dataesr/scientific-paragraphs-categorization huggingface dataset:
The direction and magnitude of the association between hospitalization and death among cases in the database will then be the result of combining the association due to this selection bias, the association due to a potential effect of hospitalization on mortality, and the association due to confounding. Hypothetical examples are shown in Tables  3 4 5. In these tables, the association in the population between hospitalization on day 8 (an arbitrarily chosen day) and death is negative; individuals hospitalized on day 8 (an arbitrarily chosen day) of symptoms have a lower probability of death than those who are not hospitalized on day 8 of symptoms. If we assume that this analysis has avoided survivorship bias by limiting analysis to cases still alive on day 8, then the population-level association would reflect a combination of the causal effect of hospitalization on day 8 on risk of death, and confounding by severity or other factors.
This population-level association is the same in Tables  3, 4 , and 5, but different probabilities are assumed for inclusion in the database, depending on whether an individual is hospitalized on day 8, dies, or both. Relative CFRs on the RR, OR, and RD scales for hospitalization on day 8 are calculated for each hypothetical example. The hypothetical data in these tables show that selection bias in such a circumstance may be either positive or negative on each of the three scales, depending on the specific probabilities of selection in each of the four states. Table  3  shows an example of negative bias on the RR, OR, and RD scales (overestimating the protective effect of hospitalization on day 8 expressed as a lower value of each relative risk measure). Table  4  shows an example of a positive bias on the RR and RD scales and a negative bias on the OR scale.
Selection bias in this setting occurs because the exposure and the outcome both affect the probability of inclusion in the database. Follow-up of a cohort of contacts ascertained before becoming cases could eliminate hospitalization and mortality as predictors of inclusion in the database, thus eliminating the form of selection bias we have discussed. It would provide an opportunity for , where S ij is the probability a case with exposure (hospitalization at day 8) i and outcome (mortality) j appears in the database. In this example, selection bias spuriously enhances the negative association between hospitalization on day 8 and death, on all scales: RR, OR, and RD. doi:10. 1371/journal. pntd. 0003846. t003 gathering data on severity and other predictors of exposure and outcome, which would facilitate control of confounding, though not guarantee to eliminate it.
Even for a well-understood disease like polio, it may be necessary to identify unusual demographic patterns of mortality in order to understand and respond effectively to an outbreak  [36]. Prognostic exercises such as these cannot suffer from confounding bias because no causal interpretation is attached to the conclusions. They can, however, suffer from selection bias. Returning to the Ebola context, one might wish to know whether pregnant women infected with Ebola are at greater risk of death from Ebola infection than other cases  [37] , for example, in order to give them greater supportive care. If the probability of entering the database depends on whether an Ebola patient is pregnant and on whether she ultimately dies of the infection, then the probability of death given pregnancy will likely differ in the database from the value in the population of direct interest for a clinical or public health decision maker.
If the goal of analysis is to inform public health decision makers on the value of efforts to prevent infection in pregnant women, then the population-wide CFR among pregnant women is the value of direct interest. If, on the other hand, the goal of analysis is to inform health care providers at a treatment center to make a better clinical decision based on an accurate prognosis of the patient presenting to them, the quantity of direct interest is the probability of death among pregnant women in the population they encounter-those admitted to the treatment center. This value, again, will differ from that in the database, which may (in our running example) have been enriched for individuals entered in the database because they died of the infection. It will also differ from that in the overall population.
Whatever one's view on this debate, it seems likely that some observational (non-randomized) studies of the effectiveness of particular therapies, or the comparative effectiveness of two or more therapeutic approaches will occur, whether for ethical reasons, logistical reasons, or both. Such studies-in which a key endpoint will be mortality-will be vulnerable to the sorts of biases described in this article, particularly in cases in which the true effect size of the treatment is limited. The biases described here should be kept in mind when evaluating the conclusions of such studies, and wherever possible, studies should be designed to minimize them. Small studies conducted using systematic approaches to enrollment and follow-up of patients may be more precise and less biased than studies with larger sample sizes that use databases collected for other reasons.
