dataesr/scientific-paragraphs-categorization huggingface dataset:
Background Pharmacoepidemiology aims at assessing the risk and benefit of pharmaceuticals in real-world populations  [1]. Computerized medical databases are increasingly being used for real life post-marketing observational studies  [2] , and have several advantages: (i) the potential for studying a very large sample size, thereby allowing for study of rare events or exposures; (ii) the availability of data for older adults, children, patients with low resources, and nursing-home residents, who are most often under-represented in clinical trials  [3] ; (iii) the inclusion of off-label prescriptions; and (iv) data are prospectively collected  [4]. However, data are usually collected for purposes other than research (administrative or healthcare management), so the databases frequently lack information on some potential confounding factors (e.
Several systematic reviews have examined the use and reporting of case-crossover designs only  [20] , selfcontrolled case series only  [21]  or self-controlled studies in general  [6]. Recently, we showed that self-controlled designs are rarely used in pharmacoepidemiology  [22]. We did not find any recent study focusing on the appropriate use of self-controlled designs in pharmacoepidemiology involving electronic healthcare databases, with regard to major and minor validity assumptions. Here we aimed to assess the appropriate use of selfcontrolled designs, in terms of their validity assumptions in pharmacoepidemiology involving electronic healthcare databases and to update the Nordmann et al. review  [6]. To achieve this goal, we performed a systematic review to describe whether the required characteristics for the use of the designs were fulfilled and adequately reported in published articles.
Search For the systematic review, we searched MEDLINE via Pubmed for English and French articles of selfcontrolled studies (case-crossover or self-controlled case series) involving electronic healthcare databases that were published from January 1, 2011 to December 31, 2014. The search keywords are reported in Additional file 1: S1. Papers reporting the study of safety or efficacy of a medical product that used a self-controlled design and involving electronic healthcare databases were included. We excluded articles of studies not examining a drug or device, methodological articles (design development or study protocol), drug utilization or prescription studies (drug utilization or drug prescription patterns), descriptive or case-series studies (prevalence or incidence of disease), clinical practice evaluation (quality improvement in clinical practice), and articles not describing a self-controlled design or not involving electronic healthcare databases.
Data collection The data were collected by three readers who used a standardized extraction form (Additional file 1: S2) based on the STROBE recommendations  [23]  and on previous systematic reviews of designs in pharmacoepidemiology  [6, 22]. The collected data focused on the characteristics of exposures and events and on the study designs. The "exposure characteristics" section contained information about the type, prevalence and characteristic of the exposure (i. e. one shot, such as vaccines; transient for a few days, such as antimicrobial therapy or analgesics; intermittent on a specified frequency, such as chemotherapy; or sustained, such as long term use of hypoglycaemic agents). In several cases, we reclassified the sustained exposures as transient, when the event of interest was explicitly hypothesised to occur shortly after the drug initiation (product initiation for incident user designs), or intermittent, when we considered that a high opportunity of switch from exposed to unexposed status (or vice versa) can be assumed over the observation period (especially when the risk period was equal and non-inferior 1 to the prescription period).
Quality of reporting We examined whether the recommendations from Nordmann et al. [6]  were applied for quality of reporting. These recommendations include the reporting of the fulfilment of key assumptions (whether the setting was valid for the design implementation in terms of major and minor assumptions); the definition (number and duration) of the case period (for case-crossover) or risk period (for self-controlled case series) and the control periods; the appropriate statistical model (i. e. conditional logistic regression model or conditional Poisson regression model for case-crossover  [29, 30] , and conditional Poisson regression model, Cox's stratified proportional hazards model or conditional logistic regression model for self-controlled case series  [30] ), an a priori sample size calculation (or power calculation, because sample size cannot be chosen in healthcare database research); the appropriate effect estimator (i.
Discussion In this review of articles, we quantified the appropriate use of self-controlled designs in pharmacoepidemiology involving electronic healthcare databases in terms of major and minor validity assumptions for the use of the designs. We focused on studies involving medical databases, in which self-controlled designs are particularly useful to adjust for time-invariant confounders that may not be collected. Self-controlled designs were not appropriately used in 34% and 13% of the articles we reviewed that described a case-crossover or self-controlled case series design, respectively. We encourage better use of these designs for situations in which major validity assumptions are fulfilled (i. e. , for which they are recommended), accounting for situations for which the design can be adapted.
Our study updated the Nordmann et al. review  [6]  and is the first systematic review exploring the appropriate use of self-controlled designs in pharmacoepidemiology involving electronic healthcare databases, in terms of major and minor validity assumptions, in accordance with recent recommendations. The fulfilment of major assumptions is the minimum requirement for selfcontrolled designs to be valid, as they can be superior to designs with comparison groups in such situations (more powered and less biased)  [5]. Moreover, recent recommendations state that the self-controlled designs should be preferred to designs with comparison groups in studies performed on healthcare databases, when key validity assumptions are fulfilled  [4]. For articles that did not fulfil all of the major validity assumptions, it was essentially due to the study of sustained exposure (e.
