dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyses All analyses were carried out using SAS 9. 2 (SAS Institute, Cary, North Carolina, USA). The simulation datasets generated for the 12 mechanisms of missing data at different volumes of missing data and the two patterns of real missing data (GERAS-1 and GERAS-2) were each run against the two different imputation methods described previously (complete cases, MI MCMC). In addition, the impact of applying a combination of imputation methods to missing data was explored using two different combination imputation scenarios for the GERAS-1 and GERAS-2 datasets. In Combination Scenario A, missing cost data for patients lost to followup (assumed to be MCAR) were imputed using the naive method of grouped means imputation, which uses the mean costs from non-missing observations for each MMSE severity group at baseline (mild, moderate; and moderately severe/severe AD dementia based on MMSE scores of 21-26, 15-20 and <15, respectively) to impute missing data within each severity group; missing cost data for patients institutionalised (assumed to be MAR) were imputed using the MI MCMC method (including the factors MMSE score, ADCS-ADL score and caregiver time); and missing cost data for patients who died (assumed to be MAR) were imputed using the MI MCMC method (including the factors patient age, MMSE score and ADCS-ADL score).
In Combination Scenario B, a fixed cost (€2940 per month, cost of institutionalisation) was used for missing cost data for institutionalised patients (assumed to be MNAR); the imputation methods used for missing cost data for patients who died or were lost to follow-up were the same as for Combination Scenario A. To avoid the results being influenced by one particular set of data, we performed each imputation scenario 1000 times on slightly different sets of data generated by random selection with replacement for each of the 14 simulation datasets generated. The estimates of these costs from the 1000 iterations were combined and compared with the 'true' cost estimates from the complete sample with no missing cost data. Six outcome measures were used to assess the performance of the imputation methods: (1) mean costs (in Euros) for both the 'true' (actual) mean cost from the complete sample and the estimated mean cost from the imputed simulation datasets; (2) absolute and relative bias: calculated as the difference in mean costs between the actual costs for the GERAS complete sample dataset and the estimated cost for the analysed sample; i.
Alternative methods for costing caregiver time To put the impact of using different imputation methods for dealing with missing cost data into context, we assessed the effects of using different methods to calculate informal care costs on the estimate of total societal costs. In the base case analysis, which was the same as the 'true' mean cost used for the simulations, total societal costs were calculated excluding caregiver supervision hours in the opportunity cost approach. Alternative total societal cost estimates were generated where: (1) caregiver supervision time was included, (2) replacement costs were used instead of opportunity costs, and (3) only caregiver time for working caregivers was included for caregiver informal costs (i. e. caregiver time for non-working caregivers was not costed; see Wimo et al.
The results of the simulations for the different mechanisms of missing data for all naïve and MI methods examined are available on request. Table  3  shows how the different imputation methods, including both combination imputation scenarios, performed when the cost data were generated based on the actual level of missing cost data at the 18-month assessment of the GERAS study [33 % missing data in total, due to institutionalisation (15 %), death (6 %) and lost to follow-up (12 %)]. For dataset GERAS-1, the MI MCMC method performed as well as Combination Scenario A, bias being -3 % for both methods. In comparison, the complete cases method showed a higher level of bias (-7 %). Although both the MI MCMC and Combination Scenario A imputation methods gave good estimates of the mean costs (i. e.
-3 % bias) for the GERAS-1 dataset, they underestimated the SE because the SEE/SSE ratios were 0. 64 and 0. 66, respectively. For dataset GERAS-2, the MI MCMC method performed better than the complete cases method (Table  3 ). A better approach was to use a combination of imputation methods, especially Combination Scenario B, which had a low level of bias (-1 %), even though it still underestimated the SE (i. e. the SSE was 49, which was lower than 61, the SE for the complete sample). Combination Scenario B gave a lower bias (-1 %) than the MI MCMC method (-6 %), and the SEE/SSE ratio was superior (0. 86 for Combination Scenario B versus 0. 53 for MI MCMC).
Discussion Given that the volume of missing data and the methods used to deal with missing data have been poorly reported in previous cost studies in AD, our results showed the importance of being able to understand the reasons for missing data from prospective studies (including why patients discontinue from studies) and how gathering informative data about study discontinuation can lead to better tailoring of methods for applying costs. Furthermore, we put these findings into context and demonstrated that the method of quantification and costing of informal caregiver time has the biggest impact on cost estimates, way above that of the different methods for handling missing data. Having a nearly complete set of baseline cost data for the large patient sample in the GERAS study allowed us to simulate costs based on real-life data, which have much greater variability and distribution than purely theoretical data.
