dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis design with the MadAnalysis 5 metalanguage From the Python console, the user is invited to describe its selection and configure MadAnalysis 5 through intuitive and human-understandable commands that inherit from the Python syntax. As an exhaustive review of this metalanguage is beyond the scope of this paper, we focus on a detailed illustrative example and refer to Ref. [1] for more information. We consider a simple analysis defined by the following commands: The first two commands (lines 0 and 1) enable the import of the event samples to be considered. We recall that MadAnalysis 5 can read samples provided in the StdHep, HepMc, Lhe, Lhco or Root format, while compressed files can be used if the Zlib package is available. The next two lines (lines 2 and 3) indicate how to use the interface to the FastJet program and select the k T algorithm for jet reconstruction (see also  Section 4).
The computation of two differential distributions, namely the missing transverse energy and the muon-pair invariant mass spectra, are then requested after enforcing the selection of muons with a transverse momentum of at least 25 GeV (lines 4 to 7). The next command (line 8) requires MadAnalysis 5 to save the reconstructed events in the Lhco format, the Root and Lhe formats being also available. The final command (line 9) requests the program to operate. A C++ code corresponding to the analysis is generated and executed. The results (histograms and selection efficiencies) are collected by a report generator based on Root (other graphical components will be available in the next release of MadAnalysis 5) and further exported into Html and L A T E X reports.
Detector fast-simulation packages The MadAnalysis 5 package offers the option to install, configure and launch the fast simulation of a detector response on simulated events given as input in order to mimic instrumental and experimental effects and increase the realism of the simulation. Three possible choice are available, a very simple (but often sufficient for, e. g. , generator-level based phenomenology) and fast simulation based on FastJet, and two more sophisticated and slower simulations based on Delphes 3. • MadAnalysis 5 is shipped with its own detector simulation based on FastJet. All the jet-clustering algorithms included in FastJet can be applied to hadron-level events, and the user can further switch on several detector effects like reconstruction efficiencies, resolution, misidentification rates, etc.
Recasting existing LHC analyses with the MadAnalysis 5 expert mode The expert mode of the program offers the possibility to write an analysis directly in C++, using the MadAnalysis 5 framework. It is developer-friendly, and, in addition, a large collection of methods dedicated to common high-energy physics issues are available, as well as various services allowing one, for instance, to produce cut-flow charts and/or histograms. A detailed manual can be found in Ref. [2]. With version 1. 1. 10 onwards, one can also recast any cut-based ATLAS and CMS analysis possibly containing multiple subanalyses or regions. This allows reinterpretation of experimental results under the approximation (compared to a real experimental setup) of using a fast, simple, detector simulation software. Our framework offers hence a way to work out the implications of the LHC for any new physics model, derive limits in a realistic-enough manner, point out possible loopholes in the current searches and help to design future analyses.
It could also be used to improve the content and realism of fast detector simulation packages. The adopted strategy consists in first applying the Delphes-MA5tune fast detector simulation on signal events stored under the StdHep or HepMc format. Secondly, a reimplementation of a public ATLAS or CMS analysis is executed to derive the number of selected signal events from the input samples. A Python script finally extracts limits by confronting the numbers associated with the more sensitive signal region to the Standard Model expectation provided in the experimental publication. In the case where a user would need a more sophisticated procedure, he/she is welcome to extract directly the limits from  the MadAnalysis output independently. Since the validation of reimplemented analyses can sometimes be complicated (see, e.
Example of recast analysis A recasting of the CMS-SUS-12-012 analysis  [17]  has been recently performed, the validated MadAnalysis 5 code being available in Ref. [18]. This validation has been achieved by   3 , where we present distributions for the jet multiplicity and the missing transverse hadronic energy / H T in the case of the T2qq simplified model  [19]. We confront MA5 predictions to CMS official results and observe a good agreement.
