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Package ‘survival’ August 24, 2021 Title Survival Analysis Priority recommended Version 3.2-13 Date 2021-08-23 Depends R (>= 3.5.0) Imports graphics, Matrix, methods, splines, stats, utils LazyData Yes LazyDataCompression xz ByteCompile Yes Description Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models. License LGPL (>= 2) URL https://github.com/therneau/survival NeedsCompilation yes Author Terry M Therneau [aut, cre], Thomas Lumley [ctb, trl] (original S->R port and R maintainer until 2009), Atkinson Elizabeth [ctb], Crowson Cynthia [ctb] Maintainer Terry M Therneau <[email protected]> Repository CRAN Date/Publication 2021-08-24 12:50:02 UTC R topics documented: aareg.............................................4 aeqSurv . .7 aggregate.survfit . .8 1 2 R topics documented: agreg.fit . .9 aml ............................................. 10 anova.coxph . 11 attrassign . 12 basehaz . 13 bladder . 14 blogit . 16 cch.............................................. 17 cgd.............................................. 19 cgd0............................................. 21 cipoisson . 22 clogit . 23 cluster . 25 colon . 26 concordance . 27 concordancefit . 30 cox.zph . 31 coxph . 33 coxph.control . 37 coxph.detail . 39 coxph.object . 40 coxph.wtest . 42 coxsurv.fit . 42 diabetic . 44 dsurvreg . 45 finegray . 46 flchain . 48 frailty . 50 gbsg............................................. 52 heart . 53 is.ratetable . 54 kidney . 55 levels.Surv . 56 lines.survfit . 56 logan ............................................ 59 logLik.coxph . 60 lung............................................. 61 mgus............................................. 62 mgus2 . 63 model.frame.coxph . 64 model.matrix.coxph . 65 myeloid . 66 myeloma . 67 nafld............................................. 68 neardate . 69 nsk.............................................. 71 nwtco . 73 ovarian . 74 R topics documented: 3 pbc.............................................. 75 pbcseq . 76 plot.aareg . 78 plot.cox.zph . 79 plot.survfit . 80 predict.coxph . 83 predict.survreg . 85 print.aareg . 87 print.summary.coxph . 88 print.summary.survexp . 88 print.summary.survfit . 89 print.survfit . 90 pseudo . 91 pspline . 92 pyears . 94 quantile.survfit . 97 ratetable . 99 ratetableDate . 100 ratetables . 101 rats.............................................. 102 rats2 . 102 reliability . 103 residuals.coxph . 104 residuals.survfit . 106 residuals.survreg . 107 retinopathy . 109 rhDNase . 110 ridge . 111 rotterdam . 113 royston . 114 rttright . 115 solder . 117 stanford2 . 118 statefig . 118 strata . 120 summary.aareg . 121 summary.coxph . 123 summary.pyears . 124 summary.survexp . 125 summary.survfit . 126 Surv............................................. 128 Surv-methods . 130 Surv2 . ..

