Kooperativer Bibliotheksverbund

UID:

Umfang: 380 S. , 21 cm

ISBN: 9783552061361

Originaltitel: Cleo 〈 dt. 〉

Inhalt: Sam, der 8-jährige Sohn der Autorin, stirbt bei einem Unglück. Doch kurz zuvor hat Sam sich ein Kätzchen ausgesucht, und das kann Helen Brown nicht ablehnen. Das winzige, kränkliche Fellbündel, Cleo getauft, erweist sich als Glücksfall für alle Beteiligten.

Anmerkung: Aus dem Engl. übers.

Sprache: Deutsch

Schlagwort(e): Belletristische Darstellung

UID:

Umfang: 444 S.

Ausgabe: 1. Aufl.

ISBN: 9783552061934

Inhalt: Auf dem Höhepunkt ihrer familiären Krise legt sich Helen Brown wider besseres Wissen eine neue Katze zu, den Siamkater Jonah. Besitzt dieser Kater, dessen liebstes Hobby die Zerstörung des Haushalts zu sein scheint, ausreichend Karma, um für Frieden zu sorgen?

Anmerkung: Aus d. Engl. von Andrea Stumpf u. Gabriele Werbeck

Sprache: Deutsch

URL: http://deposit.d-nb.de/cgi-bin/dokserv?id=4042975&prov=M&dok_var=1&dok_ext=htm

UID:

Umfang: 1 online resource

Ausgabe: Third edition.

ISBN: 9781118778241 , 1118778243 , 9781118778210 , 1118778219

Serie: Statistics in practice

Inhalt: A fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott's groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on incomplete block designs and the analysis of bilateral data. Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists. Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output. Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, and further material. This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.