Summary The public package MadAnalysis 5 offers a simple and efficient way to design and recast LHC analyses. The user can either benefit from its Python console and dedicated metalanguage to implement his/her analysis, or make use of the expert mode of the program which provides a flexible developer-friendly C++ environment. We have also discussed detector simulation within the MadAnalysis 5 framework, presenting the Delphes-MA5tune simulation of a detector response, and introduced our recently initiated database of reimplementations of LHC analyses.
Acknowledgments We thank the organizers for setting up this nice conference. This work has been partially supported by the  French ANR  projects  12-JS05-002-01 BATS@LHC  and  12-BS05-0006 DMAstroLHC , by the  Theory-LHC France initiative of the CNRS/IN2P3  and by the  Investissement d'avenir, Labex ENIGMASS.
We present a new SAS macro %pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks. Our macro first modifies the input data set appropriately and then applies SAS's standard Cox regression procedure, PROC PHREG, using weights and counting-process style of specifying survival times to the modified data set. The modified data set can also be used to estimate cumulative incidence curves for the event of interest. The application of PROC PHREG has several advantages, e. g. , it directly enables the user to apply the Firth correction, which has been proposed as a solution to the problem of undefined (infinite) maximum likelihood estimates in Cox regression, frequently encountered in small sample analyses. Deviation from proportional subdistribution hazards can be detected by both inspecting Schoenfeld-type residuals and testing correlation of these residuals with time, or by including interactions of covariates with functions of time.
We illustrate application of these extended methods for competing risk regression using our macro, which is freely available at: http://cemsiis. meduniwien. ac. at/en/kb/science-research/software/statistical-software/ pshreg, by means of analysis of a real chronic kidney disease study. We discuss differences in features and capabilities of %pshreg and the recent (January 2014) SAS PROC PHREG implementation of proportional subdistribution hazards modelling.
Differences between these two approaches are discussed, e. g. , in Wolbers et al. [5]  and Andersen et al. [6]. Here we focus on the latter approach. Programs for fitting a Fine-Gray regression model are available in R (e. g. , package cmprsk by Gray  [7] ) and STATA (stcrreg command by StataCorp. [8] ). Waltoft  [9]  describes a SAS macro for cumulative incidence curve estimation via Poisson regression. Zhang and Zhang  [10]  provide a SAS macro for the special case of computing adjusted cumulative incidence curves for two treatment groups. However, a publicly available and fairly general implementation of the Fine-Gray regression model in SAS has yet been missing. Our SAS macro %pshreg was written to fill this gap. The macro modifies an input data set by separating follow-up periods of patients with competing events into several disjoint sub-periods with declining weights, following the suggestions in Fine and Gray  [4]  and Geskus  [11].
Methods of inference in proportional subdistribution hazards models Geskus  [11]  discusses the use of model-based variance estimators instead of the robust ones proposed by Fine and Gray  [4]  in PSH models, and argues that, since subjects who experience the event type 1 are given weights of 1, and each subject appears no more than once in each risk set, the use of model-based inference, i. e. , by the model-based variance or by likelihood-based methods, is asymptotically correct. Hence, both the model-based and the robust estimator of variance can be used, as in an unweighted Cox regression analysis. Both options for inference are implemented in PROC PHREG and are therefore also available in our macro. In agreement with Gray's cmprsk R's package  [7]  we use the robust covariance by default.
Syntax The most important parameters available in %pshreg are described in Table  2. Further options implemented in the macro can be used to control the interpretation of the value of the status indicator, to specify commands which are directly passed to PROC PHREG or to request subgroup-dependent estimators of the censoring distribution. The latter option may be useful if there are subgroups with different follow-up schemes, and will cause subgroup dependent estimation of the weights that are used for subjects with competing events. This option is similar to the cengroup option in the R package cmprsk. All options are described and their use is exemplified in the User's Guide of the macro, which is available at http://cemsiis. meduniwien. ac. aten/kb/science-researchsoftware/statistical-software/ pshreg/.