Meanwhile, emphasis on recording for each patient in a database the time, place, and circumstances (e. g. , hospital, clinic, funeral, contact tracing) under which the information is being gathered can substantially improve our ability to account for biases induced by a database with unplanned entry criteria. To reduce the impact of the biases identified on causal and (where applicable) prognostic inference, it appears desirable when possible to limit analysis to a subset of cases who have been followed prospectively since they became cases. These individuals might most likely be identified by forward contact tracing, in which cases are asked to name healthy individuals with whom they have had contact, and those individuals are followed to identify further infections. It has previously been noted that cases identified by contact tracing are more representative of cases in the general infected population than those identified because of symptoms, medical need, or death  [20, 41].
Use of such a sample does not guarantee to eliminate biases, as there may be residual confounding not adequately controlled in the analysis or subtler forms of selection bias (e. g. , differential loss to follow up within the sample)  [32] , but should significantly reduce them. We have emphasized the relevance of several biases to interpretation of datasets gathered in an emergency, such as the early phases of an emerging infection. While the downward bias in estimation of the CFR due to delayed reporting of deaths is most acute in rapidly growing epidemics, the other biases described may apply regardless of the overall trajectory of an epidemic, and thus may apply to endemic diseases as well as emerging ones. Nonetheless, due to the sense of urgency to gather data and scale-up a response simultaneously, datasets assembled during infectious disease outbreak or emergency settings are especially prone to include unplanned mixes of cases who enter the dataset for various reasons.
Key Learning Points • Datasets available at the onset of new epidemics of infectious diseases are often collected for reasons other than epidemiologic analysis of absolute and comparative casefatality risks (CFR), and estimates of such quantities based on these data may be subject to biases, the relative magnitudes of which are difficult to ascertain and vary by situation. • Major sources of bias affecting the estimation of absolute CFR are differences in severity between all cases and the subset of cases who enter the dataset, typically leading to inflated estimates of CFR, and more rapid reporting (less delay) in reporting cases than in reporting the deaths of those cases, typically leading to underestimates of CFR. • Biases affecting the causal interpretation of relative CFR (causal attribution of different CFR in different groups to a particular intervention in one group, e.
g. , hospitalization) may arise from survivorship bias, in which individuals who survive longer may be more likely to receive the intervention; from confounding, in which a common factor (e. g. , disease severity) affects the probability of both the intervention and mortality; and from selection bias, in which individuals are more or less likely to enter the dataset as a function of whether they receive the intervention and whether they have the outcome. • These biases may be severe enough to lead to qualitatively mistaken inferences about the severity of the infection or about the impact of interventions (such as hospitalization) on mortality, and may be particularly misleading when comparing, for example, the effect of hospitalization at different centers, given that cases hospitalized at different centers may enter the dataset for different reasons.
Research reported in this publication was supported by the  National Institute Of General Medical Sciences  of the  US National Institutes of Health  under Award Number  U54GM088558. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. It was also supported by the  Bill and Melinda Gates Foundation , the  European Union Seventh Framework Programme  [FP7/2007-2013] under Grant Agreement no278433-PREDEMICS and by Centre funding from the  UK Medical Research Council. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In practice, patient-reported outcomes are assessed through questionnaires composed of items that indirectly measure a latent variable which represents the concept of interest. Two theories exist for the analysis of the responses of patients to items. The models from the Classical Test Theory are based on a score that often sums the responses to the items. Another theory has gained importance in patient-reported outcomes area  [4] , the Item Response Theory (IRT) including models which link the probability of a given answer to an item with item parameters and the latent variable. IRT has shown advantages such as the management of missing data  [5] , the possibility to obtain an interval measure for the latent trait, the comparison of latent traits levels independently of the instrument, the management of possible floor and ceiling effects  [6, 7].
