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Journal articles on the topic 'Event rate'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Wiersema, Roeljan, Jaap Van Der Meere, Herbert Roeyers, Rudy Van Coster, and Dieter Baeyens. "Event rate and event-related potentials in ADHD." Journal of Child Psychology and Psychiatry 47, no. 6 (January 30, 2006): 560–67. http://dx.doi.org/10.1111/j.1469-7610.2005.01592.x.

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2

Pickel, J. C. "Single-event effects rate prediction." IEEE Transactions on Nuclear Science 43, no. 2 (April 1996): 483–95. http://dx.doi.org/10.1109/23.490895.

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3

Basin, David, Bhargav Nagaraja Bhatt, Srđan Krstić, and Dmitriy Traytel. "Almost event-rate independent monitoring." Formal Methods in System Design 54, no. 3 (February 6, 2019): 449–78. http://dx.doi.org/10.1007/s10703-018-00328-3.

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4

Corcelli, S. A., J. A. Rahman, and J. C. Tully. "Efficient thermal rate constant calculation for rare event systems." Journal of Chemical Physics 118, no. 3 (January 15, 2003): 1085–88. http://dx.doi.org/10.1063/1.1529192.

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5

BELL, JOHN R. "Protocol Cuts Hospital's Hypoglycemia Event Rate." Internal Medicine News 40, no. 6 (March 2007): 11. http://dx.doi.org/10.1016/s1097-8690(07)70276-7.

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6

Roy, A., P. N. Steinmetz, and E. Niebur. "Rate Limitations of Unitary Event Analysis." Neural Computation 12, no. 9 (September 1, 2000): 2063–82. http://dx.doi.org/10.1162/089976600300015060.

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Unitary event analysis is a new method for detecting episodes of synchronized neural activity (Riehle, Grüun, Diesmann, & Aertsen, 1997). It detects time intervals that contain coincident firing at higher rates than would be expected if the neurons fired as independent inhomogeneous Poisson processes; all coincidences in such intervals are called unitary events (UEs). Changes in the frequency of UEs that are correlated with behavioral states may indicate synchronization of neural firing that mediates or represents the behavioral state. We show that UE analysis is subject to severe limitations due to the underlying discrete statistics of the number of coincident events. These limitations are particularly stringent for low (0–10 spikes/s) firing rates. Under these conditions, the frequency of UEs is a random variable with a large variation relative to its mean. The relative variation decreases with increasing firing rate, and we compute the lowest firing rate, at which the 95% confidence interval around the mean frequency of UEs excludes zero. This random variation in UE frequency makes interpretation of changes in UEs problematic for neurons with low firing rates. As a typical example, when analyzing 150 trials of an experiment using an averaging window 100 ms wide and a 5ms coincidence window, firing rates should be greater than 7 spikes per second.

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7

Bouaziz, O., and A. Guilloux. "A penalized algorithm for event-specific rate models for recurrent events." Biostatistics 16, no. 2 (November 11, 2014): 281–94. http://dx.doi.org/10.1093/biostatistics/kxu046.

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8

Hochberg, Alan M., Ronald K. Pearson, Donald J. OʼHara, and Stephanie J. Reisinger. "Drug-versus-Drug Adverse Event Rate Comparisons." Drug Safety 32, no. 2 (2009): 137–46. http://dx.doi.org/10.2165/00002018-200932020-00006.

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9

Tuli, Sagun K., Jayshree Tuli, Peng Chen, and Eric J. Woodard. "Fusion rate: a time-to-event phenomenon." Journal of Neurosurgery: Spine 1, no. 1 (July 2004): 47–51. http://dx.doi.org/10.3171/spi.2004.1.1.0047.