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SURVIVAL SURVIVAL Students
SEQUENCE SURVIVORS: ANALYSIS OF GRADUATION OR TRANSFER IN THREE YEARS OR LESS By: Leila Jamoosian Terrence Willett CAIR 2018 Anaheim WHY DO WE WANT STUDENTS TO NOT “SURVIVE” COLLEGE MORE QUICKLY Traditional survival analysis tracks individuals until they perish Applied to a college setting, perishing is completing a degree or transfer Past analyses have given many years to allow students to show a completion (e.g. 8 years) With new incentives, completion in 3 years is critical WHY SURVIVAL NOT LINEAR REGRESSION? Time is not normally distributed No approach to deal with Censors MAIN GOAL OF STUDY To estimate time that students graduate or transfer in three years or less(time-to-event=1/ incidence rate) for a group of individuals We can compare time to graduate or transfer in three years between two of more groups Or assess the relation of co-variables to the three years graduation event such as number of credit, full time status, number of withdraws, placement type, successfully pass at least transfer level Math or English courses,… Definitions: Time-to-event: Number of months from beginning the program until graduating Outcome: Completing AA/AS degree in three years or less Censoring: Students who drop or those who do not graduate in three years or less (takes longer to graduate or fail) Note 1: Students who completed other goals such as certificate of achievement or skill certificate were not counted as success in this analysis Note 2: First timers who got their first degree at Cabrillo (they may come back for second degree
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Survival Analysis: an Exact Method for Rare Events
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 12-2020 Survival Analysis: An Exact Method for Rare Events Kristina Reutzel Utah State University Follow this and additional works at: https://digitalcommons.usu.edu/gradreports Part of the Survival Analysis Commons Recommended Citation Reutzel, Kristina, "Survival Analysis: An Exact Method for Rare Events" (2020). All Graduate Plan B and other Reports. 1509. https://digitalcommons.usu.edu/gradreports/1509 This Creative Project is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Plan B and other Reports by an authorized administrator of DigitalCommons@USU. For more information, please contact [email protected]. SURVIVAL ANALYSIS: AN EXACT METHOD FOR RARE EVENTS by Kristi Reutzel A project submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in STATISTICS Approved: Christopher Corcoran, PhD Yan Sun, PhD Major Professor Committee Member Daniel Coster, PhD Committee Member Abstract Conventional asymptotic methods for survival analysis work well when sample sizes are at least moderately sufficient. When dealing with small sample sizes or rare events, the results from these methods have the potential to be inaccurate or misleading. To handle such data, an exact method is proposed and compared against two other methods: 1) the Cox proportional hazards model and 2) stratified logistic regression for discrete survival analysis data. Background and Motivation Survival analysis models are used for data in which the interest is time until a specified event. The event of interest could be heart attack, onset of disease, failure of a mechanical system, cancellation of a subscription service, employee termination, etc.
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Ocean Data Standards
Manuals and Guides 54 Ocean Data Standards Volume 2 Recommendation to Adopt ISO 8601:2004 as the Standard for the Representation of Date and Time in Oceanographic Data Exchange UNESCO Manuals and Guides 54 Ocean Data Standards Volume 2 Recommendation to Adopt ISO 8601:2004 as the Standard for the Representation of Date and Time in Oceanographic Data Exchange UNESCO 2011 IOC Manuals and Guides, 54, Volume 2 Version 1 January 2011 For bibliographic purposes this document should be cited as follows: Paris. Intergovernmental Oceanographic Commission of UNESCO. 2011.Ocean Data Standards, Vol.2: Recommendation to adopt ISO 8601:2004 as the standard for the representation of dates and times in oceanographic data exchange.(IOC Manuals and Guides, 54, Vol. 2.) 17 pp. (English.)(IOC/2011/MG/54-2) © UNESCO 2011 Printed in France IOC Manuals and Guides No. 54 (2) Page (i) TABLE OF CONTENTS page 1. BACKGROUND ......................................................................................................................... 1 2. DATE AND TIME FOR DATA EXCHANGE ......................................................................... 1 3. INTERNATIONAL STANDARD ISO 8601:2004 .............................................................. 1 4. DATE AND TIME REPRESENTATION................................................................................ 2 4.1 Date ................................................................................................................................................. 2 4.2 Time ...............................................................................................................................................