The power obtained with the Raschpower method assuming normal distribution were compared to reference power obtained from data simulated with a non-normal distribution (a beta distribution leading to U, L or J-shaped distributions). Simulation studies have shown that the powers of group effect obtained either from the Raschpower procedure or from the simulated datasets were close to each other. In conclusion, the Raschpower procedure seems robust to non-normality of the latent variable. The power determination using Raschpower in crosssectional studies depends on the expected values of the following parameters: the sample size in each group, the number of items, the group effect defined as the expected difference between the means of the latent trait of each group, the item parameters and the variance of the latent trait.
The Raschpower procedure for power estimation The Raschpower procedure provides an estimation of the power for the comparison of PRO data in two independent groups of patients when a Rasch family model is intended to be used for the analysis. This procedure is used at the planning stage and is based on a Wald test to detect a group effect. To perform the test of group effect, an estimate of the group effect γ and its standard error are required. Since no dataset exists during the planning stage, no estimate can be obtained from data. Hardouin et al. [16]  proposed to obtain a numerical estimation for the standard error of from an expected dataset of the patients' responses. In this procedure, a dataset of the patients' responses is first created conditionally on the planning expected values for the sample size in each group (N 0 and N 1 ), the group effect (γ ), the item difficulties δ j and the variance of the latent trait σ 2 θ.
All possible response patterns of the patients are determined. The associated probability and the expected frequency of each response pattern for each group are computed using the mixed Rasch model (eq. 1) given the planning expected values. The expected dataset is composed of all possible response patterns and their associated frequencies. Then, to estimate the standard error of the group effect, the expected dataset is analysed with a latent regression mixed Rasch model (eq. 2) where the item difficulties δ j and the variance of the latent trait σ 2 θ are set to the planning expected values. The Wald test of group effect is performed with the hypotheses H 0 : γ = 0 against H 1 : γ = 0 and the test statistic √ var( ) follows a standard normal distribution under H 0. The expected power of the test of the group effect, 1 -β, can be approximated by  [16] : 1 -β ≈ 1 - z 1-α/2 - γ v ar( γ ) (3) with γ assumed to take on positive values, z 1-α/2 be the quantile of the standard normal distribution and v ar γ estimated from the expected dataset.
Furthermore, to avoid ceiling and floor effects and ensure that the questionnaire was suitable for the population (not too specific nor too generic)  [17] , we decided to exclude cases where σ 2 δ j > 8 × σ 2 θ with σ 2 θ = {0. 25, 0. 5, 0. 75, 1, 1. 5, 2, 2. 5, 3, 4, 5, 6, 7, 8, 9}. The other parameters used at the planning stage could also vary: the sample size in each group (N 0 = N 1 = 50, 100, 200, 300, 500), the number of items (J = 3, 5, 7, 9, 11, 13, 15), the value of the group effect (γ = 0. 1, 0. 2, 0. 5, 0. 8). The draw of the parameters and the estimation of power using the Raschpower procedure for all combinations of parameters were performed with Stata software.
For longitudinal studies, the impact of a misspecification of the parameters will not only depend on the value of the variance of the latent variable σ 2 but also on the whole covariance matrix, i. e. on the variance of the latent variable at each measurement occasion and its correlation between measurement occasions. For questionnaires composed of polytomous items, this impact will depend on the number of items and also on the number of response categories of the items. A number of software programs or websites are useful for power analysis and sample size calculation. Some specialized programs (G*POWER, PASS, NQuery Advisor, PC-Size, PS) and some more general statistical programs (SAS, Stata, R) can provide power and sample size through the t-test based formula for the comparison of two normally distributed endpoints in two independent groups of patients.
