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Ditemukan 12103 dokumen yang sesuai dengan query
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Lawson, Andrew B.
London: CRC Press, Taylor & Francis Group, 2009
614.42 LAW b
Buku Teks SO  Universitas Indonesia Library
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Jewell, Nicholas P.
Boca Raton: Chapman & Hall/CRC, 2004
614.4 JEW s
Buku Teks SO  Universitas Indonesia Library
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Setia Gunawan Wijaya
"Scan statistic merupakan suatu analisis untuk mendeteksi daerah yang merupakan kejadian luar biasa atau KLB (outbreak). Salah satu metode yang mendasari analisis scan statistic adalah metode Bayesian Scan Statistic. Metode ini menerapkan prinsip teorema bayesian, yaitu memanfaatkan informasi prior untuk menghasilkan informasi posterior yang dapat memperbaiki informasi prior. Metode Bayesian Scan Statistic memilih keadaan atau kondisi yang memiliki posterior probability yang terbesar sebagai daerah KLB-nya. Fungsi marginal likelihood dan prior probability merupakan dua komponen penting yang digunakan dalam metode ini untuk menghitung posterior probability untuk tiap-tiap daerah. Fungsi marginal likelihood didapat dari data historis dan modelnya merupakan gabungan antara distribusi poisson dan distribusi gamma. Sedangan untuk prior probability juga didapat dari data historis atau berdasarkan pada pengalaman seseorang. Metode bayesian scan statistic ini dapat digunakan jika terdapat data masa lalu. Kata kunci : bayesian scan statistic, bayesian cluster detection, prior probability, posterior probability. x + 54 hlm. ; gamb. ; lamp. ; tab. Bibliografi : 9 (1986-2006)"
Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2007
S27733
UI - Skripsi Membership  Universitas Indonesia Library
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Kramer, Michael S.
New York : Springer-Verlag, 1988
614.4 KRA c
Buku Teks SO  Universitas Indonesia Library
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Evi Riyanti Yasir
"Dalam dunia epidemiologi, dibutuhkan suatu pemetaan untuk menggambarkan distribusi penyakit pada populasi, yang disebut dengan disease risk map. Mapping tersebut dibuat berdasarkan nilai SMR (Standardized Morbidity or Mortality Ratio) yang diperoleh dari informasi mengenai banyaknya penderita suatu penyakit di daerah tertentu. Semakin kecil skala disease risk map tersebut, maka semakin tepat sasaran untuk melakukan pencegahan terhadap suatu penyakit. Namun, masalah yang sering dijumpai adalah data banyaknya penderita penyakit hanya tersedia pada lingkup area yang besar. Sedangkan data mengenai penyebab terjangkitnya penyakit tersebut, tersedia dalam skala area yang lebih kecil. Ketidakseimbangan nilai-nilai variabel inilah yang disebut sebagai spatial misalignment. Sehingga digunakan pemodelan Bayesian berhierarki yang memanfaatkan fungsi likelihood dari variabel respon yang tersedia pada skala area lebih besar dan nilai-nilai kovariat yang tersedia pada area yang lebih kecil. Kemudian, dari distribusi posterior yang diperoleh, digunakan metode Markov Chain Monte Carlo (MCMC) untuk mencari nilai taksiran parameter. Berdasarkan persamaan linier dari log SMR pada model, diperoleh nilai estimasi SMR untuk skala area lebih kecil. Pemodelan Bayesian berhierarki ini diterapkan untuk membuat disease risk map skala area puskesmas Kota Depok pada kasus kelahiran bayi mati.

In epidemiology, mapping is needed to describe the distribution of disease in an area or among population, which is called disease risk map. The construction of disease risk map is based on the value of SMR (Standardized Morbidity or Mortality Ratio), that is obtained from the information about the number diagnosed of a disease in an area. If the scale of disease risk map is smaller, the prevention of the disease is more effective. However, the data about the number of cases of a disease is available from a larger scale area. On the other hand, data about the causes or factors of that disease is available at the smaller scale area. Such unbalance sources of those variables is called spatial misalignment. So that, it is needed to apply Bayesian hierarchical modeling that uses the likelihood of response variable which is available at the larger scale area and the value of covariates which is available at the smaller scale area. Then, by using the Markov Chain Monte Carlo (MCMC) method which build samples from the posterior distribution, the value of estimated parameters are obtained. Furthermore, based on the linear model for SMR, the estimated SMRs for the smaller scale area are obtained. To give an illustration, Bayesian hierarchical modeling is applied to construct the disease risk map at clinic scale area for stillbirths cases in Depok."
