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Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
| Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash |
| Autore | Lash Timothy L. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (475 pages) |
| Disciplina | 614.4072 |
| Collana | Statistics for Biology and Health |
| Soggetto topico |
Epidemiology - Research
Social sciences - Methodology Epidemiologia Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-82673-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction, Objectives, and an Alternative -- Introduction: Biases in Health Research -- Statistical Inference in Public Health Research -- The Treatment of Uncertainty in Nonrandomized Research -- When Bias Analysis Will Be Most Useful -- Judgments Under Uncertainty -- The Dual-Process Model of Cognition -- Anchoring and Adjustment -- Overconfidence -- Failure to Account for the Base-Rate -- Conclusion -- References -- Chapter 2: A Guide to Implementing Quantitative Bias Analysis -- Introduction -- Reducing Error -- Reducing Error by Design -- Reducing Error in the Analysis -- Quantifying Error -- Evaluating the Potential Value of Quantitative Bias Analysis? -- Planning for Bias Analysis -- Creating a Data Collection Plan for Bias Analysis -- Creating an Analytic Plan for a Bias Analysis -- Type of Data: Record-Level Versus Summary -- Type of Bias Analysis -- Order of Bias Analysis Adjustments -- Bias Analysis Techniques -- Simple Bias Analysis -- Multidimensional Bias Analysis -- Probabilistic Bias Analysis -- Multiple Bias Modeling -- Direct Bias Modeling and Missing Data Methods -- Bayesian Bias Analysis -- Assigning Values and Distributions to Bias Parameters -- Directed Acyclic Graphs -- Conclusion -- References -- Chapter 3: Data Sources for Bias Analysis -- Bias Parameters -- Internal Data Sources -- Selection Bias -- Uncontrolled Confounding -- Information Bias -- Design of Internal Validation Studies -- Limitations of Internal Validation Studies -- External Data Sources -- Selection Bias -- Unmeasured Confounder -- Information Bias -- Expert Opinion -- Summary -- References -- Chapter 4: Selection Bias -- Introduction -- Definitions and Terms -- Conceptual -- Depicting Selection Bias Using Causal Graphs -- Design Considerations -- Bias Analysis.
Motivation for Bias Analysis -- Sources of Data -- Simple Bias-Adjustment for Differential Initial Participation -- Example -- Introduction to Bias Analysis -- Bias Analysis by Projecting the Exposed Proportion Among Nonparticipants -- Bias Analysis Using Selection Proportions -- Bias Analysis Using Inverse Probability of Participation Weighting -- Simple Bias-Adjustment for Differential Loss-to-Follow-up -- Example -- Bias Analysis by Modeling Outcomes -- Bias Analysis by Inverse Probability of Attrition Weighting -- Multidimensional Bias Analysis for Selection Bias -- Example -- References -- Chapter 5: Uncontrolled Confounders -- Introduction -- Key Concepts -- Definitions -- Motivation for Bias Analysis -- Data Sources -- Introduction to Simple Bias Analysis -- Approach -- Introduction to the Example -- Bias Parameters -- Implementation of Simple Bias Analysis -- Ratio Measures -- Example -- Difference Measures -- Person-time Designs -- Unmeasured Confounder in the Presence of Effect Measure Modification -- Polytomous Confounders -- Multidimensional Bias Analysis for Unmeasured Confounding -- Example -- Bounding the Bias Limits of an Unmeasured Confounding -- Analytic Approach -- The E-Value and G-Value -- Signed Directed Acyclic Graphs to Estimate the Direction of Bias -- References -- Chapter 6: Misclassification -- Introduction -- Definitions and Terms -- Differential vs. Nondifferential Misclassification -- Dependent vs. Independent Misclassification -- Directed Acyclic Graphs and