Acknowledgments MB thanks financial support from  UNAM-DGAPA-PAPIIT  ( IA200714 ,  IN113013-3 ) and  CONACyT  ( 221341 ). JA's PhD research is done at the Posgrado en  Ciencias Biomédicas ,  Universidad Nacional Autónoma de México  ( Instituto de Ecología ). JA thanks the  PhD scholarship program  from  Consejo Nacional de Ciencia y Tecnología  and  Universidad Nacional Autónoma de México. The authors thank the three reviewers for their valuable comments and the CC3D team for their help and useful workshops.
Acknowledgments. H. I. thanks John Alexander Cruz Morales and Anton Mellit for helpful discussions. We thank referees for suggesting several improvements on the statement and the proof of Lemma 3. 8. This work is supported by the grants ANR-13-IS01-0001-01 and ANR-09-JCJC-0104-01 of the Agence nationale de la recherche and JSPS KAKENHI Grant Number 25400069, 23224002, 26610008.
Acknowledgments. H. I. thanks John Alexander Cruz Morales and Anton Mellit for helpful discussions. We thank referees for suggesting several improvements on the statement and the proof of Lemma 3. 8. This work is supported by the grants ANR-13-IS01-0001-01 and ANR-09-JCJC-0104-01 of the Agence nationale de la recherche and JSPS KAKENHI Grant Number 25400069, 23224002, 26610008. e λ the equivariant Euler class. e the non-equivariant Euler class. Z) represented by effective curves. γ(d) the pairing d γ between γ ∈ H 2 (X) and d ∈ H 2 (X).
1 ) and for the Tyrannidae family of birds in Fig. 6. For every family we analyze Bayesian sampling of 1000 trees downloaded from the database  [24]. Namely, we collect pairwise distances, n-minimal distances and distances between cherries of all 1000 trees and plot the histograms of these distances (with the y-axis divided by 1000) in Figs. 5  and 6. We fit all the points in a figure using the iterative reweighted least squares algorithm  [26]  in Matlab. Unfortunately, the explicit dependencies on λ and µ in Eqs. (14, 20, 21)  are insufficient to estimate all parameters. Instead one can estimate from the fit only the effective growth rate, λ -µ and λσ. The value of σ can be obtained assuming a certain ratio µ/λ. In the captions of Figs. 5  and 6  we present the obtained estimates for σ for different assumptions about the ratio µ/λ.
Figure4: The benefit to use N (t|T ) instead of N (t|T ) to estimate the parameters of the evolution process in a case of a small dataset. In this plot T = 1, λ = 11, µ = 5, σ = 0. 01 and dt = 0. 005. After average over many samples (S ∼ 10 6 in this particular case) empirical averages of both N (t|T ) (full circles) and N (t|T ) (open circles) converge nicely to the analytic formulas. The last are given in Eqs. (14) and (25) , respectively, and are denoted by the lines in the figure (see the legend). However, for a single random tree, S = 1, the values of N 1 (t|T ) (diamonds) are highly dispersed (most intervals show zero counts and do not show up in the semilogarithmic plot), such that their fit to the analytic formula of N (t|T ) is not expected to lead to a good estimation of the model's parameters.
)Figure 4 : 4 Figure4: The benefit to use N (t|T ) instead of N (t|T ) to estimate the parameters of the evolution process in a case of a small dataset. In this plot T = 1, λ = 11, µ = 5, σ = 0. 01 and dt = 0. 005. After average over many samples (S ∼ 10 6 in this particular case) empirical averages of both N (t|T ) (full circles) and N (t|T ) (open circles) converge nicely to the analytic formulas. The last are given in Eqs. (14) and (25) , respectively, and are denoted by the lines in the figure (see the legend). However, for a single random tree, S = 1, the values of N 1 (t|T ) (diamonds) are highly dispersed (most intervals show zero counts and do not show up in the semilogarithmic plot), such that their fit to the analytic formula of N (t|T ) is not expected to lead to a good estimation of the model's parameters.