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Object. The term “fusion rate” is generally denoted in the literature as the percentage of patients with successful fusion over a specific range of follow up. Because the time to fusion is a time-to-event phenomenon a more accurate method of representation may be made using the Kaplan—Meier method of estimation. Methods. The current study was performed to illustrate that fusion rate is more accurately represented by median times as calculated using survival analysis. Patients undergoing a cervical decompressive corpectomy and reconstruction formed the basis of the primary analysis. A secondary analysis was made to evaluate the difference in the fusion times for one- compared with multilevel corpectomy cases. Data were collected at a tertiary care institution over a 5-year period with 6-month follow up after the last recruitment. Descriptive statistics of baseline patient characteristics, the extent of disease, and the surgical intervention were obtained. Fusion was the final outcome, and it was defined as the “event.” The presence of any trabeculae bridging between the vertebral body and allograft signified the occurrence of an event. Postoperative static radiographs were evaluated by independent neuroradiologists to assess the presence of fusion. Fusion rate was determined using the Kaplan—Meier estimate. The median time to fusion was calculated, as were the 95% confidence intervals (CIs). These were stratified for patients who underwent one- and two-level vertebrectomy. The log-rank test was used to differentiate between one-level and multilevel corpectomy. Multivariate analysis was performed using Cox regression for further evaluation, by adjusting for covariates (age, sex, smoking history). Fifty-seven patients underwent single- or multilevel corpectomy and fusion. The male/female ratio was similar, with a median age of 53 years. Fourteen patients had a history of cigarette smoking. Thirty-six patients underwent a one-level corpectomy, 20 a two-level corpectomy, and one patient underwent a three-level corpectomy. The analysis was restricted to one- and two-level cases. The median time to fusion for the cephalad and caudad aspect of the graft—host interface was 88 days (95% CI 82–94 days) and 85 days (95% CI 77–93 days), respectively. As generally reported in the literature, this translates to a 92% (by 2.1 years) and 93% (by 1.5 years) fusion rate, for the cephalad and caudad, respectively. The median time to fusion for the cephalad aspect of the graft for one-level vertebrectomy was 87 days (95% CI 83–91 days), whereas for two-level vertebrectomy was 90 days (95% CI 59–121 days). The median time to fusion for the caudal aspect of the graft—host interface was 85 days (95% CI 80–90 days) for one-level corpectomy and 90 days (95% CI 83–97 days) for the two-level cases. There was no statistically significant difference in the median time to fusion for one- and two-level corpectomy at either the superior or inferior aspect of the graft (p = 0.19 and 0.84, respectively). This held true even after adjusting for covariates. Conclusions. Fusion rate is a time-to-event phenomenon and is more accurately represented using the Kaplan—Meier method of estimation.

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10

MACNEIL, JANE SALODOF. "LMW Heparin Cuts Event Rate in Cancer." Internal Medicine News 42, no. 1 (January 2009): 1–4. http://dx.doi.org/10.1016/s1097-8690(09)70001-0.

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11

Z. Idris, Shahrul, Tuan N. T., Surinder K., Azlan H., Zunida A., Tay G. H., Noor A., and Razali O. "Cardiac Event Rate in Hypertrophic Cardiomyopathy Patients." Journal of Arrhythmia 27, Supplement (2011): PE4_082. http://dx.doi.org/10.4020/jhrs.27.pe4_082.

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12

Jondeau, Guillaume, Delphine Detaint, Florence Tubach, Florence Arnoult, Olivier Milleron, Francois Raoux, Gabriel Delorme, et al. "Aortic Event Rate in the Marfan Population." Circulation 125, no. 2 (January 17, 2012): 226–32. http://dx.doi.org/10.1161/circulationaha.111.054676.

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13

Petersen, E. L. "Approaches to proton single-event rate calculations." IEEE Transactions on Nuclear Science 43, no. 2 (April 1996): 496–504. http://dx.doi.org/10.1109/23.490896.

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14

Hall, B. D., D. Reinhard, and R. Monot. "Optimum event rate for a CCD detector." Review of Scientific Instruments 66, no. 3 (March 1995): 2668–71. http://dx.doi.org/10.1063/1.1145607.

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15

Letaw, John R. "Single event effects rate predictions in space." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 56-57 (May 1991): 1260–62. http://dx.doi.org/10.1016/0168-583x(91)95146-5.

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16

Claypoole, Victoria L., Daryn A. Dever, Kody L. Denues, and James L. Szalma. "The Effects of Event Rate on a Cognitive Vigilance Task." Human Factors: The Journal of the Human Factors and Ergonomics Society 61, no. 3 (August 2, 2018): 440–50. http://dx.doi.org/10.1177/0018720818790840.