Depok: Universitas Indonesia, 2014
S54803
UI - Skripsi Membership  Universitas Indonesia Library
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Rothman, Kenneth J.
"The thoroughly revised and updated Third Edition of Dr. Rothman's acclaimed Modern Epidemiology reflects the conceptual development of this evolving science and the engagement of epidemiologists with an increasing range of current public health concerns.
"Modern Epidemiology covers a broad range of concepts and methods, including epidemiologic measures of occurrence and effect, study designs, validity, precision, statistical reference, and causal diagrams. Topics in data analysis range from basic tabular analyses to stratified analysis, multiple comparisons, Bayesian analysis, sensitivity analysis, and bias analysis, with an extensive overview of modern regression methods including logistic and survival regression, splines, hierarchical (multilevel) regression, propensity scores and other scoring methods, and g-estimation"
Philadelphia: Lippincott Williams / Wilkins, 2008
614.4 ROT m
Buku Teks SO  Universitas Indonesia Library
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Daniels, Michael J.
London: Chapman & Hall / CRC, 2008
519.5 DAN m
Buku Teks SO  Universitas Indonesia Library
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Yu-Kang Tu, editor
"Modern methods for epidemiology provides a concise introduction to recent development in statistical methodologies for epidemiological and biomedical researchers."
Dordrecht: [, Springer], 2012
e20410799
eBooks  Universitas Indonesia Library
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Dordrecht: Springer , 2012
614.407 2 MOD
Buku Teks SO  Universitas Indonesia Library
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Woodward, M. (Mark)
"Highly praised for its broad, practical coverage, the second edition of this popular text incorporated the major statistical models and issues relevant to epidemiological studies. Epidemiology: Study Design and Data Analysis, Third Edition continues to focus on the quantitative aspects of epidemiological research. Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems. New to the Third EditionNew chapter on risk scores and clinical decision rules New chapter on computer-intensive methods, including the bootstrap, permutation tests, and missing value imputationNew sections on binomial regression models, competing risk, information criteria, propensity scoring, and splinesMany more exercises and examples using both Stata and SASMore than 60 new figures After introducing study design and reviewing all the standard methods, this self-contained book takes students through analytical methods for both general and specific epidemiological study designs, including cohort, case-control, and intervention studies. In addition to classical methods, it now covers modern methods that exploit the enormous power of contemporary computers. The book also addresses the problem of determining the appropriate size for a study, discusses statistical modeling in epidemiology, covers methods for comparing and summarizing the evidence from several studies, and explains how to use statistical models in risk forecasting and assessing new biomarkers. The author illustrates the techniques with numerous real-world examples and interprets results in a practical way. He also includes an extensive list of references for further reading along with exercises to reinforce understanding. Web ResourceA wealth of supporting material can be downloaded from the book's CRC Press web page, including:Real-life data sets used in the textSAS and Stata programs used for examples in the textSAS and Stata programs for special techniques coveredSample size spreadsheet "--
"Preface This book is about the quantitative aspects of epidemiological research. I have written it with two audiences in mind: the researcher who wishes to understand how statistical principles and techniques may be used to solve epidemiological problems and the applied statistician who wishes to find out how to apply her or his subject in this field. A practical approach is used; although a complete set of formulae are included where hand calculation is viable, mathematical proofs are omitted and statistical nicety has largely been avoided. The techniques described are illustrated by example, and results of the applications of the techniques are interpreted in a practical way. Sometimes hypothetical datasets have been constructed to produce clear examples of epidemiological concepts and methodology. However, the majority of the data used in examples, and exercises, are taken from real epidemiological investigations, drawn from past publications or my own collaborative research. Several substantial datasets are either listed within the book or, more often, made available on book's web site for the reader to explore using her or his own computer software. SAS and Stata programs for most of the examples, where appropriate, are also provided on this web site. Finally, an extensive list of references is included for further reading. I have assumed that the reader has some basic knowledge of statistics, such as might be obtained from a medical degree course, or a first-year course in statistics as part of a science degree. Even so, this book is self-contained in that all the standard methods necessary to the rest of the book are reviewed in Chapter 2. From this base, the text goes through analytical methods for general and specific epidemiological study designs"
Boca Raton: Taylor and Francis, 2014
614.407 2 WOO e
Buku Teks SO  Universitas Indonesia Library
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