We did not explore this assumption the studies included in our review. The previous systematic review by Nordmann et al. reported the validity assumptions of self-controlled designs in pharmacoepidemiology between 1995 and 2010 (before the development of the previously cited recommendations)  [6]. The authors reported an inappropriate use of self-controlled designs: validity assumptions were not fulfilled for 76% of the articles describing a casecrossover design and 60% self-controlled case series. Concerning major assumptions, our review, which covered healthcare database studies published recently, shows that these data have improved. Moreover, major and minor validity assumptions were not distinguished in the Nordmann et al. review. Nevertheless, we found the same main reasons for the inappropriate use of these designs (i.
However, studies involving electronic healthcare databases usually have very large sample sizes and perhaps the sample size calculation is not needed in this context, because the sample size cannot be chosen. However post-hoc power calculation in the database study sample is important to decide which healthcare database should be used and to interpret the absence of a statistical association, especially in the context of a very rare event. Post-hoc power calculation indicates how easily an effect that is fixed a priori can be shown (accounting for the observed number of patients/cases and the observed variability). Even if confidence intervals (which represent how accurate the results are estimated) are reported, the power calculation is more related to the number of observed events, and is information that is easier for the reader to understand: in case of nonsignificant associations, power can be quite difficult to interpret on the basis of the sole confidence interval.
Moreover, the definition of abrupt versus insidious onset of the event is somewhat subjective, as is transient versus sustained exposure, or some minor assumptions for the selfcontrolled case series. We tried to limit this issue by consensus. Some strengths of this study are worth noting. We focused on studies involving medical databases because of a growing interest in the use of "big data" in healthcare research  [40]. We updated Nordmann et al. review  [6]  up to 2014, and to ensure that no indexing issue in PubMed can be suspected, we updated our literature search in July 2016.
Conclusion Self-controlled designs have many advantages, including their ability to inherently adjust for time-invariant factors, which is important when using electronic healthcare databases, where some confounding factors are usually not collected or nor available. We found that in terms of fulfilling the major assumptions of the designs, the designs for one-third of the case-crossover studies and less than one-fifth of self-controlled case series we reviewed were not appropriately used in these pharmacoepidemiology studies involving electronic healthcare databases. We encourage a better justification of the design validity in terms of major and minor assumptions in accordance with recommendations for their use and more accurate reporting of self-controlled case series. Addressing these issues will contribute to a wiser use of these self-controlled designs, which is advantageous for pharmacoepidemiology involving large healthcare databases.
Assume that E[ε|X] = 0 and V[ε|X] = C the covariance matrix of the residuals. Then GLS estimates β as (see  [2]  for a more detailed discussion): β = (X * CX) -1 X * C -1 y. (1. 4) We shall investigate this estimator in Section 7. (ii) Generalized methods of moments (GMM): Suppose one wants to calibrate the parameters Θ of a model on some data set. The idea is to compute the empirical average of a set of k functions (generalized moments) of the data, which should all be zero for the correct values of the parameters, Θ = Θ 0. The distance to zero is measured using the covariance of these functions. A precise measurement of this k × k covariance matrix increases the efficiency of the GMM -see  [3]. Note that GLS is a special form of GMM. (iii) Classification  [4] : Suppose that we want to classify the variables y between two Gaussian populations with different mean µ 1 and µ 2 , priors π 1 and π 2 , but same covariance matrix C.
125. Clearly, effects induced by a non zero value of q are expected to be highly relevant in many applications. 1. 2. Historical survey. The rapid growth of RMT (Random Matrix Theory) in the last two decades is due both to the increasing complexity of the data in many fields of science (the "Big Data" phenomenon) and to many new, groundbreaking mathematical results that challenge classical results of statistics. In particular, RMT has allowed a very precise study of large sample covariance matrices and also the design of estimators that are consistent in the large dimensional limit (LDL) presented above. The aim of this review is to provide the reader an introduction to the different RMT inspired techniques that allow one to investigate problems of high-dimensional statistics, with the estimation of large covariance matrices as the main thread.
This property is in fact at the core of RMT which makes this theory particularly appealing. At the same time, some empirical evidence of the relevance of these results for sample covariance matrices weres provided in  [22, 23]  using financial data sets, which are known to be non-Gaussian  [24]. More precisely, these works suggested that most of the eigenvalues (the bulk ) of financial correlation matrices agrees, to a first approximation, with the null hypothesis C = I, while a finite number of "spikes" (outliers) reside outside of the bulk. This observation is the very essence of the spiked covariance matrix model named after the celebrated paper of Johnstone in 2001 with many applications in principal components analysis (PCA)  [25]. Indeed, the author showed another manifestation of universal properties of RMT, namely the Tracy-Widom distribution for the top bulk eigenvalues in the spiked covariance matrix  [26, 25].