SAS code generation If action=code, then SAS code will be written into the SAS Log window. This SAS code can be copied to the Editor window and submitted to estimate the Fine-Gray model. In a typical analysis situation, not only one but several multivariable models will be estimated, e. g. , for checking nonlinear effects, interactions or confounding. It is not necessary to repeat the macro call in this case; once the modified data set is created, the user can apply PROC PHREG with different variable lists etc. in the same manner as shown in the example code of the SAS Log. In PSH analyses of large data sets, creation of the modified data set is often the most time-consuming part of analysis. Computing time can be considerably reduced in such analyses if the modified data set is created only once by a single macro call.
Computational details The macro code is structured as follows: Step 1. Create a summary of all macro options in form of a SAS data set and send the data set to the current output destination. Step 2. Estimate Ĝ(t) (cf. Eq. (  3 )), which serves as denominator of the time-dependent weights (cf. Eq. (  2 )) at event times t (1) ,. , t (r) , using PROC LIFETEST. Step 3. Split the data set into two subsets: subset 1, consisting of all subjects with event indicators ε i = 0 or ε i = 1, and subset 2, consisting of all subjects with ε i = 2. Step 3. 1. For each subject in subset 1, create a single time interval, with a start time of 0. Set stop time to t i , and the censoring indicator censcrr to ε i. Create a weight variable weights , which is constantly 1. Step 3. 2. For each subject in subset 2, create the first time interval by setting start =0, stop =t i , and censcrr =0.
Subsequent time intervals and corresponding weights are efficiently created using PROC SQL statements: • Look up the weight denominator of subject i as Ĝ(t i ). • Extract all event times t (j) greater than t i. • For each of these event times, compute the subject-and time-specific weights variable as w i (t (j) ) = Ĝ(t (j) )/ Ĝ(t i ), and create the episodes in counting process formulation as described in Table  1 , setting censcrr =0. Step 4. Merge subset 1 with subset 2. Step 5. If requested by action=estimate, invoke PROC PHREG using the modified data set; otherwise (action=code), write the SAS statements of the PROC PHREG step into the Log window. %pshreg does not do any statistical calculations besides calling PROC LIFETEST to compute survival probabilities in order to generate the time-dependent weights.
Example with default settings In this section we illustrate an application of our macro %pshreg by analyzing the CKD data set described before. The primary aim of the analysis is to investigate the effects of several covariates on the cumulative incidence of dialysis in patients with chronic kidney disease. The variable Time contains the time span from diagnosis to the occurrence of the first event (dialysis or death) or end of follow-up in months. The variable status is the corresponding status indicator, it specifies whether a patient reached end stage renal disease and was started on dialysis (status=1), died (status=2) or experienced none of these events up to the last follow-up visit (status=0). Prognostic factors of interest are age in decades, osmolarity, creatinine clearance, proteinuria, beta-blocker, diuretic therapies and type of underlying renal disease (PKD).
Their distribution is summarized in Table  3. Note that for further analyses, urine osmolarity, proteinuria and creatinine clearance were log 2 transformed. Thus, their subdistribution hazard ratios refer to each doubling of their values. To generate a data set dat crr prepared for PSH regression analysis using PROC PHREG, the following code can be submitted: %pshreg(action=code, data=CKDstudy, time=Time, cens=status, varlist=age logUosm logProt logCCR pkd bblock diur, out=dat crr); The Output window will show a summary of the macro options, and a frequency of the status indicator: The PSHREG macro: summary of macro options   The Log window displays the following SAS statements, which may be submitted to estimate the PSH model, or which may be further modified to include a different set of variables or change options controlling the computation or output of PROC PHREG: PROC PHREG DATA=dat crr covs(aggregate); MODEL ( start , stop )* censcrr (0)=age logUosm logProt logCCR pkd bblocker diur; ID id ; WEIGHT weight ; RUN; By default, %pshreg will request a robust covariance matrix by specifying covs(aggregate) in the first statement, proposed by Fine and Gray  [4].