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Objective: The present experiment sought to examine the effects of event rate on a cognitive vigilance task. Background: Vigilance, or the ability to sustain attention, is an integral component of human factors research. Vigilance task difficulty has previously been manipulated through increasing event rate. However, most research in this paradigm has utilized a sensory-based task, whereas little work has focused on these effects in relation to a cognitive-based task. Method: In sum, 84 participants completed a cognitive vigilance task that contained either 24 events per minute (low event rate condition) or 40 events per minute (high event rate condition). Performance was measured through the proportion of hits, false alarms, mean response time, and signal detection analyses (i.e., sensitivity and response bias). Additionally, measures of perceived workload and stress were collected. Results: The results indicated that event rate significantly affected performance, such that participants who completed the low event rate task achieved significantly better performance in terms of correction detections and false alarms. Furthermore, the cognitive vigil utilized in the present study produced performance decrements comparable to traditional sensory vigilance tasks. Conclusion: Event rate affects cognitive vigilance tasks in a similar manner as traditional sensory vigilance tasks, such that a direct relation between performance and level of event rate was established. Application: Cognitive researchers wishing to manipulate task difficulty in their experiments may use event rate presentation as one avenue to achieve this result.

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17

van Aalst, Robertus, Edward Thommes, Maarten Postma, Ayman Chit, and Issa J. Dahabreh. "On the Causal Interpretation of Rate-Change Methods: The Prior Event Rate Ratio and Rate Difference." American Journal of Epidemiology 190, no. 1 (June 29, 2020): 142–49. http://dx.doi.org/10.1093/aje/kwaa122.

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Abstract A growing number of studies use data before and after treatment initiation in groups exposed to different treatment strategies to estimate “causal effects” using a ratio measure called the prior event rate ratio (PERR). Here, we offer a causal interpretation for PERR and its additive scale analog, the prior event rate difference (PERD). We show that causal interpretation of these measures requires untestable rate-change assumptions about the relationship between 1) the change of the counterfactual rate before and after treatment initiation in the treated group under hypothetical intervention to implement the control strategy; and 2) the change of the factual rate before and after treatment initiation in the control group. The rate-change assumption is on the multiplicative scale for PERR but on the additive scale for PERD; the 2 assumptions hold simultaneously under testable, but unlikely, conditions. Even if investigators can pick the most appropriate scale, the relevant rate-change assumption might not hold exactly, so we describe sensitivity analysis methods to examine how assumption violations of different magnitudes would affect study results. We illustrate the methods using data from a published study of proton pump inhibitors and pneumonia.

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18

SCHAUBEL, DOUGLAS, and JIANWEN CAI. "Rate/Mean Regression for Multiple-Sequence Recurrent Event Data with Missing Event Category." Scandinavian Journal of Statistics 33, no. 2 (June 2006): 191–207. http://dx.doi.org/10.1111/j.1467-9469.2006.00459.x.

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19

Xapsos, M. A., L. W. Massenqill, W. J. Stapor, P. Shapiro, A. B. Campbell, S. E. Kerns, K. W. Fernald, and A. R. Knudson. "Single-Event, Enhanced Single-Event and Dose-Rate Effects with Pulsed Proton Beams." IEEE Transactions on Nuclear Science 34, no. 6 (1987): 1419–25. http://dx.doi.org/10.1109/tns.1987.4337491.

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20

Radaelli, Giovanni. "Detection of an unknown increase in the rate of a rare event." Journal of Applied Statistics 23, no. 1 (February 1996): 105–14. http://dx.doi.org/10.1080/02664769624396.

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21

Zeng, D., and J. Cai. "A semiparametric additive rate model for recurrent events with an informative terminal event." Biometrika 97, no. 3 (July 26, 2010): 699–712. http://dx.doi.org/10.1093/biomet/asq039.

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22

Teuteberg, J. J., G. C. Stewart, M. Jessup, R. L. Kormos, B. Sun, O. H. Frazier, D. C. Naftel, and L. W. Stevenson. "62: Strategies for LVAD Therapy in INTERMACS: Intent Rate v. Event Rate." Journal of Heart and Lung Transplantation 29, no. 2 (February 2010): S26—S27. http://dx.doi.org/10.1016/j.healun.2009.11.070.

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23

Chumakov, A. I., V. M. Uzhegov, A. O. Akhmetov, D. V. Boychenko, A. V. Yanenko, and N. V. Ryasnoy. "Single Event Effects Rate Calculation with Different Models." Bezopasnost informacionnyh tehnology, no. 1 (March 2017): 73–84. http://dx.doi.org/10.26583/bit.2017.1.09.