Misclassification -- Calculating Classification Bias Parameters from Validation Data -- Sources of Data -- Bias Analysis of Exposure Misclassification -- Bias-Adjusting for Exposure Misclassification Using Sensitivity and Specificity: Nondifferential and Independent Errors -- Bias-Adjusting for Exposure Misclassification Using Predictive Values. Bias-Adjustment for Nondifferential Outcome Misclassification Using Positive Predictive Values for the Risk Ratio Measure of A... -- Bias-Adjustments Using Sensitivity and Specificity: Differential Independent Errors -- Bias-Adjustments Using Sensitivity and Specificity: Internal Validation Data -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Disease Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Independent Errors -- Disease Misclassification in Case-Control Studies -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Covariate Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Differential Misclassification with Independent Errors -- Overreliance on Nondifferential Misclassification of Covariates Biasing Toward the Null -- Dependent Misclassification -- Matrix Method for Misclassification Adjustment -- Multidimensional Bias Analysis for Misclassification -- Limitations -- Negative Expected Cell Frequencies -- Other Considerations -- References -- Chapter 7: Preparing for Probabilistic Bias Analysis -- Introduction -- Preparing for Probabilistic Bias Analysis -- Statistical Software for Probabilistic Bias Analysis -- Summary Level Versus Record Level Probabilistic Bias Analysis -- Describing Uncertainty in the Bias Parameters -- Probability Distributions -- Uniform Distribution -- Generalized Method for Sampling from Distributions -- Trapezoidal Distribution -- Triangular Distribution -- Normal Distribution -- Beta Distribution -- Bernoulli and Binomial Distributions -- Other Probability Distributions -- Sensitivity to Chosen Distributions -- Correlated Distributions -- Conclusions -- References -- Chapter 8: Probabilistic Bias Analysis for Simulation of Summary Level Data -- Introduction. Analytic Approach for Summary Level Probabilistic Bias Analysis -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Incorporate Conventional Random Error by Sampling Summary Statistics -- Step 4c (Alternate): Resample the Prevalence of Misclassification Adjusted Exposure -- Step 5: Save the Bias-Adjusted Estimate and Repeat Steps 4a-c -- Step 6: Summarize the Bias-Adjusted Estimates with a Frequency Distribution that Yields a Central Tendency and Simulation Inte... -- Misclassification Implementation: Predictive Values -- Misclassification Implementation: Predictive Values - Alternative -- Misclassification of Outcomes and Confounders -- Uncontrolled Confounding Implementation -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Confounding Implementation Alternative: Relative Risk Due to Confounding -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions. Step 4b: Generate Bias-Adjusted Results Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Implementation -- An Example of Probabilistic Bias Analysis in the Presence of Substantial Source Population Data -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Adjustment Using Selection Probabilities -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Computing Issues with Summary Level Probabilistic Bias Analysis -- Bootstrapping -- Impossible Values for Bias Parameters and Model Diagnostic Plots -- Conclusions -- Appendix: Sampling Models for Exposure Misclassification -- References -- Chapter 9: Probabilistic Bias Analysis for Simulation of Record-Level Data -- Introduction -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters. Step 3: Assign Probability Distributions to Each Bias Parameter. |
| Record Nr. | UNINA-9910556891103321 |
Lash Timothy L.
