標題: Titlebook: Clinical Prediction Models; A Practical Approach Ewout W. Steyerberg Book 2019Latest edition Springer Nature Switzerland AG 2019 An?sthesie [打印本頁] 作者: 從未迷惑 時間: 2025-3-21 17:30
書目名稱Clinical Prediction Models影響因子(影響力)
書目名稱Clinical Prediction Models影響因子(影響力)學科排名
書目名稱Clinical Prediction Models網(wǎng)絡公開度
書目名稱Clinical Prediction Models網(wǎng)絡公開度學科排名
書目名稱Clinical Prediction Models被引頻次
書目名稱Clinical Prediction Models被引頻次學科排名
書目名稱Clinical Prediction Models年度引用
書目名稱Clinical Prediction Models年度引用學科排名
書目名稱Clinical Prediction Models讀者反饋
書目名稱Clinical Prediction Models讀者反饋學科排名
作者: TATE 時間: 2025-3-21 20:40
Book 2019Latest editions a practical checklist that needs to be considered for development of avalid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters wit作者: set598 時間: 2025-3-22 00:26
Ewout W. SteyerbergFeatures, in this new edition, a discussion of Big Data and its implications of the design of prediction models.Includes, in this new edition, new case studies, more simulations with missing "y" value作者: 有節(jié)制 時間: 2025-3-22 07:18
Statistics for Biology and Healthhttp://image.papertrans.cn/c/image/228182.jpg作者: annexation 時間: 2025-3-22 11:05 作者: Processes 時間: 2025-3-22 15:04
978-3-030-16401-0Springer Nature Switzerland AG 2019作者: Processes 時間: 2025-3-22 20:28
Clinical Prediction Models978-3-030-16399-0Series ISSN 1431-8776 Series E-ISSN 2197-5671 作者: 補角 時間: 2025-3-22 22:09
Supporting Marketing Applications, medicine. Predictions come from statistical models or?algorithms that may be developed with more stringent or more relaxed assumptions. The reliability of predictions suffers from aleatory and epistemic uncertainty, which relate to sample size and broader epidemiologic concepts of generalizability. Finally, the structure of the book is presented.作者: arcane 時間: 2025-3-23 02:28 作者: peritonitis 時間: 2025-3-23 06:26 作者: laceration 時間: 2025-3-23 12:48
Supporting Marketing Applications, medicine. Predictions come from statistical models or?algorithms that may be developed with more stringent or more relaxed assumptions. The reliability of predictions suffers from aleatory and epistemic uncertainty, which relate to sample size and broader epidemiologic concepts of generalizability.作者: esthetician 時間: 2025-3-23 15:43 作者: Intentional 時間: 2025-3-23 21:11
https://doi.org/10.1007/978-1-4020-2131-2urvival data. We discuss common statistical models in medical research such as the linear, logistic, and Cox regression model. We consider simpler approaches and more flexible extensions, including regression trees and neural networks. We also discuss competing risks and the concept of dynamic predi作者: avarice 時間: 2025-3-24 00:09 作者: 腐蝕 時間: 2025-3-24 04:59 作者: Kaleidoscope 時間: 2025-3-24 09:54
https://doi.org/10.1007/978-3-319-16492-2 predict. Traditional complete case analysis suffers from inefficiency, selection bias of subjects, and other limitations when developing a prediction model. We briefly review the theoretical background on mechanisms of missingness of predictor values and how these may affect prediction models. We f作者: ATRIA 時間: 2025-3-24 13:23
SpringerBriefs in Molecular Scienceal studies were available to: (a) quantify predictor effects; (b) develop and validate prognostic models. Missing values were a key issue. Some values were systematically missing per study, since few studies recorded all predictors of interest. The use of single and multiple imputation methods is il作者: Maximizer 時間: 2025-3-24 16:55
https://doi.org/10.1007/978-3-319-16492-2e for entering in regression models and must first be inspected and managed before the?statistical analysis starts. As in any data analysis, we will usually start with obtaining an impression of the data under study, such as the occurrence of missing values and the distribution of predictors and out作者: DEBT 時間: 2025-3-24 19:33