Anmerkung: Cover -- Title Page -- Copyright -- Contents -- Preface to third edition -- Mixed models notation -- About the Companion Website -- Chapter 1 Introduction -- 1.1 The use of mixed models -- 1.2 Introductory example -- 1.2.1 Simple model to assess the effects of treatment (Model A) -- 1.2.2 A model taking patient effects into account (Model B) -- 1.2.3 Random effects model (Model C) -- 1.2.4 Estimation (or prediction) of random effects -- 1.3 A multi-centre hypertension trial -- 1.3.1 Modelling the data -- 1.3.2 Including a baseline covariate (Model B) -- 1.3.3 Modelling centre effects (Model C) -- 1.3.4 Including centre-by-treatment interaction effects (Model D) -- 1.3.5 Modelling centre and centre·treatment effects as random (Model E) -- 1.4 Repeated measures data -- 1.4.1 Covariance pattern models -- 1.4.2 Random coefficients models -- 1.5 More about mixed models -- 1.5.1 What is a mixed model? -- 1.5.2 Why use mixed models? -- 1.5.3 Communicating results -- 1.5.4 Mixed models in medicine -- 1.5.5 Mixed models in perspective -- 1.6 Some useful definitions -- 1.6.1 Containment -- 1.6.2 Balance -- 1.6.3 Error strata -- Chapter 2 Normal mixed models -- 2.1 Model definition -- 2.1.1 The fixed effects model -- 2.1.2 The mixed model -- 2.1.3 The random effects model covariance structure -- 2.1.4 The random coefficients model covariance structure -- 2.1.5 The covariance pattern model covariance structure -- 2.2 Model fitting methods -- 2.2.1 The likelihood function and approaches to its maximisation -- 2.2.2 Estimation of fixed effects -- 2.2.3 Estimation (or prediction) of random effects and coefficients -- 2.2.4 Estimation of variance parameters -- 2.3 The Bayesian approach -- 2.3.1 Introduction -- 2.3.2 Determining the posterior density. , 2.3.3 Parameter estimation, probability intervals and p-values -- 2.3.4 Specifying non-informative prior distributions -- 2.3.5 Evaluating the posterior distribution -- 2.4 Practical application and interpretation -- 2.4.1 Negative variance components -- 2.4.2 Accuracy of variance parameters -- 2.4.3 Bias in fixed and random effects standard errors -- 2.4.4 Significance testing -- 2.4.5 Confidence intervals -- 2.4.6 Checking model assumptions -- 2.4.7 Missing data -- 2.4.8 Determining whether the simulated posterior distribution has converged -- 2.5 Example -- 2.5.1 Analysis models -- 2.5.2 Results -- 2.5.3 Discussion of points from Section 2.4 -- Chapter 3 Generalised linear mixed models -- 3.1 Generalised linear models -- 3.1.1 Introduction -- 3.1.2 Distributions -- 3.1.3 The general form for exponential distributions -- 3.1.4 The GLM definition -- 3.1.5 Fitting the GLM -- 3.1.6 Expressing individual distributions in the general exponential form -- 3.1.7 Conditional logistic regression -- 3.2 Generalised linear mixed models -- 3.2.1 The GLMM definition -- 3.2.2 The likelihood and quasi-likelihood functions -- 3.2.3 Fitting the GLMM -- 3.3 Practical application and interpretation -- 3.3.1 Specifying binary data -- 3.3.2 Uniform effects categories -- 3.3.3 Negative variance components -- 3.3.4 Presentation of fixed and random effects estimates -- 3.3.5 Accuracy of variance parameters and potential bias -- 3.3.6 Bias in fixed and random effects standard errors -- 3.3.7 The dispersion parameter -- 3.3.8 Significance testing -- 3.3.9 Confidence intervals -- 3.3.10 Model checking -- 3.3.11 Determining whether the simulated posterior distribution has converged -- 3.4 Example -- 3.4.1 Introduction and models fitted -- 3.4.2 Results -- 3.4.3 Discussion of points from Section 3.3. , Chapter 4 Mixed models for categorical data -- 4.1 Ordinal logistic regression (fixed effects model) -- 4.2 Mixed ordinal logistic regression -- 4.2.1 Definition of the mixed ordinal logistic regression model -- 4.2.2 Residual variance matrix -- 4.2.3 Likelihood and quasi-likelihood functions -- 4.2.4 Model fitting methods -- 4.3 Mixed models for unordered categorical data -- 4.3.1 The G matrix -- 4.3.2 The R matrix -- 4.3.3 Fitting the model -- 4.4 Practical application and interpretation -- 4.4.1 Expressing fixed and random effects results -- 4.4.2 The proportional odds assumption -- 4.4.3 Number of covariance parameters -- 4.4.4 Choosing a covariance pattern -- 4.4.5 Interpreting covariance parameters -- 4.4.6 Checking model assumptions -- 4.4.7 The dispersion parameter -- 4.4.8 Other points -- 4.5 Example -- Chapter 5 Multi-centre trials and meta-analyses -- 5.1 Introduction