Also, it allowed us to compare the simulated costs with the 'true' costs used to simulate the missing data, thereby providing a measure of the performance (bias, accuracy and coverage) of the different imputation methods. Our findings, therefore, extend previous analyses of missing cost data based on simulations of purely theoretical datasets (e. g. Oostenbrink and Maiwenn  [6] ), which have a low level of bias because they do not include the randomness, variability and inconsistencies of real-world data. In our simulations, we explored how different imputation methods performed under the different mechanisms of missing data with increasing volumes of missing data. Our findings confirm those reported previously in the theoretical simulation study by Oostenbrink and Maiwenn  [6]. We showed that complete case analysis performed as well as MI MCMC for missing cost data assumed to be MCAR.
e. had high levels of bias) and this increased as the missing data level increased to 40 %. In reality, it is known that missing data occur for many different reasons, especially in studies involving elderly people  [27]. One-third of patients with AD dementia dropped out during the 18-month follow-up period of the GERAS study because they died, were institutionalised or were lost to follow-up for other reasons including unavailability of the caregiver. Thus, there were at least three distinct reasons for study discontinuation and, therefore, for not having cost data. In many cost studies, death and institutionalisation are used as end % bias was calculated as ((estimated-actual)/actual cost × 100), where actual cost was the mean cost for the complete sample f Combination Scenario A: patients lost to follow-up (data MCAR) had costs imputed using group means method, patients institutionalised (data MAR) were imputed using MI MCMC method (including factors MMSE, ADCS-ADL and caregiver time), and patients who died (data MAR) had costs imputed using the MI MCMC method (including factors MMSE, patient age and ADCS-ADL) g Combination Scenario B: same imputation methods as Combination Scenario A, but a fixed cost (€2940 per month) was used for patients who were institutionalised (data MNAR) points rather than as missing data, with cost set to zero after death and a fixed cost for institutionalisation.
For this analysis, however, we used death and institutionalisation as reasons for having missing data, which enabled the simulation of different assumptions about missing data mechanisms. By creating datasets that included actual volume and patterns of reasons for discontinuation, we showed that a single imputation method, MI MCMC, was acceptable when the data were MCAR and/or MAR (as in the GERAS-1 dataset). However, when some of the data were MNAR (as in the GERAS-2 dataset) and when using a combination of imputation methods, Scenario B performed better than the individual approaches because it mirrored more closely how the missing data were generated. Combination Scenario A performed better on the GERAS-1 dataset but was similar to the MI methods. Our analyses showed that it is important to understand the reasons why patients discontinue from a study and the reasons for missing data because it can inform us about the most appropriate imputation methods to use to reduce the biases due to missing data.
Using a combined approach for missing data reasons (MCAR, MAR and MNAR) provides the opportunity to minimise the impact of data that are MNAR and the ineffectiveness of imputation methods to deal with this. For example, if 30 % of data are missing overall but only 10 % of these are MNAR, then using an imputation method that assumes the data are MAR will produce less bias than when the proportion of MNAR is 20 %. Kaambwa et al. [28]  came to a similar conclusion in their exploration of how different methods for analysing missing data from an observational study were influenced by using extra information obtained during data collection about why data were missing. Thus, reasons for missingness should be collected during prospective studies in order to be informative in cost analyses. This is particularly important for clinical trials, when reasons for missing data may differ depending on the type of treatments administered.
This would be applicable not just for AD, but also for other chronic diseases, especially those where a caregiver is also required. We ran further simulations to consider the impact of unmeasured confounders (see Additional file 2). For imputation methods that relied on knowing which factors were associated with both missing data and costs, we showed that performance was affected if this factor was excluded in the imputation method (as discussed by Spratt et al. [29] ). We found that use of the correct combined method could reduce this effect. In our analyses for imputing missing data, we assumed that the cost data were normally distributed. This is generally not a problem for large datasets like GERAS, where you can assume the central limit theorem, which states that as a dataset gets larger you can assume normality even if the data have a skewed distribution  [30].
However, bias due to missing data generally increases with smaller datasets. The MI method used in our analysis assumes normality. However, if working on smaller datasets, MI methods that do not assume that the data are normally distributed (e. g. MI predictive mean matching regression [PMMR] and MI propensity score) should be considered. As part of our sensitivity analysis (see Additional file 2), MI PMMR performed better than the MI regression method as the volume of missing data increased, for both relative bias and estimating the SE. The assumption of normality for the imputation of missing data, however, does not negate the requirement to understand the distributional properties of the cost data (with imputed values) when conducting further statistical analysis. To gain a better understanding of the relative impact on total societal cost estimates of using different imputation methods for dealing with missing cost data, we estimated total societal costs using different methods for calculating informal care costs.
