. Therefore, it is important in the event of convergence issues to know how to play with the arguments/values couple as shown in the previous section. Thus, users can choose the method of integration, initial values, the number of nodes for splines and the smoothing parameters, the number of nodes to use for the Gauss-Hermite quadrature and the number of samples for the Monte-Carlo integration when applicable, the random number generator, and other necessary arguments. It is also possible to set some parameters of the model in the event of identifiability issues. This underlines the flexibility of the frailtypack package in managing convergence issues. This flexibility is quite different from that obtained with the surrosurv package  [9]  or macros SAS  [10]  for evaluating surrogate endpoints using the two-step Copula model or one-step Poisson model.
Thus, in the event of convergence, change in arguments/values mostly produced similar results. For example, when we reduced the number of samples for Monte-Carlo integration to 100 (nb. mc = 100) in the application based on the advanced ovarian cancer meta-analysis dataset, we observed R2trial = 1. 000 [95%CI: 0. 998-1. 002), R2boot = 0. 981 [95%CI: 0. 891-1. 000], Kendall's τ = 0. 683 [95%CI: 0. 664-0. 695], STE = -0. 291 (HR = 0. 747) and LCV = 9. 161. These results are quite similar to those using nb. mc = 200 (see illustration section in manuscript). In addition, if we integrate over the random effect at the individual level using the pseudo-adaptive Gaussian-Hermite quadrature (argument adaptatif = 1) instead of the classical Gaussian-Hermite quadrature, the results are similar with R2trial = 1.
Numerous tools have been presented in this paper for evaluating surrogacy. We have the following: the surrogate threshold effect which is used in combination with R 2 trial to assess the validity of the potential surrogate endpoint; the predict() function used in a new trial to predict the treatment effect of the true endpoint based on the observed treatment effect on the surrogate endpoint; and the leave-one-out cross-validation which can be used to assess the accuracy of the prediction using model  (1). Furthermore, a graphical representation of the baseline hazard and survival functions is possible using the plot() function. The jointSurroPenal() function can also be used in interim analyses to estimate the fixed treatment effect on the surrogate endpoint, taking into account competing risk of death and heterogeneity in the data at the individual level and at the trial level in interaction with treatment.
This is an alternative to the joint frailty-copula model between tumor progression and death for meta-analysis proposed in  [34]. We now plan to extend the model (  1 ) and the jointSurroPenal() function to take into account interval censoring for endpoints where the exact event times are unknown. This extension will also make it possible to model the baseline hazard functions parametrically, using a Weibull distribution. To improve the use of frailtypack, intuition can be gained by developing an associated interactive web app using the R package Shiny available at https://CRAN. R-project. org/package=Shiny.
joint. simul10 <-jointSurroPenalSimul(nb. dataset = 10, nbSubSimul = 600, ntrialSimul = 30, LIMparam = 0. 001, LIMlogl = 0. 001, LIMderiv = 0. 001, nb. mc = 200, nb. gh = 20, nb. gh2 = 32, true. init. val = 1, print. iter = F) summary(joint. simul10, R2boot = 0) Simulation and estimation pamareters nb. subject = 600 nb. trials = 30 nb. simul = 10 int. method = 2 nb. gh = 20 nb. gh2 = 32 nb. mc = 200 kappa. use = 4.
Acknowledgments The authors thank the Ovarian Cancer Meta-Analysis Project for sharing the data used to illustrate the programs. This work was supported by the  Association pour la Recherche sur le Cancer , Grant/Award Number:  PJA20161205147 ;  Institut National du Cancer , Grant/Award Number:  2017-125 ; Institut national de la santeet de la recherche medicale; Region Aquitaine. We also thank  Antoine Barbieri ,  INSERM U1219 , for his support for programming the Bayesian approach. We gratefully acknowledge very helpful and constructive comments and suggestions from the academic editor and the three anonymous referees, which lead to significant improvements of this manuscript.
Primary energy prices: oil, natural gas and coal The price of primary energy has an effect on the prices of allowances. In this regard, when there is an increase in the price of oil and gas, the use of coal to produce energy is seen on the rise. Following this principle, we can say that CO2 emissions increase, which leads to an increase in the price of allowances (El Amri, Boutti, Rodhain, 2020). In order to achieve this, we analyze the future prices of primary energies to show the price variations of the EUA (European Union Allowance) according to the needs of industrial firms, when the carbon market is launched. The future markets for these energies are the most liquid. The data was taken from Reuters in Bourgeois (2010). The brent price (expressed in Euro/BBL) is the brent crude futures Month Ahead price negotiated on ICE (Fig.