to multi-centre trials -- 5.1.1 What is a multi-centre trial? -- 5.1.2 Why use mixed models to analyse multi-centre data? -- 5.2 The implications of using different analysis models -- 5.2.1 Centre and centre·treatment effects fixed -- 5.2.2 Centre effects fixed, centre·treatment effects omitted -- 5.2.3 Centre and centre·treatment effects random -- 5.2.4 Centre effects random, centre·treatment effects omitted -- 5.3 Example: a multi-centre trial -- 5.4 Practical application and interpretation -- 5.4.1 Plausibility of a centre·treatment interaction -- 5.4.2 Generalisation -- 5.4.3 Number of centres -- 5.4.4 Centre size -- 5.4.5 Negative variance components -- 5.4.6 Balance -- 5.5 Sample size estimation -- 5.5.1 Normal data -- 5.5.2 Binary data -- 5.5.3 Categorical data -- 5.6 Meta-analysis -- 5.7 Example: meta-analysis -- 5.7.1 Analyses -- 5.7.2 Results -- 5.7.3 Treatment estimates in individual trials -- Chapter 6 Repeated measures data. , 6.1 Introduction -- 6.1.1 Reasons for repeated measurements -- 6.1.2 Analysis objectives -- 6.1.3 Fixed effects approaches -- 6.1.4 Mixed models approaches -- 6.2 Covariance pattern models -- 6.2.1 Covariance patterns -- 6.2.2 Choice of covariance pattern -- 6.2.3 Choice of fixed effects -- 6.2.4 General points -- 6.3 Example: covariance pattern models for normal data -- 6.3.1 Analysis models -- 6.3.2 Selection of covariance pattern -- 6.3.3 Assessing fixed effects -- 6.3.4 Model checking -- 6.4 Example: covariance pattern models for count data -- 6.4.1 Analysis models -- 6.4.2 Analysis using a categorical mixed model -- 6.5 Random coefficients models -- 6.5.1 Introduction -- 6.5.2 General points -- 6.5.3 Comparisons with fixed effects approaches -- 6.6 Examples of random coefficients models -- 6.6.1 A linear random coefficients model -- 6.6.2 A polynomial random coefficients model -- 6.7 Sample size estimation -- 6.7.1 Normal data -- 6.7.2 Binary data -- 6.7.3 Categorical data -- Chapter 7 Cross-over trials -- 7.1 Introduction -- 7.2 Advantages of mixed models in cross-over trials -- 7.3 The AB/BA cross-over trial -- 7.3.1 Example: AB/BA cross-over design -- 7.4 Higher order complete block designs -- 7.4.1 Inclusion of carry-over effects -- 7.4.2 Example: four-period, four-treatment cross-over trial -- 7.5 Incomplete block designs -- 7.5.1 Example: Three treatment two-period cross-over trial -- 7.6 Optimal designs -- 7.6.1 Example: Balaam's design -- 7.7 Covariance pattern models -- 7.7.1 Structured by period -- 7.7.2 Structured by treatment -- 7.7.3 Example: four-way cross-over trial -- 7.8 Analysis of binary data -- 7.9 Analysis of categorical data -- 7.10 Use of results from random effects models in trial design -- 7.10.1 Example -- 7.11 General points. , Chapter 8 Other applications of mixed models -- 8.1 Trials with repeated measurements within visits -- 8.1.1 Covariance pattern models -- 8.1.2 Example -- 8.1.3 Random coefficients models -- 8.1.4 Example: random coefficients models -- 8.2 Multi-centre trials with repeated measurements -- 8.2.1 Example: multi-centre hypertension trial -- 8.2.2 Covariance pattern models -- 8.3 Multi-centre cross-over trials -- 8.4 Hierarchical multi-centre trials and meta-analysis -- 8.5 Matched case -- control studies -- 8.5.1 Example -- 8.5.2 Analysis of a quantitative variable -- 8.5.3 Check of model assumptions -- 8.5.4 Analysis of binary Variables -- 8.6 Different variances for treatment groups in a simple between-patient trial -- 8.6.1 Example -- 8.7 Estimating variance components in an animal physiology trial -- 8.7.1 Sample size estimation for a future experiment -- 8.8 Inter- and intra-observer variation in foetal scan measurements -- 8.9 Components of variation and mean estimates in a cardiology experiment -- 8.10 Cluster sample surveys -- 8.10.1 Example: cluster sample survey -- 8.11 Small area mortality estimates -- 8.12 Estimating surgeon performance -- 8.13 Event history analysis -- 8.13.1 Example -- 8.14 A laboratory study using a within-subject 4 x 4 factorial design -- 8.15 Bioequivalence studies with replicate cross-over designs -- 8.15.1 Example -- 8.16 Cluster randomised trials -- 8.16.1 Example: A trial to evaluate integrated care pathways for treatment of children with asthma in hospital -- 8.16.2 Example: Edinburgh randomised trial of breast screening -- 8.17 Analysis of bilateral data -- 8.18 Incomplete block designs -- 8.18.1 Introduction -- 8.18.2 Balanced incomplete block (BIB) designs.