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24

&NA;. "Regimen containing verapamil lowers event rate post-MI." Inpharma Weekly &NA;, no. 1082 (April 1997): 18. http://dx.doi.org/10.2165/00128413-199710820-00037.

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25

JANCIN, BRUCE. "Pioglitazone Cut Ischemic Cardiac Event Rate by 17%." Family Practice News 38, no. 19 (October 2008): 18. http://dx.doi.org/10.1016/s0300-7073(08)71225-8.

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26

Lin, Yi-Bing, Ying-Ju Shih, Hung-Chun Tseng, and Ling-Jyh Chen. "LWA Rate Adaption by Enhanced Event-Triggered Reporting." IEEE Transactions on Vehicular Technology 67, no. 11 (November 2018): 10950–59. http://dx.doi.org/10.1109/tvt.2018.2865782.

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27

Harris, Euan D., and Timothy G. Griffin. "Rate Limiting in an Event-Driven BGP Speaker." IEEE Journal on Selected Areas in Communications 28, no. 8 (October 2010): 1287–98. http://dx.doi.org/10.1109/jsac.2010.101006.

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28

WACHTER, KERRI. "Adverse Event Rate Higher in Users of Biologics." Internal Medicine News 44, no. 6 (April 2011): 14. http://dx.doi.org/10.1016/s1097-8690(11)70275-x.

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29

Nakamura, T., and R. Nishi. "Expected EAGLE Event Rate towards the Magellanic Clouds." Progress of Theoretical Physics 99, no. 6 (June 1, 1998): 963–70. http://dx.doi.org/10.1143/ptp.99.963.

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30

Chlouber, D., P. O'Neill, and J. Pollock. "General upper bound on single-event upset rate." IEEE Transactions on Nuclear Science 37, no. 2 (April 1990): 1065–71. http://dx.doi.org/10.1109/23.106755.

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31

Chilouber, D., and J. Pollock. "General Upper Bound On Single-event Upset Rate." IEEE Transactions on Nuclear Science 37, no. 2 (April 1990): 1065–71. http://dx.doi.org/10.1109/tns.1990.574199.

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32

Pickel, J. C. "Correction to "Single-Event Effects Rate Prediction" [Correspondence]." IEEE Transactions on Nuclear Science 43, no. 4 (August 1996): 2454. http://dx.doi.org/10.1109/tns.1996.531796.

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33

Bruinsma, M., H. Fleckenstein, E. K. E. Gerndt, J. Flammer, A. Michetti, I. Negri, M. Norenberg, et al. "Track finding at 10-MHz hadronic event rate." IEEE Transactions on Nuclear Science 49, no. 2 (April 2002): 347–56. http://dx.doi.org/10.1109/tns.2002.1003734.

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34

Petersen, E. L., V. Pouget, L. W. Massengill, S. P. Buchner, and D. McMorrow. "Rate predictions for single-event effects - critique II." IEEE Transactions on Nuclear Science 52, no. 6 (December 2005): 2158–67. http://dx.doi.org/10.1109/tns.2005.860687.

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35

Petersen, E. L., J. C. Pickel, J. H. Adams, and E. C. Smith. "Rate prediction for single event effects-a critique." IEEE Transactions on Nuclear Science 39, no. 6 (1992): 1577–99. http://dx.doi.org/10.1109/23.211340.

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36

Zeng, Donglin, Douglas E. Schaubel, and Jianwen Cai. "Semiparametric Transformation Rate Model for Recurrent Event Data." Statistics in Biosciences 3, no. 2 (October 21, 2011): 187–207. http://dx.doi.org/10.1007/s12561-011-9043-4.

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37

Pellish, Jonathan A., Robert A. Reed, Akil K. Sutton, Robert A. Weller, Martin A. Carts, Paul W. Marshall, Cheryl J. Marshall, et al. "A Generalized SiGe HBT Single-Event Effects Model for On-Orbit Event Rate Calculations." IEEE Transactions on Nuclear Science 54, no. 6 (December 2007): 2322–29. http://dx.doi.org/10.1109/tns.2007.909987.