J. D. B. Lambert,K. K. Kadyrzhanovrther analysis, in particular if our data set is relatively small.?A small sample size leads to problems as discussed in Chap. ., such as limited power to test effects of potential predictors, and too extreme predictions when predictions are based on the standard regression coefficients (overfitting作者: 神經(jīng) 時間: 2025-3-24 23:19 作者: unstable-angina 時間: 2025-3-25 06:03
J. D. B. Lambert,K. K. Kadyrzhanovch can be assessed with interaction terms. We also consider the linearity assumption of continuous predictors in a multivariable regression model, where multiple nonlinear terms can be included to allow for nonlinear relations between predictors and outcome. Throughout we stress parsimony in strateg作者: ALIAS 時間: 2025-3-25 08:29
Nato Security through Science Series C:hods. These modern estimation methods include uniform shrinkage methods (heuristic or bootstrap based) and penalized maximum likelihood methods (with various forms of penalty, including ridge regression and the LASSO).作者: 憤怒歷史 時間: 2025-3-25 12:15
https://doi.org/10.1007/978-3-319-53457-2 information from published studies.?The aim is to develop a global model, which has broad applicability. Such a model might be informed by a meta-analysis based on individual patient data (IPD) from multiple studies. We illustrate this approach with a meta-analysis of 15 studies of traumatic brain 作者: 狂熱語言 時間: 2025-3-25 16:56
siRNA Therapeutics to Treat Liver Disorders,re absolute risks, which go beyond assessments of relative risks, such as regression coefficients, odds ratios, or hazard ratios. We can distinguish apparent, internally validated, and externally validated model performance (Chap.?.). For all types of validation, we need performance criteria in line作者: generic 時間: 2025-3-25 22:42 作者: 極力證明 時間: 2025-3-26 02:24
https://doi.org/10.1007/978-94-011-4878-8an to learn for the future. Validation, hence, is an important aspect of the process of predictive modeling. An important distinction is between apparent, internal and external validation. In this chapter, we focus on internal and external validation techniques, with illustrations in case studies.作者: 委托 時間: 2025-3-26 06:30 作者: 懲罰 時間: 2025-3-26 11:39
Applications of Prediction ModelsIn this chapter, we consider several areas of application of prediction models in public health, clinical practice,?and medical research. We use several small case studies for illustration.作者: Gastric 時間: 2025-3-26 15:22 作者: 樹木心 時間: 2025-3-26 18:34
Modern Estimation Methodshods. These modern estimation methods include uniform shrinkage methods (heuristic or bootstrap based) and penalized maximum likelihood methods (with various forms of penalty, including ridge regression and the LASSO).作者: 畏縮 時間: 2025-3-26 22:56 作者: Metastasis 時間: 2025-3-27 03:23
Introduction, medicine. Predictions come from statistical models or?algorithms that may be developed with more stringent or more relaxed assumptions. The reliability of predictions suffers from aleatory and epistemic uncertainty, which relate to sample size and broader epidemiologic concepts of generalizability.作者: 夜晚 時間: 2025-3-27 06:30 作者: 尖酸一點 時間: 2025-3-27 09:53 作者: strain 時間: 2025-3-27 17:39 作者: abnegate 時間: 2025-3-27 18:15 作者: 埋葬 時間: 2025-3-28 01:08
Missing Values predict. Traditional complete case analysis suffers from inefficiency, selection bias of subjects, and other limitations when developing a prediction model. We briefly review the theoretical background on mechanisms of missingness of predictor values and how these may affect prediction models. We f作者: ENNUI 時間: 2025-3-28 04:02 作者: jumble 時間: 2025-3-28 06:44
Coding of Categorical and Continuous Predictorse for entering in regression models and must first be inspected and managed before the?statistical analysis starts. As in any data analysis, we will usually start with obtaining an impression of the data under study, such as the occurrence of missing values and the distribution of predictors and out作者: Tdd526 時間: 2025-3-28 10:51