Simulation datasets can be generated in many different ways using different factors. However, if a different set of factors is used to create the dataset, this may impact on the outcomes. For example, cost was used to generate the MNAR dataset. Also, some of the imputation methods used MMSE and ADCS-ADL scores as factors in the model, and these factors are associated with costs, such that costs increase as cognitive and functional ability scores worsen  [4, 8, 9, 14-16, 18, 32]. We applied a simple mechanism to generate missing cost data based on only a small number of patient characteristics. In reality, however, missing cost data are likely to be influenced by multiple patient characteristics. We have not explored if these methods are affected by the number of patient factors that determine the missingness of the data.
Conclusions Missing data is a complex issue when estimating the cost of illness, as exemplified in this analysis from the GERAS study of patients with AD dementia. First, the bias caused by missing data can be reduced by a better understanding of the causes of the missing data. Where there are different reasons for the missing data, consideration should be given to the use of different imputation methods. The use of single imputation techniques can be improved by using the missingness information to tailor imputation rules, resulting in more accurate cost estimates. Second, in the context of other assumptions made for estimating costs of AD (with missing data similar to the GERAS profile), then methods for costing informal caregiver time have a much larger impact on the cost estimates compared with assumptions made on which imputation method to use.
Summary statistics of simulations a with missing data pattern reflecting GERAS study data at 18 months b Numbers in bold text show the imputation method(s) that perform the best (lowest bias) for each of the two datasets (GERAS-1 and GERAS-2) Abbreviations: CP coverage probability, MAR missing at random, MCAR missing completely at random, MCMC Markov Chain Monte Carlo, MI multiple imputation, MNAR missing not at random, SEE standard error estimate, SSE sampling standard error.
Acknowledgements The paper was drafted by ML, IB, KL and PS who contributed equally. The authors thank all the patients with cancer and cancer professionals who have given us advice and inspiration to pursue this project and its goals. We also warmly thank the  Society for Translational Oncology (STO)  for its stewardship of the ECC initiative without which it would not have been possible and we would particularly thank  Ann Murphy  for her support and guidance. Dr Martin J Murphy , Jr is a coauthor of this and the previous document and was the convenor and fundraiser for the initial ECC meetings and the support of  STO  for ongoing work. Without his input this initiative would not have happened. The original version of the  European Cancer Patient's Bill of Rights  was published in The Oncologist.
Data Our research analyzes the nature of synergy gains arising from the strategic acquisition process and their specific contributions to bidder value creation in a French context. The study focus also on the determinants of each component of long-term synergy gains from the standpoint of the bidder. Data on takeovers were collected from the "Autorité des Marchés Financiers" (AMF) database. Our study covers the 1999-2011 period with an initial sample of 197 takeovers: cash offers and stock swap acquisitions. We excluded non listed bidding firms and takeovers for which the AMF database did not provide information on the deals ("Note d'information"). We likewise excluded financial and banking companies (SIC codes between 6000 and 6999,  Barber and Lyon, 1996; Sharma and Ho, 2002)  insofar as they have different operating, financial and risk-based characteristics.
To begin addressing the knowledge gap of the provenance of imported feed, research linking ecology and agriculture focusing on providing national average estimates reflecting the state and value of biodiversity of national production will be valuable. Global trade data could then be a proxy for direct knowledge of the feed origin and the new national averages used as default values. This would provide new evidence to raise awareness among decisionmakers, and foster collaboration between actors of the agricultural supply chain on the importance of tracking the exact location of activities. Data. Many available regional-scale ecological and agricultural datasets are not used by LCA methods' developers and could lead to three main types of improvements. First, they could be used as local information to increase the spatial differenciation of LCA models from global to regional or subregional scale.
Second, ecological data such as the Pan-European Common Bird Monitoring Scheme or the Mapping and Assessment of Ecosystem Services could address new taxa and new biodiversity dimensions in LCA. Third, ecological and agricultural datasets could be useful to include new land use classes and drivers of biodiversity changes that are specifically related to agriculture (e. g. with the Farm Accountancy Data Network providing a detailed description of agricultural inputs and practices). A key issue related to the use of ecological data in LCA method development will be sharing and ensuring data integrity, which is critical when data is transferred through interdisciplinary research and used in decision-making  (Norton 1998). Finally, a crucial challenge in using local and regional data is to provide a high geographical coverage of impacts, while maintaining high spatial resolution.