Literature review and selection of parameters The literature search was conducted by consulting Medline, restricting it to articles published until December 2011. Seven different sets of keywords were considered (Table  1 ). A systematic selection procedure was conducted in two steps by two researchers independently. In the first step, the major topics of the articles were assessed by title and abstract. In this phase of the selection procedure, all articles reporting epidemiological data on influenza pandemics were included. In case of doubt on the article's relevant information, the article was included in the second selection step. In the second step, the full text articles, previously selected , were assessed. These articles were included in the review if they reported at least one of the following 13 parameters: basic reproductive number (R 0 ); clinical attack rate (CAR); age-specific CAR; case fatality rate (CFR); communicability/generation interval; modes of transmission; incubation period; timing and duration of pandemic; clinical consultation rate (CCR); hospital admission rate (HAR); intensive care admission rate (ICAR); work absenteeism; bed occupancy rate (BOR).
If the articles included did not contain information on at least one of the parameters listed above or if the study design was of low quality (e. g. small sample size, unclear definition of outcomes), they were excluded. Moreover, pertinent related citations were considered. Of each article included in the review, the following data were recorded: year of the study, year of pandemic referring to, country, and described parameters. International technical reports were obtained by consulting the websites of the European Centre for Disease Prevention and Control (ECDC) and WHO. Influenza pandemic preparedness plans for the European Union/ European Economic Area (EU/EEA) countries were obtained from the ECDC website  [9]. We also considered relevant studies based on mathematical modelling published in the literature but not retrieved through the search strategy.
Documents detected using seven combinations of keywords (n=338) • Most relevant official reports (n=327) Of the 83 relevant articles, 23 articles reported information on R 0 , with values ranging from 0. 99 to 3. 75. Thirty-four reported data on CAR, whose values ranged widely between 0 and 50%, while eight documents provided some information on age-specific CAR. For the CFR, 30 articles showed a range between 0 and 25%. Only three articles dealt with the generation interval, whose range was 1. 6-4. 1 days. The duration of infectiousness, reported in five articles, ranged between one and 21 days. Only seven articles provided generic descriptions of possible modes of transmission: all of them reported the respiratory route by droplets of infected secretions and/or hand-face contact after touching a contaminated person or surface.
Collection of Clinical Data Image acquisition. Imaging data of a patient with a right parietal GBM were acquired in the context of normal clinical practice at the Fondazione IRCCS Istituto Neurologico Besta by using a 3T Magnetic Resonance (MR) imaging scanner (Achieva; Philips Healthcare) equipped with a 32-channel phased array coil. Clinical imaging sequences included pre-and post-contrast axial volumetric T1 spin echo (SE) sequences, axial volumetric T2-turbo spin echo (T2-TSE) and a sagittal volumetric fluid attenuated inversion recovery (FLAIR). Pre-contrast whole-brain DTI data-sets were acquired using a single shot spin-echo echo planar imaging (EPI) sequence (TR shortest (4687 ms), TE 80 ms, voxel 2. 20×2. 20×2. 20 (mm 3 ), slices 90, SENSE 2, FAT SAT SPIR 200 Hz). The DTI protocol was multi-shell.
Data processing. Diffusion data were processed using a comprehensive correction pipeline with TORTOISE  [55]. T2-TSE images were used as the structural target for DTI data processing. T2-TSE image were aligned to the hemispheric mid line and the anterior and posterior commissure planes using MIPAV  [56]. DTI dataset were corrected to reduce the effects of rigid body motion, eddy current distortions  [57] , and EPI distortions  [58]. Corrections were performed in the native space, and appropriate rotations will be applied to the b-matrix  [57, 59]. Then, robust estimation of tensors by outlier rejection (RESTORE)  [60]  were used to estimate the diffusion tensor and tensor derived metrics. The RESTORE algorithm have been selected for its ability to detect and remove artifactual data points on a voxel-wise basis, correcting for subtle artifacts such as cardiac pulsation and respiration signal drop-outs, which has been shown to be an important consideration in clinical analyses of DTI data  [61].