Already at this stage, it is interesting to notice that the distribution (2. 1) can alternatively be rewritten in terms of the eigenvalues and eigenvectors of M as: P β (M)DM ∝ e -βN 2 N i=1 V (νi) N i<j |ν i -ν j | β N i=1 dν i dΩ , (2. 2) where the Vandermonde determinant ( |ν iν j | β )comes from the change of variables (from the M ij to the ν i and Ω ij ). This representation is extremely useful, as will be illustrated below. What kind of universal properties can be of interest in practice? Let us consider a standard problem in multivariate statistics. Suppose that we have a very large dataset with correlated variables. A common technique to deal with this large dataset is to reduce the dimension of the problem using for instance a principal component analysis (PCA), obtained by diagonalizing the covariance matrix of the different variables.
Eq. (3. 1) is then approximated by an average value over the whole sample data of size T , leading to the sample (or empirical ) covariance matrix estimator: E ij = 1 T (YY * ) ij = 1 T T τ =1 Y it Y jt. (3. 3) In the statistical literature, this estimator is known as Pearson estimator and in the RMT community, the resulting matrix sometimes referred to as the Wishart Ensemble. Whereas the Wigner Ensemble has been the subject of a large amount of studies in physics  [49] , results on the Wishart Ensemble mostly come from mathematics & statistics  [17, 55, 92] , telecommunication  [56]  or the financial/econophysics literature  [23, 28, 66] , although some work in the physics literature also exists  [93, 94, 95, 96]  -to cite a few. In what we call the "classical" statistical limit, i. e. T → ∞ with N fixed, the law of large numbers tells us that E converges to the true covariance C.
More specifically, we will show that Eq. (4. 4) exhibits yet again a self-averaging behavior in the large N limit, i. e. independent from the realization of E and Ẽ. We will moreover see that the overlaps (4. 4) significantly depart from the trivial null hypothesis as soon as the population C has a non-trivial structure. Hence, this suggests that we might be able to infer the correlation structure of very large databases using empirical quantities only. All these results have been obtained in the recent work  [124]  and we shall only give here the main steps. For the sake of clarity, we use the notations λ1 λ2. λN to denote the eigenvalues of Ẽ and by ũ1 , ũ2 ,. , ũN the associated eigenvectors. Note that we will again index the eigenvectors by their corresponding eigenvalues for convenience.
Roughly speaking, in the presence of a very large number of eigenvectors, one can discretize the Marčenko-Pastur equation and solve the inverse problem using either a parametric  [28]  or non-parametric approach  [32]. The last method concerns factors models, or structured covariance estimators, where one tries to explain the correlation matrix through a simplified model of the underlying structure of the data. This is a very popular approach in finance and economics, and we will see how RMT has allowed some recent progress. All these methods will be tested using real financial data in the next chapter. 7. 2. 1. Linear Shrinkage. We recall that the linear shrinkage is given by Ξ lin = α s E + (1 -α s )I N , α ∈ [0, 1]. (7. 49) As discussed in Chapter 5, this estimator has a long history in high-dimensional statistics  [15, 16]  as it provides a simple proof that the sample estimator E is inconsistent whenever N and T are both large.
, M at time t and the N × T matrix ε it is the idiosyncratic part of Y it (or the residual in Statistics), assumed to be of zero mean. The model  (7. 62)  in matrix form reads Y = βF + E, (7. 63) which is known as Generalized Linear Model  [142]. It is often assumed that the residuals are i. i. d. across i with t fixed (see e. g. [143]  for an application in Finance). It is not hard to see that the covariance matrix under the model (7. 62), is given by C = βΣ F β * + Σ ε (7. 64) where Σ F is the covariance matrix of size M × M of the factors F -which can be chosen, without loss of generality, to be proportional to the identity matrix -and Σ ε is the N × N covariance matrix of the residuals ε, which is simply the identity in the simplest framework. Within the linear decomposition (7. 62), we see that we have generically a number of parameters to estimate of order O(N M ) out of datasets of size O(N T ).
We see that the estimator (9. 2) can be rewritten as M ij (τ ) = T t=0 H it H jt , with E H it H it = δ tt (1 -α)α t , (9. 3) i. e. the variance of the random variables have an explicit time dependence. Another interesting way to generalize the Marčenko-Pastur framework concerns the distribution of the entries. An important assumption for the Marčenko-Pastur equation to be valid is that each entry Y it possesses a finite fourth moment. Again, this assumption may not be satisfied in real dataset, especially in finance  [102]. As alluded to in Section 3. 1. 3, a more robust estimate of the covariance matrix in then needed  [99]. Let us assume that we can rewrite the observations as Y it = σ t C 1/2 X it for any i ∈ [[1, N ]] and t ∈ [[1, T ]] , where σ t is a fluctuating global volatility that sets the overall scale of the returns, and X are IID Gaussian variables.