Data used in the simulations are as follows. Elasticities are taken from the literature  (Krichene, 2002; Gately and Huntington, 2002; Dahl and Sterner, 1991) : long-term OECD demand and income elasticities are set to -0. 7 and 0. 56, respectively, whereas non-OECD demand and income elasticities are set to -0. 4 and 0. 53, respectively. Non-OPEC's elasticity of oil supply is set to 0. 1  (Horn, 2004). For energy efficiency, we set η 1 to -0. 2 and -0. 1 for OECD and non-OECD respectively. These energy efficiency estimates have been chosen to correspond with estimates from  Medlock and Soligo (2001). GDP projections used are taken from IIASA 2009; they correspond to the medium growth estimates. Base year production and proven reserves estimates are taken from BP (2009); OPEC (2011), while, remaining resources are computed from USGS (2000) mean estimates of ultimately recoverable reserves.
Acknowledgments The authors acknowledge the financial support of the  NWO 's  ACTS Sustainable Hydrogen research program. They also wish to thank  Harrold Houba ,  Jordi Perdiguero ,  Larry Karp ,  Chuck F. Mason ,  Hiroaki Sakamato , and participants at the following gatherings: EMEE (14th to 15th -July -2011) SMU Dallas Texas workshop, the  Eureka  (4th -Feb -2012) seminar series of the VU Amsterdam, SURED (3rd to 8th -June -2012) Ascona conference, and the IEW ( 19th to 21st -June-2012), Capetown conference. Any arising errors are the sole responsibility of the authors.
This allowed an understanding of the relative importance of the criteria for the participants. The relative importance of the criteria was normalized as expressed in the Data Analysis section. Expressing frequencies and relative importance as percentages allowed a better comparison of the importance of the criteria in the two cases. Focus group discussions were recorded with a video recorder. This device was important for clarifying the moments when participants designated the yam tubers for each of the activities during the discussions. We completed the focus group discussions with a sociodemographic questionnaire to keep a record of their characteristics.
Analysis of the focus group transcripts The focus group discussions were fully transcribed using video records and field notes. Coding and analysis of the transcripts was performed using the content analysis software NVivo10 (QSR International, Melbourne, Australia). The codes resulted from the original research question and hypothesis and from recurring themes that emerged during the focus group discussions. The data were coded according to 14 main themes that ranged from the appreciation of yams to questions of traceability. The themes and their definition are given in Table  3. Content analysis was performed on all themes. The "Purchasing and Consumption Criteria" theme contained 14 categories that related to the criteria that the participants selected in the questionnaire. Consumers' profiles were established following content analysis of the theme "Attitude toward the price of yam".
Transcripts coded under this theme related to participant opinions about the price of yam, their purchasing behavior, and strategies. For each profile identified, we examined the frequencies and relative importance of the purchasing and consumption criteria. We also proceeded to the content analysis of the themes "origin," "mode of production," and "traceability" to understand the heterogeneity in perception and interest in those themes. Additionally, queries were run using the "Matrix Coding Query" tool available in the software to examine the intersection between themes or between a theme and participant profiles. This allowed understanding a theme within differentiated categories.
Acknowledgments The authors thank  José Dubréas  for his much appreciated comments on the interview guide and on the earlier version of this paper and  Samuel Joseph  for his support in the organization of the focus group discussions. They also thank the consumers who took part in this study. The first author benefited from a PhD Grant co-financed by the  European Union (Fonds Social Européen) , the  Guadeloupe Regional Council , and the  INRA social science division. Experiments and data collection were part of the  AGROECOTROP  project co-financed by the  European Union  ( Fond Européen de Développement Régional ) and the  Guadeloupe Regional Council.
Introduction Current prescribing guidelines for the antipsychotic amisulpride are based largely on pharmacokinetic (PK) studies in young adults, and there is a relative absence of data on older patients, who are at greatest risk of developing adverse events. Methods This study aimed to develop a population PK model for amisulpride specifically in older people, by combining data from a richly sampled phase 1, single (50 mg) dose study in healthy older people (n = 20, 65-79 years), with a clinical dataset obtained during off label, low-dose (25-75 mg daily) amisulpride prescribing in older people with Alzheimer's disease (AD) (n = 25, 69-92 years), as part of an observational study. Results After introducing a scaling factor based on body weight, age accounted for 20 % of the inter-individual variability in drug clearance (CL), resulting in a 54 % difference in CL between those aged 65 and those aged 85 years, and higher blood concentrations in older patients.
To maximise the potential utility of this dataset to inform safer prescribing in AD, we aimed to use a population approach to establish the consistency and identify sources of variability in PK-PD relationships. This analysis represents the first stage of model development, with the following aims: 1. To develop a population PK model for amisulpride specifically for older people and AD, by combining the clinical dataset with published data from the single (50 mg) dose study 2. To investigate the contribution of physiological characteristics to inter-individual variability in PK parameters 3. To use model outputs to simulate and predict amisulpride dose-concentration relationships in people aged 65 years and over.