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Islamic Calendar from Wikipedia, the Free Encyclopedia
Islamic calendar From Wikipedia, the free encyclopedia -at اﻟﺘﻘﻮﻳﻢ اﻟﻬﺠﺮي :The Islamic, Muslim, or Hijri calendar (Arabic taqwīm al-hijrī) is a lunar calendar consisting of 12 months in a year of 354 or 355 days. It is used (often alongside the Gregorian calendar) to date events in many Muslim countries. It is also used by Muslims to determine the proper days of Islamic holidays and rituals, such as the annual period of fasting and the proper time for the pilgrimage to Mecca. The Islamic calendar employs the Hijri era whose epoch was Islamic Calendar stamp issued at King retrospectively established as the Islamic New Year of AD 622. During Khaled airport (10 Rajab 1428 / 24 July that year, Muhammad and his followers migrated from Mecca to 2007) Yathrib (now Medina) and established the first Muslim community (ummah), an event commemorated as the Hijra. In the West, dates in this era are usually denoted AH (Latin: Anno Hegirae, "in the year of the Hijra") in parallel with the Christian (AD) and Jewish eras (AM). In Muslim countries, it is also sometimes denoted as H[1] from its Arabic form ( [In English, years prior to the Hijra are reckoned as BH ("Before the Hijra").[2 .(ﻫـ abbreviated , َﺳﻨﺔ ﻫِ ْﺠﺮﻳّﺔ The current Islamic year is 1438 AH. In the Gregorian calendar, 1438 AH runs from approximately 3 October 2016 to 21 September 2017.[3] Contents 1 Months 1.1 Length of months 2 Days of the week 3 History 3.1 Pre-Islamic calendar 3.2 Prohibiting Nasī’ 4 Year numbering 5 Astronomical considerations 6 Theological considerations 7 Astronomical
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Alexander Jones Calendrica I: New Callippic Dates
ALEXANDER JONES CALENDRICA I: NEW CALLIPPIC DATES aus: Zeitschrift für Papyrologie und Epigraphik 129 (2000) 141–158 © Dr. Rudolf Habelt GmbH, Bonn 141 CALENDRICA I: NEW CALLIPPIC DATES 1. Introduction. Callippic dates are familiar to students of Greek chronology, even though up to the present they have been known to occur only in a single source, Ptolemy’s Almagest (c. A.D. 150).1 Ptolemy’s Callippic dates appear in the context of discussions of astronomical observations ranging from the early third century B.C. to the third quarter of the second century B.C. In the present article I will present new attestations of Callippic dates which extend the period of the known use of this system by almost two centuries, into the middle of the first century A.D. I also take the opportunity to attempt a fresh examination of what we can deduce about the Callippic calendar and its history, a topic that has lately been the subject of quite divergent treatments. The distinguishing mark of a Callippic date is the specification of the year by a numbered “period according to Callippus” and a year number within that period. Each Callippic period comprised 76 years, and year 1 of Callippic Period 1 began about midsummer of 330 B.C. It is an obvious, and very reasonable, supposition that this convention for counting years was instituted by Callippus, the fourth- century astronomer whose revisions of Eudoxus’ planetary theory are mentioned by Aristotle in Metaphysics Λ 1073b32–38, and who also is prominent among the authorities cited in astronomical weather calendars (parapegmata).2 The point of the cycles is that 76 years contain exactly four so-called Metonic cycles of 19 years.
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WLOTA LIGHTHOUSE CALENDAR by F5OGG – WLOTA Manager
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S S symmetry Article Survival and Reliability Analysis with an Epsilon-Positive Family of Distributions with Applications Perla Celis 1,*, Rolando de la Cruz 1 , Claudio Fuentes 2 and Héctor W. Gómez 3 1 Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Peñalolén, Santiago 7941169, Chile; [email protected] 2 Department of Statistics, Oregon State University, 217 Weniger Hall, Corvallis, OR 97331, USA; [email protected] 3 Departamento de Matemática, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1240000, Chile; [email protected] * Correspondence: [email protected] Abstract: We introduce a new class of distributions called the epsilon–positive family, which can be viewed as generalization of the distributions with positive support. The construction of the epsilon– positive family is motivated by the ideas behind the generation of skew distributions using symmetric kernels. This new class of distributions has as special cases the exponential, Weibull, log–normal, log–logistic and gamma distributions, and it provides an alternative for analyzing reliability and survival data. An interesting feature of the epsilon–positive family is that it can viewed as a ﬁnite scale mixture of positive distributions, facilitating the derivation and implementation of EM–type algorithms to obtain maximum likelihood estimates (MLE) with (un)censored data. We illustrate the ﬂexibility of this family to analyze censored and uncensored data using two real examples. One of them was previously discussed in the literature; the second one consists of a new application to model recidivism data of a group of inmates released from the Chilean prisons during 2007.