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
| Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash |
| Autore | Lash Timothy L. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (475 pages) |
| Disciplina | 614.4072 |
| Collana | Statistics for Biology and Health |
| Soggetto topico |
Epidemiology - Research
Social sciences - Methodology Epidemiologia Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-82673-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction, Objectives, and an Alternative -- Introduction: Biases in Health Research -- Statistical Inference in Public Health Research -- The Treatment of Uncertainty in Nonrandomized Research -- When Bias Analysis Will Be Most Useful -- Judgments Under Uncertainty -- The Dual-Process Model of Cognition -- Anchoring and Adjustment -- Overconfidence -- Failure to Account for the Base-Rate -- Conclusion -- References -- Chapter 2: A Guide to Implementing Quantitative Bias Analysis -- Introduction -- Reducing Error -- Reducing Error by Design -- Reducing Error in the Analysis -- Quantifying Error -- Evaluating the Potential Value of Quantitative Bias Analysis? -- Planning for Bias Analysis -- Creating a Data Collection Plan for Bias Analysis -- Creating an Analytic Plan for a Bias Analysis -- Type of Data: Record-Level Versus Summary -- Type of Bias Analysis -- Order of Bias Analysis Adjustments -- Bias Analysis Techniques -- Simple Bias Analysis -- Multidimensional Bias Analysis -- Probabilistic Bias Analysis -- Multiple Bias Modeling -- Direct Bias Modeling and Missing Data Methods -- Bayesian Bias Analysis -- Assigning Values and Distributions to Bias Parameters -- Directed Acyclic Graphs -- Conclusion -- References -- Chapter 3: Data Sources for Bias Analysis -- Bias Parameters -- Internal Data Sources -- Selection Bias -- Uncontrolled Confounding -- Information Bias -- Design of Internal Validation Studies -- Limitations of Internal Validation Studies -- External Data Sources -- Selection Bias -- Unmeasured Confounder -- Information Bias -- Expert Opinion -- Summary -- References -- Chapter 4: Selection Bias -- Introduction -- Definitions and Terms -- Conceptual -- Depicting Selection Bias Using Causal Graphs -- Design Considerations -- Bias Analysis.
Motivation for Bias Analysis -- Sources of Data -- Simple Bias-Adjustment for Differential Initial Participation -- Example -- Introduction to Bias Analysis -- Bias Analysis by Projecting the Exposed Proportion Among Nonparticipants -- Bias Analysis Using Selection Proportions -- Bias Analysis Using Inverse Probability of Participation Weighting -- Simple Bias-Adjustment for Differential Loss-to-Follow-up -- Example -- Bias Analysis by Modeling Outcomes -- Bias Analysis by Inverse Probability of Attrition Weighting -- Multidimensional Bias Analysis for Selection Bias -- Example -- References -- Chapter 5: Uncontrolled Confounders -- Introduction -- Key Concepts -- Definitions -- Motivation for Bias Analysis -- Data Sources -- Introduction to Simple Bias Analysis -- Approach -- Introduction to the Example -- Bias Parameters -- Implementation of Simple Bias Analysis -- Ratio Measures -- Example -- Difference Measures -- Person-time Designs -- Unmeasured Confounder in the Presence of Effect Measure Modification -- Polytomous Confounders -- Multidimensional Bias Analysis for Unmeasured Confounding -- Example -- Bounding the Bias Limits of an Unmeasured Confounding -- Analytic Approach -- The E-Value and G-Value -- Signed Directed Acyclic Graphs to Estimate the Direction of Bias -- References -- Chapter 6: Misclassification -- Introduction -- Definitions and Terms -- Differential vs. Nondifferential Misclassification -- Dependent vs. Independent Misclassification -- Directed Acyclic Graphs and Misclassification -- Calculating Classification Bias Parameters from Validation Data -- Sources of Data -- Bias Analysis of Exposure Misclassification -- Bias-Adjusting for Exposure Misclassification Using Sensitivity and Specificity: Nondifferential and Independent Errors -- Bias-Adjusting for Exposure Misclassification Using Predictive Values. Bias-Adjustment for Nondifferential Outcome Misclassification Using Positive Predictive Values for the Risk Ratio Measure of A... -- Bias-Adjustments Using Sensitivity and Specificity: Differential Independent Errors -- Bias-Adjustments Using Sensitivity and Specificity: Internal Validation Data -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Disease Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Independent Errors -- Disease Misclassification in Case-Control Studies -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Covariate Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Differential Misclassification with Independent Errors -- Overreliance on Nondifferential Misclassification of Covariates Biasing Toward the Null -- Dependent Misclassification -- Matrix Method for Misclassification Adjustment -- Multidimensional Bias Analysis for Misclassification -- Limitations -- Negative Expected Cell Frequencies -- Other Considerations -- References -- Chapter 7: Preparing for Probabilistic Bias Analysis -- Introduction -- Preparing for Probabilistic Bias Analysis -- Statistical Software for Probabilistic Bias Analysis -- Summary Level Versus Record Level Probabilistic Bias Analysis -- Describing Uncertainty in the Bias Parameters -- Probability Distributions -- Uniform Distribution -- Generalized Method for Sampling from Distributions -- Trapezoidal Distribution -- Triangular Distribution -- Normal Distribution -- Beta Distribution -- Bernoulli and Binomial Distributions -- Other Probability Distributions -- Sensitivity to Chosen Distributions -- Correlated Distributions -- Conclusions -- References -- Chapter 8: Probabilistic Bias Analysis for Simulation of Summary Level Data -- Introduction. Analytic Approach for Summary Level Probabilistic Bias Analysis -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Incorporate Conventional Random Error by Sampling Summary Statistics -- Step 4c (Alternate): Resample the Prevalence of Misclassification Adjusted Exposure -- Step 5: Save the Bias-Adjusted Estimate and Repeat Steps 4a-c -- Step 6: Summarize the Bias-Adjusted Estimates with a Frequency Distribution that Yields a Central Tendency and Simulation Inte... -- Misclassification Implementation: Predictive Values -- Misclassification Implementation: Predictive Values - Alternative -- Misclassification of Outcomes and Confounders -- Uncontrolled Confounding Implementation -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Confounding Implementation Alternative: Relative Risk Due to Confounding -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions. Step 4b: Generate Bias-Adjusted Results Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Implementation -- An Example of Probabilistic Bias Analysis in the Presence of Substantial Source Population Data -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Adjustment Using Selection Probabilities -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Computing Issues with Summary Level Probabilistic Bias Analysis -- Bootstrapping -- Impossible Values for Bias Parameters and Model Diagnostic Plots -- Conclusions -- Appendix: Sampling Models for Exposure Misclassification -- References -- Chapter 9: Probabilistic Bias Analysis for Simulation of Record-Level Data -- Introduction -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters. Step 3: Assign Probability Distributions to Each Bias Parameter. |
| Record Nr. | UNISA-996466557403316 |
|
Lash Timothy L.
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Basic principles and practical applications in epidemiological research [[electronic resource] /] / Jung-Der Wang
| Basic principles and practical applications in epidemiological research [[electronic resource] /] / Jung-Der Wang |
| Autore | Wang Jung-Der |
| Pubbl/distr/stampa | Singapore ; ; River Edge, N.J., : World Scientific, c2002 |
| Descrizione fisica | 1 online resource (379 p.) |
| Disciplina | 614.4072 |
| Collana | Quantitative sciences on biology and medicine |
| Soggetto topico |
Epidemiology - Research
Epidemiology |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-92834-8
9786611928346 981-277-572-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; 1 Introduction to Epidemiological Research; 1.1 Definition of epidemiology; 1.2 Evolving trends of epidemiological research; 1.3 Types of inferences in epidemiological research; 1.4 Outline of the basic principles of epidemiological research; 1.5 Summary; Quiz of Chapter 1; 2 Principles of Scientific Research: Deductive Methods and Process of Conjecture and Refutation; 2.1 The process of scientific research; 2.2 Deductive methods: Common logical reasoning; 2.3 Conjectures and Refutations; 2.4 Why take a refutational attitude?