Weitere Ausg.: Erscheint auch als: Druck-Ausgabe Brown, Helen, 1962-. Applied mixed models in medicine

Sprache: Englisch

Schlagwort(e): Electronic books. ; Electronic books. ; Electronic books.

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118778210

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118778210

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118778210

UID:

Umfang: 1 online resource

ISBN: 9781118421574 , 1118421574 , 9781118419700 , 1118419707 , 9781118585481 , 1118585488 , 9781118707364 , 1118707362 , 1118297393 , 9781118297391 , 9781299385139 , 1299385133

Serie: The AFP fund development series

Inhalt: Essential tools for implementing right-sized prospect research techniques that help nonprofit organizations reach their fundraising goals Written especially for front-line fundraisers, Prospect Research for Fundraisers presents a practical understanding of prospect research, prospect management, and fundraising analytics, demonstrating how research can be used to raise more money. Filled with examples, case studies, interviews, and stories, this unique book is structured around the fundraising cycle and illustrates the myriad of current and ever-changing prospect research tools.

Anmerkung: Includes index. , Cover; Title Page; Copyright; Contents; Foreword; Acknowledgments; Introduction; Chapter 1 The Big Picture; What Is Prospect Research?; Overview of Terms; Why Do Prospect Research?; Prospect Research Informs Fundraising Strategy; How Does Research Fit into My Work and into the Gift Cycle?; Prospect Research in the Gift Cycle; Communication; Dispelling Myths and Corroborating Evidence; Summary; For Further Reading; Chapter 2 Identifying New Prospects; Overview: Why We Identify New Prospects; Capturing and Maintaining Constituent Information. , How Much Information Do You Capture on Each of Your Constituents?Basic Prospect Identification Terms; Prospecting Project Types; Manual Prospect Identification Methods; When Might You Undertake a Manual Prospecting Project?; What Are Some Tools a Researcher Might Use?; Manual Research Sample Case Studies; Wealth Screenings; What Kinds of Information Do Wealth-Screening Vendors Provide?; Preparing for a Wealth Screening; Setting the Purpose and Goal for an Electronic Screening; Selecting a Screening Product; Basic Information to Send to the Vendor; Screening Verification and Analysis. , Demographic/Psychographic Data AppendsRisk and Electronic Screenings; Peer Screenings; Surveying; Sample Projects a Researcher Might Do; Simple Data Mining; Sample Projects a Researcher Might Do; Advanced Data Mining; Sample Projects a Researcher Might Do; Donor Modeling; Sample Projects a Researcher Might Do; List Rental; Summary; For Further Reading; Chapter 3 Researching Prospects; Different Levels of Research; Types of Donor Profiles; Individual Profiles; Company Profiles; Foundation Profiles; Assembling Your Research Toolkit; Assess the Scope of Your Profile Needs; Fee versus Free. , How to Keep Up with Online ResourcesSearch Techniques Every Fundraiser Should Know; Step One: Find the Full Legal Name; Step Two: Become a Search-Engine Power User; Step Three: Find Giving History and Community Involvement; Step Four: Find Occupational Information; Step Five: Find General and Biographical Information; Other Search Concepts; Capacity Ratings: Putting the Information Together; Summary; For Further Reading; Chapter 4 Donor Relationship Management; What Is a Relationship Management System?; The Three Moving Pieces; Rating Your Best Prospects; Moving Your Prospects. , Reporting on Your ProgressCreating Your Own System; Tips on Entering Relationship Management Information; Creating Prospect Ratings; Recording Prospect Moves; Creating Reports to Track Progress; Summary; For Further Reading; Chapter 5 Managing Prospect Research; Managing to Your Fundraising Goals; As Part of Everyone's Job; As a Standalone Department or Staff Member; Hiring Employees and Volunteers; Employees; Volunteers; Skill Sets for Hiring; Managing Expectations; Defining Common Terms and Using Forms; Purchasing Resources; Search Tools; Analytics Tools; Summary Look-Up Tools.

Weitere Ausg.: Print version: Filla, Jennifer J., 1970- Prospect research for fundraisers. Hoboken, New Jersey : John Wiley & Sons, Inc., [2013] ISBN 9781118297391

Sprache: Englisch

Schlagwort(e): Electronic books. ; Electronic books. ; Electronic books.

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118707364

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118707364

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118707364

UID:

Umfang: 380 Seiten , 21 cm

ISBN: 9783868007725

Serie: Weltbild-Reader

Originaltitel: Cleo

Anmerkung: Lizenz des Deuticke-Verl., Wien

Sprache: Deutsch

Schlagwort(e): Familienkonflikt ; Bewältigung ; Katze ; Lebenshilfe ; Belletristische Darstellung ; Belletristische Darstellung ; Fiktionale Darstellung

Mehr zum Autor: Stumpf, Andrea

UID:

Umfang: 448 S.

ISBN: 9783863655945

Anmerkung: Aus dem Englischen

Sprache: Deutsch

UID:

Umfang: 448 Seiten , 19 cm

ISBN: 9783863655945

Anmerkung: Lizenz des Deuticke-Verl., Wien.

Sprache: Deutsch

Mehr zum Autor: Brown, Helen

Mehr zum Autor: Stumpf, Andrea

UID:

Umfang: 4 CD (295 Min.), Beiheft

ISBN: 9783837104769

Inhalt: Nach einem tragischen Unglück, bei dem sie ihren Sohn verliert, findet Helen Brown und ihre Familie durch eine kleine Katze wieder zurück ins Leben.

Sprache: Deutsch

UID:

Umfang: 4 CD (ca. 295 Min.) , Beil.

Ausgabe: Gekürzte, autoris. Lesefassung

ISBN: 9783837104769

Inhalt: Sam, der 8-jährige Sohn der Autorin, stirbt bei einem Unglück. Doch kurz zuvor hat Sam sich ein Kätzchen ausgesucht, und das kann Helen Brown nicht ablehnen. Das winzige kränkliche Fellbündel, Cleo getauft, erweist sich als Glücksfall für alle Beteiligten.

Sprache: Deutsch

Schlagwort(e): Kind ; Tod ; Trauer ; Trauerarbeit ; Katze ; Lebensbejahung ; Belletristische Darstellung ; CD ; Belletristische Darstellung ; CD ; Fiktionale Darstellung

UID:

Umfang: 1 online resource (187 p.)

ISBN: 9781446251393 (ebook) :

Inhalt: Working with lesbians and gay men is a largely neglected area of social work practice. This book provides social workers and other professionals with an overview of a number of key challenges and concerns that play a significant part in the lives of lesbians and gay men.

Weitere Ausg.: Print version : ISBN 9781847873910

Sprache: Englisch

URL: SAGE knowledge