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A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess
S S symmetry Article A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess Guillermo Martínez-Flórez 1, Víctor Leiva 2,* , Emilio Gómez-Déniz 3 and Carolina Marchant 4 1 Departamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad de Córdoba, Montería 14014, Colombia; [email protected] 2 Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile 3 Facultad de Economía, Empresa y Turismo, Universidad de Las Palmas de Gran Canaria and TIDES Institute, 35001 Canarias, Spain; [email protected] 4 Facultad de Ciencias Básicas, Universidad Católica del Maule, 3466706 Talca, Chile; [email protected] * Correspondence: [email protected] or [email protected] Received: 30 June 2020; Accepted: 19 August 2020; Published: 1 September 2020 Abstract: In this paper, we consider skew-normal distributions for constructing new a distribution which allows us to model proportions and rates with zero/one inflation as an alternative to the inflated beta distributions. The new distribution is a mixture between a Bernoulli distribution for explaining the zero/one excess and a censored skew-normal distribution for the continuous variable. The maximum likelihood method is used for parameter estimation. Observed and expected Fisher information matrices are derived to conduct likelihood-based inference in this new type skew-normal distribution. Given the flexibility of the new distributions, we are able to show, in real data scenarios, the good performance of our proposal. Keywords: beta distribution; centered skew-normal distribution; maximum-likelihood methods; Monte Carlo simulations; proportions; R software; rates; zero/one inflated data 1.
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Png Medical Manual Volume 5 Part E
Civil Aviation Safety Authority Of Papua New Guinea PNG MEDICAL MANUAL VOLUME 5 PART E Control Copy Number: …………… PNG MEDICAL MANUAL PRELIMINARY AUTHORISATION Page III of XIII AUTHORIZATION This manual is a Civil Aviation Safety Authority document setting out the procedures for aviation medical practices and forms part of the Civil Aviation Safety Authority Manual Suite. This manual sets out responsibilities; specific procedures and systems applicable to Aviation Medical Standards and Certification. The main purpose of the PNG Medical Manual is to assist and guide designated aviation medical examiners (DAMEs), medical assessors (MAs) and CASA, in decisions relating to the medical fitness of licence applicants as specified in Annex 1. The originator or Controlling Authority of this document is the Principal Medical Officer (PMO). For the purpose of ensuring that the process detailed in this document is standardised, I require all designated aviation medical examiners and relevant staff to use this document in the performance of their duties. This manual is a living document and I encourage DAMEs, Mas and CASA staff to continually contribute to its improvement taking into account the legislative changes, annex amendments and latest technological changes and experience, and also to your work practices covered by the procedures contained in this document. This document has been issued under the authority of the Director of the Civil Aviation Safety Authority. Rev. No. 01 Civil Aviation Safety Authority of Papua New Guinea Revision Date: 30/11/2017 PNG MEDICAL MANUAL PRELIMINARY AUTHORISATION Page IV of XIII INTENTIONALLY LEFT BLANK Rev. No. 01 Civil Aviation Safety Authority of Papua New Guinea Revision Date: 30/11/2017 PNG MEDICAL MANUAL PRELIMINARY CONDITION OF USE Page V of XIII CONDITION OF USE The assigned manual holder is responsible for the care and upkeep of the manual, and for its revision, in accordance with any instructions or revision material provided by the Civil Aviation Safety Authority.