2.5 The limitations of conjectures and refutations2.6 Summary; Quiz of Chapter 2; 3 Scientific Hypothesis and Degree of Corroboration; 3.1 Hypothesis Formation - How to form a conjecture?; 3.2 What makes a hypothesis scientific?; 3.3 Successful refutation and auxiliary hypotheses - Has one disproved the primary hypothesis?; 3.4 Failure to falsify and degree of corroboration - Do the results of the study corroborate the primary hypothesis?; 3.5 Credibility of a hypothesis and decision-making; 3.6 Summary; Quiz of Chapter 3; 4 Causal Inference and Decision 4.1 Causal concepts in medicine and public health4.2 Proposed criteria for causal decisions; 4.2.1 Necessary criteria; 4.2.2 Quasi-necessary criteria; 4.2.3 Other supportive criteria; 4.3 Objective knowledge and consensus method; 4.4 Summary; Quiz of Chapter 4; 5 Basic Principles of Measurement; 5.1 What is measurement?; 5.2 Why does one perform measurement?; 5.3 How does one measure?; 5.3.1 Measurements in socio-behavioral science; 5.4 Accuracy of measurement: Validity and reliability; 5.5 Scales of measurement; 5.5.1 Nominal scale: A scale of qualitative measurement 5.5.2 Ordinal scale: A scale of semi-quantitative measurement5.5.3 Interval scale: Quantitative measurement with or without an absolute zero starting point; 5.5.4 Ratio scale: Quantitative measurement with an absolute zero starting point; 5.6 Common evaluation method in medical diagnostic tests; 5.7 Validity and reliability of physico-chemical, biological and socio-behavioral measurements from a refutationist's point of view; 5.7.1 Measurement of chemicals in the environment or inside the human body 5.7.2 Conceptualization of exposure dose and its measurement in occupational and environmental medicine5.7.3 Validity and reliability of socio-behavioral measurement; 5.8 How to perform accurate measurement by questionnaire Limitations of questionnaire information; 5.8.1 Construction of a questionnaire; 5.8.2 Interview procedures; 5.9 Summary; Quiz of Chapter 5; 6 Basic Measurements in Epidemiological Research; 6.1 Evolving trends in epidemiological measurement; 6.2 Basic measurements of outcome in epidemiology 6.2.1 Outcome measurement: Counting of events and states, rate, proportion, and ratio |
| Record Nr. | UNINA-9910454070703321 |
|
Wang Jung-Der
|
||
| Singapore ; ; River Edge, N.J., : World Scientific, c2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Basic principles and practical applications in epidemiological research [[electronic resource] /] / Jung-Der Wang
| Basic principles and practical applications in epidemiological research [[electronic resource] /] / Jung-Der Wang |
| Autore | Wang Jung-Der |
| Pubbl/distr/stampa | Singapore ; ; River Edge, N.J., : World Scientific, c2002 |
| Descrizione fisica | 1 online resource (379 p.) |
| Disciplina | 614.4072 |
| Collana | Quantitative sciences on biology and medicine |
| Soggetto topico |
Epidemiology - Research
Epidemiology |
| ISBN |
1-281-92834-8
9786611928346 981-277-572-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; 1 Introduction to Epidemiological Research; 1.1 Definition of epidemiology; 1.2 Evolving trends of epidemiological research; 1.3 Types of inferences in epidemiological research; 1.4 Outline of the basic principles of epidemiological research; 1.5 Summary; Quiz of Chapter 1; 2 Principles of Scientific Research: Deductive Methods and Process of Conjecture and Refutation; 2.1 The process of scientific research; 2.2 Deductive methods: Common logical reasoning; 2.3 Conjectures and Refutations; 2.4 Why take a refutational attitude?