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38

Fan, Wen Hao, Lei Ding, Bi Hua Tang, Fan Wu, and Hong Guang Zhang. "Event Probability Based Priority Filter for Efficient Event Matching." Applied Mechanics and Materials 687-691 (November 2014): 1672–76. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1672.

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Event matching plays a critical role in content-based publish/subscribe system. Most traditional methods focus on existing subscriptions separation and combination. However, an event usually comes with certain probability distribution in each dimension. Thus taking both existing subscriptions and probable coming event into consideration can improve event matching time efficiency. Based on that, we put forward PF (Priority Filter), a highly efficient event matching algorithm. By building up a unified model with historical subscriptions for continuous and discrete attributes, we derive formulas to calculate each attribute’s filtering rate. Besides, in order to guarantee time efficiency both in matching, inserting, and deleting, a red-black tree regarded as a priority filter is built up on all attributes according to filtering rate. Experiments demonstrate that PF has a 30% faster speed compared to existing methods with acceptable insertion and deletion time and memory consumption.

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39

JANCIN, BRUCE. "Antianginal Drug Cuts Event Rate in High-Risk Patients." Internal Medicine News 41, no. 18 (September 2008): 1–2. http://dx.doi.org/10.1016/s1097-8690(08)71009-6.

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40

Jancin, Bruce. "High Bariatric Surgeon Volume Predicts Low Adverse Event Rate." Internal Medicine News 42, no. 14 (August 2009): 51. http://dx.doi.org/10.1016/s1097-8690(09)70549-9.

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41

JANCIN, BRUCE. "MADIT-CRT Cuts Event Rate in Mildly Symptomatic HF." Internal Medicine News 42, no. 17 (October 2009): 6. http://dx.doi.org/10.1016/s1097-8690(09)70660-2.

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42

Leyland, Elizabeth. "US research highlights high adverse event rate of antibiotics." Lancet Infectious Diseases 8, no. 10 (October 2008): 592. http://dx.doi.org/10.1016/s1473-3099(08)70214-7.

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43

De Pascalis, V., and R. J. Barry. "Event-related potentials and heart rate to emotional words." International Journal of Psychophysiology 25, no. 1 (January 1997): 46. http://dx.doi.org/10.1016/s0167-8760(97)85464-5.

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44

Basri, Atikah Balqis, Ahmad Fadzil Ismail, Muhamad Haziq Khairolanuar, Nuurul Hudaa Mohd Sobli, Khairayu Badron, and Mohammad Kamrul Hasan. "Analyses of Rainfall Rate During Malaysian 2014 Flood Event." International Journal of Multimedia and Ubiquitous Engineering 11, no. 8 (August 31, 2016): 237–46. http://dx.doi.org/10.14257/ijmue.2016.11.8.25.

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45

CHIANG, CHIN-TSANG, MEI-CHENG WANG, and CHIUNG-YU HUANG. "Kernel Estimation of Rate Function for Recurrent Event Data." Scandinavian Journal of Statistics 32, no. 1 (March 2005): 77–91. http://dx.doi.org/10.1111/j.1467-9469.2005.00416.x.

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46

Jancin, Bruce. "Body Mass Index Affects CV Event Rate in Hypertensives." Internal Medicine News 38, no. 3 (February 2005): 63. http://dx.doi.org/10.1016/s1097-8690(05)71510-9.

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47

Baltz, Edward A., and Joseph Silk. "Event Rate and Einstein Time Evaluation in Pixel Microlensing." Astrophysical Journal 530, no. 2 (February 20, 2000): 578–92. http://dx.doi.org/10.1086/308385.

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48

Proskurnikov, Anton V., and Manuel Mazo. "Lyapunov Event-Triggered Stabilization With a Known Convergence Rate." IEEE Transactions on Automatic Control 65, no. 2 (February 2020): 507–21. http://dx.doi.org/10.1109/tac.2019.2907435.

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49

Laitinen, K. "Use individual event rate adjustment for follow up time." BMJ 310, no. 6992 (June 3, 1995): 1469–70. http://dx.doi.org/10.1136/bmj.310.6992.1469b.

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50

Hinich, Melvin J., and Apostolos Serletis. "Episodic Nonlinear Event Detection in the Canadian Exchange Rate." Journal of the American Statistical Association 102, no. 477 (March 2007): 68–74. http://dx.doi.org/10.1198/016214506000001004.

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