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WLOTA LIGHTHOUSE CALENDAR by F5OGG – WLOTA Manager
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Survival Analysis on Duration Data in Intelligent Tutors
Survival Analysis on Duration Data in Intelligent Tutors Michael Eagle and Tiffany Barnes North Carolina State University, Department of Computer Science, 890 Oval Drive, Campus Box 8206 Raleigh, NC 27695-8206 {mjeagle,tmbarnes}@ncsu.edu Abstract. Effects such as student dropout and the non-normal distribu- tion of duration data confound the exploration of tutor efficiency, time-in- tutor vs. tutor performance, in intelligent tutors. We use an accelerated failure time (AFT) model to analyze the effects of using automatically generated hints in Deep Thought, a propositional logic tutor. AFT is a branch of survival analysis, a statistical technique designed for measur- ing time-to-event data and account for participant attrition. We found that students provided with automatically generated hints were able to complete the tutor in about half the time taken by students who were not provided hints. We compare the results of survival analysis with a standard between-groups mean comparison and show how failing to take student dropout into account could lead to incorrect conclusions. We demonstrate that survival analysis is applicable to duration data col- lected from intelligent tutors and is particularly useful when a study experiences participant attrition. Keywords: ITS, EDM, Survival Analysis, Efficiency, Duration Data 1 Introduction Intelligent tutoring systems have sizable effects on student learning efficiency | spending less time to achieve equal or better performance. In a classic example, students who used the LISP tutor spent 30% less time and performed 43% better on posttests when compared to a self-study condition [2]. While this result is quite famous, few papers have focused on differences between tutor interventions in terms of the total time needed by students to complete the tutor.
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Establishing the Discrete-Time Survival Analysis Model
What will we cover? §11.1 p.358 Specifying a suitable DTSA model §11.2 p.369 Establishing the Discrete-Time Fitting the DTSA model to data §11.3 p.378 Survival Analysis Model Interpreting the parameter estimates §11.4 p.386 (ALDA, Ch. 11) Displaying fitted hazard and survivor §11.5 p.391 functions Comparing DTSA models using §11.6 p.397 goodness-of-fit statistics. John Willett & Judy Singer Harvard University Graduate School of Education May, 2003 1 Specifying the DTSA Model Specifying the DTSA Model Data Example: Grade at First Intercourse Extract from the Person-Level & Person-Period Datasets Grade at First Intercourse (ALDA, Fig. 11.5, p. 380) • Research Question: Whether, and when, Not censored ⇒ did adolescent males experience heterosexual experience the event intercourse for the first time? Censored ⇒ did not • Citation: Capaldi, et al. (1996). experience the event • Sample: 180 high-school boys. • Research Design: – Event of interest is the first experience of heterosexual intercourse. Boy #193 was – Boys tracked over time, from 7th thru 12th grade. tracked until – 54 (30% of sample) were virgins (censored) at end he had sex in 9th grade. of data collection. Boy #126 was tracked until he had sex in 12th grade. Boy #407 was censored, remaining a virgin until he graduated. 2 Specifying the DTSA Model Specifying the DTSA Model Estimating Sample Hazard & Survival Probabilities Sample Hazard & Survivor Functions Grade at First Intercourse (ALDA, Table 11.1, p. 360) Grade at First Intercourse (ALDA, Fig. 10.2B, p. 340) Discrete-Time Hazard is the Survival probability describes the conditional probability that the chance that a person will survive event will occur in the period, beyond the period in question given that it hasn’t occurred without experiencing the event: earlier: S(t j ) = Pr{T > j} h(t j ) = Pr{T = j | T > j} Estimated by cumulating hazard: Estimated by the corresponding ˆ ˆ ˆ sample probability: S(t j ) = S(t j−1)[1− h(t j )] n events ˆ j e.g., h(t j ) = n at risk j ˆ ˆ ˆ S(t9 ) = S(t8 )[1− h(t9 )] e.g.
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