2.5 The limitations of conjectures and refutations2.6 Summary; Quiz of Chapter 2; 3 Scientific Hypothesis and Degree of Corroboration; 3.1 Hypothesis Formation - How to form a conjecture?; 3.2 What makes a hypothesis scientific?; 3.3 Successful refutation and auxiliary hypotheses - Has one disproved the primary hypothesis?; 3.4 Failure to falsify and degree of corroboration - Do the results of the study corroborate the primary hypothesis?; 3.5 Credibility of a hypothesis and decision-making; 3.6 Summary; Quiz of Chapter 3; 4 Causal Inference and Decision 4.1 Causal concepts in medicine and public health4.2 Proposed criteria for causal decisions; 4.2.1 Necessary criteria; 4.2.2 Quasi-necessary criteria; 4.2.3 Other supportive criteria; 4.3 Objective knowledge and consensus method; 4.4 Summary; Quiz of Chapter 4; 5 Basic Principles of Measurement; 5.1 What is measurement?; 5.2 Why does one perform measurement?; 5.3 How does one measure?; 5.3.1 Measurements in socio-behavioral science; 5.4 Accuracy of measurement: Validity and reliability; 5.5 Scales of measurement; 5.5.1 Nominal scale: A scale of qualitative measurement 5.5.2 Ordinal scale: A scale of semi-quantitative measurement5.5.3 Interval scale: Quantitative measurement with or without an absolute zero starting point; 5.5.4 Ratio scale: Quantitative measurement with an absolute zero starting point; 5.6 Common evaluation method in medical diagnostic tests; 5.7 Validity and reliability of physico-chemical, biological and socio-behavioral measurements from a refutationist's point of view; 5.7.1 Measurement of chemicals in the environment or inside the human body 5.7.2 Conceptualization of exposure dose and its measurement in occupational and environmental medicine5.7.3 Validity and reliability of socio-behavioral measurement; 5.8 How to perform accurate measurement by questionnaire Limitations of questionnaire information; 5.8.1 Construction of a questionnaire; 5.8.2 Interview procedures; 5.9 Summary; Quiz of Chapter 5; 6 Basic Measurements in Epidemiological Research; 6.1 Evolving trends in epidemiological measurement; 6.2 Basic measurements of outcome in epidemiology 6.2.1 Outcome measurement: Counting of events and states, rate, proportion, and ratio |
| Record Nr. | UNINA-9910782280103321 |
|
Wang Jung-Der
|
||
| Singapore ; ; River Edge, N.J., : World Scientific, c2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biostatistical methods in epidemiology [[electronic resource] /] / Stephen C. Newman
| Biostatistical methods in epidemiology [[electronic resource] /] / Stephen C. Newman |
| Autore | Newman Stephen C. <1952-> |
| Pubbl/distr/stampa | New York, : John Wiley & Sons, c2001 |
| Descrizione fisica | 1 online resource (403 p.) |
| Disciplina |
614.4/07/27
614.4072 614.40727 |
| Collana | Wiley series in probability and statistics. Biostatistics section |
| Soggetto topico |
Epidemiology - Statistical methods
Cohort analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-36696-6
9786610366965 0-470-35001-6 0-471-46160-1 0-471-27261-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Biostatistical Methods in Epidemiology; Contents; Preface; 1. Introduction; 1.1 Probability; 1.2 Parameter Estimation; 1.3 Random Sampling; 2. Measurement Issues in Epidemiology; 2.1 Systematic and Random Error; 2.2 Measures of Effect; 2.3 Confounding; 2.4 Collapsibility Approach to Confounding; 2.5 Counterfactual Approach to Confounding; 2.6 Methods to Control Confounding; 2.7 Bias Due to an Unknown Confounder; 2.8 Misclassification; 2.9 Scope of this Book; 3. Binomial Methods for Single Sample Closed Cohort Data; 3.1 Exact Methods; 3.2 Asymptotic Methods