Restrictions on Candidate Predictorsrther analysis, in particular if our data set is relatively small.?A small sample size leads to problems as discussed in Chap. ., such as limited power to test effects of potential predictors, and too extreme predictions when predictions are based on the standard regression coefficients (overfitting作者: 種族被根除 時間: 2025-3-28 16:03 作者: 方便 時間: 2025-3-28 20:52
Assumptions in Regression Models: Additivity and Linearitych can be assessed with interaction terms. We also consider the linearity assumption of continuous predictors in a multivariable regression model, where multiple nonlinear terms can be included to allow for nonlinear relations between predictors and outcome. Throughout we stress parsimony in strateg作者: 圣人 時間: 2025-3-28 23:29 作者: 人類 時間: 2025-3-29 04:50 作者: LAIR 時間: 2025-3-29 10:31
Evaluation of Performancere absolute risks, which go beyond assessments of relative risks, such as regression coefficients, odds ratios, or hazard ratios. We can distinguish apparent, internally validated, and externally validated model performance (Chap.?.). For all types of validation, we need performance criteria in line作者: Incommensurate 時間: 2025-3-29 13:04
Evaluation of Clinical Usefulness model is useful to support medical decision-making: is the model beneficial to guide selection of subjects for screening, for diagnostic work-up, or decision-making on therapy? For such decisions, we need a cutoff for the predicted probability (“decision threshold”, or “classification cutoff”, see 作者: Conduit 時間: 2025-3-29 17:58 作者: 獨白 時間: 2025-3-29 19:57 作者: prostatitis 時間: 2025-3-30 03:07 作者: lipids 時間: 2025-3-30 04:55
https://doi.org/10.1007/978-1-4020-2131-2ction modeling. We focus on the most relevant aspects of these models in a prediction context. All models are illustrated with case studies. In Chap. ., we will discuss aspects of choosing between alternative statistical models for the same type of outcome.作者: Introduction 時間: 2025-3-30 10:38
https://doi.org/10.1007/978-3-319-16492-2come. Descriptive analyses, such as frequency tables and graphical displays, are useful to this aim. We will consider various issues in coding of unordered and ordered categorical predictors. For continuous predictors, we specifically discuss how we can limit the influence of outliers and interpret regression coefficients.作者: accrete 時間: 2025-3-30 16:11 作者: 持久 時間: 2025-3-30 18:31
J. D. B. Lambert,K. K. Kadyrzhanovies to extend a prediction model with interactions and nonlinear terms, since better fulfillment of assumptions in a particular sample does not necessarily imply better predictive performance for future subjects. We consider several case studies for illustration of strategies to deal with additivity and linearity.作者: 沒有貧窮 時間: 2025-3-30 23:46 作者: Infect 時間: 2025-3-31 01:25 作者: 蒼白 時間: 2025-3-31 05:29 作者: 有抱負者 時間: 2025-3-31 11:25
SpringerBriefs in Molecular Science were systematically missing per study, since few studies recorded all predictors of interest. The use of single and multiple imputation methods is illustrated with a detailed description of the analyses in R software.作者: 聯(lián)想 時間: 2025-3-31 14:33 作者: Living-Will 時間: 2025-3-31 19:36 作者: scrutiny 時間: 2025-3-31 23:56
Conference proceedings 20061st editiontween centers, if we would use the observed average outcomes per center as predictions of mortality.?Bootstrap resampling is presented as a central technique to correct overfitting and quantify optimism in model performance.作者: 繞著哥哥問 時間: 2025-4-1 03:39 作者: 隱語 時間: 2025-4-1 07:02
J. D. B. Lambert,K. K. Kadyrzhanovrs based on subject knowledge, considering distributions of predictors, combining similar variables, and averaging the effects of similar variables. We provide a detailed description of a case study of modeling similar effects of aspects of family history for robust prediction of the presence of a genetic mutation.