The first is known as Product Development Partnerships  (PDPs) , that are formed to develop pharmaceutical solutions for low and middle income countries; whereas the second type of PPPs, known as 'pre-competitive PPPs', are formed to generate novel scientific concepts (e. g. disease targets) and infrastructure (e. g. databases) by pooling of complementary expertise and knowledge, and sharing of rewards. Apart from these two types, another type of PPP exists in the form of 'access partnerships', which are formed to exclusively focus on delivery of existing technologies or health service delivery  (Grace & Britain, 2010). Notwithstanding the increasing formation of PPPs in general and PDPs in particular and their significance in the global health system, there is a gap in the strategic management and organisational literature on the phenomenon.
The decrease in family farm succession is a problem in many countries worldwide. However, no systematic compilation of relevant studies exists. The method of analysis described in this article enabled us to obtain descriptive statistical results such as: most-studied variables, least-studied variables, patterns in the effects of the variables on succession, new types of variables analyzed, and principal approaches of the qualitative studies. We conducted a literature search in four databases, which resulted in 59 papers relevant to the topic. Twenty-nine of them are quantitative and 30 are qualitative. The literature search in peer-reviewed journals in English resulted primarily in articles from developed countries. Due to this, the selection, analysis and results turned out to be focused on Global North countries.
Methodology To conduct the literature search, we utilized an adaptation of the methodology presented by  Fink (2010)  and applied also by  Suess-Reyes and Fuetsch (2016). We used the following steps: 1 selecting bibliographic databases; 2 choosing search terms; 3 applying practical and methodical screening criteria; 4 conducting the review; 5 synthesizing the results. Regarding step 1 ("selecting bibliographic databases"), we consulted seven databases: DOAJ, Google Scholar, JSTOR, Scopus, Springer, ScienceDirect and Web of Science. In step 2 ("choosing search terms"), we chose for the seven databases the following key words: farm succession/transfer, farm, and family. Each was separated by the Boolean operator "AND" which ensures papers related to family farm succession would appear in the search results.
On the other hand,  Glauben et al. (2004)  showed that family farm succession could be negatively affected by the educational level of the father. Others, such as  Mishra and El-Osta (2008) , link a higher education level of the incumbent with higher rate of family farm succession, this is because a higher level of education implies greater skills, which, if employed for farming entail a greater farm profitability, something that is more attractive for prospective successors. The latter authors used farm-level data to investigate the impact of government farm policy and farm growth on both succession decisions and the likelihood of intra-family transfers of the farm business. This research, used a particularly large sample of more the one million US farm operators 45 years of age or older.
68  In practice, it should be obvious that there is not common knowledge: the most difficult economic challenges relate to assessing the future course of economic organization, politics, and technology, about which there is never likely to be agreement, no matter how much data we have available. Economic models should help us understand how economies behave in such circumstances. To assume an economic framework 68. It has long been known that RE equilibrium are not in general (constrained) Pareto efficient (see  Newbery and Stiglitz 1982). Accordingly, it is not that much of a surprise that deviations from RE might be welfare enhancing. in which none of these challenges arise is likely to be at most of limited help. Here, we have constructed a model where the underlying driver of technological change is sufficiently complex that it is not easily discoverable by economic agents.
In their model, seasonality and migration parameters were drawn from prior distributions, and other parameters were fixed to values drawn from the literature. They used this model to examine possible trajectories that the worldwide epidemic could take over the next months and years. We built upon this model to investigate the influence of seasonality and summer holiday migrations on the epidemic in France. In particular, we used empirical data to inform the pattern of migrations between French regions in the mainland, and we take into account migrations in both directions: to the holiday region and back. We also used estimated reproduction numbers and combined them with a model of seasonality.
Purpose: Although quantitative benefiterisk models (qBRms) are indisputably valuable tools for gaining comprehensive assessments of health care interventions, they are not systematically used, probably because they lack an integrated framework that provides methodologic structure and harmonization. An alternative that allows all stakeholders to design operational models starting from a standardized framework was recently developed: the discretely integrated condition event (DICE) simulation. The aim of the present work was to assess the feasibility of implementing a qBRm in DICE, using the example of rotavirus vaccination. Methods: A model of rotavirus vaccination was designed using DICE and implemented in spreadsheet software with 3 worksheets: Conditions, Events, and Outputs. Conditions held the information in the model; this information changed at Events, and Outputs were special Conditions that stored the results collected during the analysis.
4, 8 19e21 The latter is crucial for ensuring transparency, with the aim of supporting informed decision making. 4, 8 ecently, an alternative for designing and structuring decision-analytic models was developed: the discretely integrated condition event (DICE) simulation. 22 DICE brings together the common modeling approaches into a standardized framework that can be used by any stakeholder to structure a decision problem. A DICE model is conveniently specified in a series of tables that follow a consistent format, and the simulation can be fully implemented in spreadsheet software, making design, analysis, review, and validation straightforward. 23 lthough DICE simulation is increasingly applied in health economics, 24e28 it has not yet been used for BRA. It was tempting to challenge this novel approach with actual problems of BRA.