In this way, subjects can be traced in the output data set. The ID statement is important for the purpose of computing the robust covariance matrix (recall its construction via the change in regression coefficients D i caused by omitting each subject i in turn), and to identify outlying or influential observations. By copying the SAS code from the Log window into the Editor window, and submitting the code, the PSH model for time to dialysis is actually fitted. Part of the output of PROC PHREG is shown below: The PHREG Procedure The standard errors, 2 statistics and P-values are based on the robust sandwich covariance matrix. The column labelled StdErr Ratio contains the ratio of the robust to the modelbased standard error. In case of model misspecification, model-based standard errors may understate the true variability of the regression coefficients ˆ if the model is not specified correctly, while robust standard errors are still appropriate  [16].
435 fold the subdistribution hazard estimated for patients with pkd=0. Although in our example data set the cause-specific hazard ratio of pkd is very similar to its subdistribution hazard ratio, this is not generally the case, as the two quantities have different interpretation  [5]. For graphical visualization of the effect of osmolarity on the cumulative incidence of initiation of dialysis, one may create predicted CIF for fictive patients with different baseline urine osmolarities. Here we exemplify the necessary SAS statements to estimate and display these CIFs, using the output data set of the macro. The CIFs should be estimated for three (fictive) patients with baseline urine osmolarities of 315, 510 or 780 mOsmol/L (corresponding to the 10th, 50th, and 90th percentiles) and average values for all other covariates.
We assume that the covariate values are saved in the data set datameans (Fig. 3 ). (The values given for logUosm are log 2 (315), log 2 (510), and log 2 (780). ) First, we request PROC PHREG to compute estimated 'survival' curves for the three fictive patients, by submitting the BASELINE statement. Note that the macro does not need to be invoked again, as the modified data set dat crr is still in the working library: PROC PHREG DATA=dat crr covs(aggregate); MODEL ( start , stop )* censcrr (0)=age logUosm logProt logCCR pkd bblock diur/rl; WEIGHT weight ; BASELINE out=cuminccurves covariates=datameans survival= surv ; RUN; The output data set cuminccurves contains, for the three fictive patients, 'survival' curves estimated from the weighted Cox regression analysis. These three estimated 'survival' curves can be simply transformed into CIFs:.
DATA cuminccurves; SET cuminccurves; cuminc=1 -surv ; RUN; Finally, the CIFs are plotted: SYMBOL1 v=none i=steplj line=1 c=blue width=2; SYMBOL2 v=none i=steplj line=1 c=black width=2; SYMBOL3 v=none i=steplj line=1 c=green width=2; AXIS1 order=0 to 0. 40 by 0. 05 label=(angle=90 'Cumulative incidence of dialysis'); AXIS2 order=0 to 8 by 1 value=('0' '1' '2' '3' '4' '5' '6' '7' '8') label=('Time in years'); LEGEND1 value=('315' '510' '780') label=('Osmolarity'); PROC GPLOT DATA=cuminccurves; PLOT cuminc* stop =logUosm/vaxis=axis1 haxis=axis2 legend=legend1; RUN; As visualised in Fig. 4 , patients with increased osmolarity have a higher predicted cumulative dialysis incidence probability than patients with lower osmolarity.
An example with time-dependent effects The following code can be used to obtain weighted Schoenfeld-type residuals for the PSH model: The third line of the code shown above (the OUTPUT statement) creates a new data set, schoenfeld data, containing the weighted Schoenfeld-type residuals for all variables in the model and all event time points. In the code below, we merge the data set of the Schoenfeld-type residuals with the data set estimates, created by PROC PHREG above, which contains the parameter estimates. For merging, it is necessary to specify a primary key variable. We can make use of the constant by , which is automatically generated by the macro, and The output of PROC CORR shown below indicates some issues with the proportionality assumption for the variable logCCR: the correlation coefficient of the weighted Schoenfeld-type residuals with log of time is 0.
Concluding remarks So far, data analysists working with SAS had to switch to a different statistical programming environment such as R or STATA if they needed a PSH analysis. Although using multiple environments for an analysis project may have advantages in terms of flexibility, it is often inconvenient, as workflows cannot be saved as a single program file, and thus are harder to trace in case of revisions. The new macro enables a workflow that resides entirely in the SAS environment, using as the workhorse procedure the well-validated and documented PROC PHREG. We have compared the results obtained from our macro with those obtained by R's cmprsk package for the data sets presented here as well as for some other data sets, and could not find any relevant differences; the maximum difference in estimated parameter estimates was negligible if the time scale was truly continuous.