Acknowledgment We want to warmly thank all our collaborators on the different topics considered in this review, in particular  Romain Allez ,  Antti Knowles  and  Satya N. Majumdar. We also acknowledge insightful discussions with  Marc Abeille ,  Jean-Yves Audibert ,  Yanis Bahroun ,  Florent Benaych-Georges ,  Raphaël Benichou ,  Alexios Beveratos ,  Giulio Biroli ,  Rémy Chicheportiche ,  Benoit Collins ,  Romain Couillet ,  Noureddine El Karoui ,  Sandrine Péché ,  Adam Rej ,  Emmanuel Sérié ,  Guillaume Simon  and  Denis Ullmo.
Acknowledgements The authors thanks the anonymous referee for his/her valuable comments which helped to improve the presentation of the paper. The second author thanks Guillaume Carlier for useful conversations. The first author was partially supported by FIRB 2010 project RBFR10GHHH 002 "Geometria e topologia delle varietà in bassa dimensione". The second author was partially supported by the ANR GR-Analysis-Geometry. Most of this work was achieved during stays of the second author in the Department of Mathematics of the University of Pavia. He thanks the institution for its hospitality. 2 Area measure of F-convex sets.
Adherence to medications is an important indicator of the quality of medication management and impacts on health outcomes and cost-effectiveness of healthcare delivery. Electronic healthcare data (EHD) are increasingly used to estimate adherence in research and clinical practice, yet standardization and transparency of data processing are still a concern. Comprehensive and flexible open-source algorithms can facilitate the development of high-quality, consistent, and reproducible evidence in this field. Some EHD-based clinical decision support systems (CDSS) include visualization of medication histories, but this is rarely integrated in adherence analyses and not easily accessible for data exploration or implementation in new clinical settings. We introduce AdhereR, a package for the widely used opensource statistical environment R, designed to support researchers in computing EHD-based adherence estimates and in visualizing individual medication histories and adherence patterns.
Introduction Electronic healthcare data (EHD) are a main source of information on patients' adherence to medications for research and clinical practice  [1]. As adherence impacts considerably on health outcomes and cost-effectiveness of healthcare  [2] , EHD have the potential to support large-scale investigations of the prevalence and role of adherence in routine care, as well as provide the infrastructure for implementing adherence-enhancing programmes. Routinelycollected information on medication dispensing and/or prescribing is nowadays accessible in primary or secondary care electronic medical records (EMR), pharmacy dispensing databases, or health insurance claims systems. These data can be used to estimate retrospectively how patients used the recommended medication over specific intervals (to the extent that obtaining a medication supply led to medication use) without the intrusiveness and costs inherent to prospective data collection via self-report or electronic monitoring devices.
This taxonomy provides a general framework for adherence research irrespective of the type of data source (e. g. EHD, electronic monitoring, self-report), and therefore requires adaptation to the specific requirements of limitations of the accessible data. Yet, it is largely consistent with terms and definitions proposed by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) of initial medication adherence  [7] , adherence (compliance), and persistence  [8]. While these definitions represent a common starting point for adherence studies, their application to particular conditions and medications requires further consideration of specific clinical contexts  [9]. ISPOR also provides practical guidance on conducting, reporting and evaluating adherence studies using retrospective databases  [4].
The field would therefore benefit from open-source algorithms sufficiently comprehensive and flexible to support the whole data analysis process and allow transparent decision-making and data sharing. We developed AdhereR in order to provide this support for computing EHD-based adherence (implementation) and persistence estimates within the widely-used open-source environment R  [22]. As R has become the preferred statistical software in many research settings, our solution allows researchers to perform complete analyses in R using AdhereR, possibly in combination with other R packages, starting from importing raw data, through generating descriptive statistics, interactive exploratory plots, publication-quality figures, and up to modeling of relationships with available predictors or outcomes.
Terms, definitions and basic considerations AdhereR uses the ISPOR terms of 'adherence' (implementation in the ABC taxonomy) and 'persistence' to denote the two components of adherence to medications implemented. It is designed to process a single data source and assumes that the medication studied was prescribed to all patients selected for fixed dosing regimens for a period equal to or longer than the time period investigated and that at least one medication event was recorded in that period for each patient. Medication events are individual records of prescribing or dispensing a specific medication for a patient at a given date. A record needs to include a patient unique identifier, an event date, and a duration (the number of days this quantity would last if used as recommended). Duration may be already available or computed based on quantity (the number of doses prescribed or dispensed on that occasion) divided by daily dosage (the number of doses recommended to be taken daily).