However, with the possibility of tied event and censoring times, there might be slight differences as in our macro we clearly interpret censoring times which are tied with event times as occurring after event times. It is not fully documented how such ties are handled in cmprsk. For fitting standard proportional subdistribution hazards models, our SAS macro offers the same functionality as the crr function of the R package cmprsk. Moreover, we have extended the Fine-Gray model and its existing implementations in R and STATA by providing a bias correction in small samples, which effectively eliminates the occurrence of infinite parameter estimates. In addition, PROC PHREG enables the analyst to make use of several additional built-in features, such as Bayesian analysis, model assessment via martingale residuals, inclusion of frailty effects, estimation of effect contrasts, etc.
; all of which not yet explored in context of the Fine-Gray model. It is also possible to apply nonlinear estimation techniques in combination with our macro, such as restricted cubic splines  [21, 22]  or fractional polynomials  [23]. Very recently, a new SAS version 9. 4 (including SAS/STAT version 13. 1) has been released in which the Fine-Gray model has been made directly available in PROC PHREG, by specifying the code of the event of interest in a new option EVENTCODE of the MODEL statement. All other codes which are not contained in the list of censoring values are then treated as competing event codes. We have compared the functionality of this new option with our macro by re-analyzing our examples. Even with the new EVENTCODE option, it is not possible to: • apply variable selection (e.
g. , backward elimination), • compute Schoenfeld-type residuals, • apply the Firth correction or compute profile-likelihood based confidence intervals, • use the ASSESS statement for assessing model assumptions using martingale residuals, • include frailty effects. All these options are possible with %pshreg as it first modifies the input data set which can then be treated as any other survival data set, making full use of the functionality of PROC PHREG. A limitation of our current implementation of %pshreg (and of the new SAS 9. 4 implementation of the Fine-Gray model) is that it does not allow for left-truncation or interval-censoring. Similarly, the macro and the SAS 9. 4 implementation cannot handle time-varying predictors implemented via multiple records of follow-up time (counting process style input) per subject  [24, 25].
Acknowledgments This work was partially funded by  European Commission  grant  HEALTH-F2-2009-241544. We acknowledge helpful remarks on our macro by  Lovedeep Gondara ,  British Columbia Cancer Agency, Vancouver, BC ,  Canada , and many other users who suggested useful modifications of earlier versions of our macro. We also thank three anonymous reviewers and the associated editor for many suggestions that helped to improve an earlier version of this manuscript. r e f e r e n c e s.
Speech data were collected with a sampling frequency of 8 kHz and sent to a central server for the feature extraction process. The reliability of the estimated features was not indicated, and it is not clear whether the overall process is automatic. On the other hand, a complex feature extraction and classification procedure is discussed, and preliminary results indicate good classification of hypomania, while lower performance was obtained with depression. In this study, we explore the possibility of using a smartphone to collect and process speech data for the estimation of features related to the speech F 0 and its variability measured over voiced segments. The features are estimated locally, on the smartphone, exploiting an algorithm based on a spectral matching approach  [24] , and sent to a server, thus guaranteeing subjects' privacy.
Application The Android application is integrated in a software tool of the PSYCHE Platform System  [8]. This system is developed to monitor the mood states of subjects suffering from bipolar disorders. The system allows for compiling a mood agenda, answering daily self-administered questionnaires, storing data coming from a physiological signal monitoring system and acquiring and analyzing speech data  [25]. In Figure  1 , two screenshots of the application main menu and an example of a mood agenda are shown. The application is also able to record speech data using two structured tasks. In the first task, the subject is asked to read text, while in the second, to comment on some pictures. Both text and images were selected by the clinicians participating in the study. The text is the Declaration of Human Rights that is supposed not to elicit any strong emotional reaction.