10. Poisson Methods for Censored Survival Data10.1 Poisson Methods for Single Sample Survival Data; 10.2 Poisson Methods for Unstratified Survival Data; 10.3 Poisson Methods for Stratified Survival Data; 11. Odds Ratio Methods for Case-Control Data; 11.1 Justification of the Odds Ratio Approach; 11.2 Odds Ratio Methods for Matched-Pairs Case-Control Data; 11.3 Odds Ratio Methods for (1 : M) Matched Case-Control Data; 12. Standardized Rates and Age-Period-Cohort Analysis; 12.1 Population Rates; 12.2 Directly Standardized Death Rate; 12.3 Standardized Mortality Ratio 12.4 Age-Period-Cohort Analysis |
| Record Nr. | UNINA-9910142503103321 |
|
Newman Stephen C. <1952->
|
||
| New York, : John Wiley & Sons, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biostatistical methods in epidemiology [[electronic resource] /] / Stephen C. Newman
| Biostatistical methods in epidemiology [[electronic resource] /] / Stephen C. Newman |
| Autore | Newman Stephen C. <1952-> |
| Pubbl/distr/stampa | New York, : John Wiley & Sons, c2001 |
| Descrizione fisica | 1 online resource (403 p.) |
| Disciplina |
614.4/07/27
614.4072 614.40727 |
| Collana | Wiley series in probability and statistics. Biostatistics section |
| Soggetto topico |
Epidemiology - Statistical methods
Cohort analysis |
| ISBN |
1-280-36696-6
9786610366965 0-470-35001-6 0-471-46160-1 0-471-27261-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Biostatistical Methods in Epidemiology; Contents; Preface; 1. Introduction; 1.1 Probability; 1.2 Parameter Estimation; 1.3 Random Sampling; 2. Measurement Issues in Epidemiology; 2.1 Systematic and Random Error; 2.2 Measures of Effect; 2.3 Confounding; 2.4 Collapsibility Approach to Confounding; 2.5 Counterfactual Approach to Confounding; 2.6 Methods to Control Confounding; 2.7 Bias Due to an Unknown Confounder; 2.8 Misclassification; 2.9 Scope of this Book; 3. Binomial Methods for Single Sample Closed Cohort Data; 3.1 Exact Methods; 3.2 Asymptotic Methods
10. Poisson Methods for Censored Survival Data10.1 Poisson Methods for Single Sample Survival Data; 10.2 Poisson Methods for Unstratified Survival Data; 10.3 Poisson Methods for Stratified Survival Data; 11. Odds Ratio Methods for Case-Control Data; 11.1 Justification of the Odds Ratio Approach; 11.2 Odds Ratio Methods for Matched-Pairs Case-Control Data; 11.3 Odds Ratio Methods for (1 : M) Matched Case-Control Data; 12. Standardized Rates and Age-Period-Cohort Analysis; 12.1 Population Rates; 12.2 Directly Standardized Death Rate; 12.3 Standardized Mortality Ratio 12.4 Age-Period-Cohort Analysis |
| Record Nr. | UNINA-9910830319303321 |
|
Newman Stephen C. <1952->
|
||
| New York, : John Wiley & Sons, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biostatistical methods in epidemiology / / Stephen C. Newman
| Biostatistical methods in epidemiology / / Stephen C. Newman |
| Autore | Newman Stephen C. <1952-> |
| Pubbl/distr/stampa | New York, : John Wiley & Sons, c2001 |
| Descrizione fisica | 1 online resource (403 p.) |
| Disciplina |
614.4/07/27
614.4072 614.40727 |
| Collana | Wiley series in probability and statistics. Biostatistics section |
| Soggetto topico |
Epidemiology - Statistical methods
Cohort analysis |
| ISBN |
1-280-36696-6
9786610366965 0-470-35001-6 0-471-46160-1 0-471-27261-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Biostatistical Methods in Epidemiology; Contents; Preface; 1. Introduction; 1.1 Probability; 1.2 Parameter Estimation; 1.3 Random Sampling; 2. Measurement Issues in Epidemiology; 2.1 Systematic and Random Error; 2.2 Measures of Effect; 2.3 Confounding; 2.4 Collapsibility Approach to Confounding; 2.5 Counterfactual Approach to Confounding; 2.6 Methods to Control Confounding; 2.7 Bias Due to an Unknown Confounder; 2.8 Misclassification; 2.9 Scope of this Book; 3. Binomial Methods for Single Sample Closed Cohort Data; 3.1 Exact Methods; 3.2 Asymptotic Methods
10. Poisson Methods for Censored Survival Data10.1 Poisson Methods for Single Sample Survival Data; 10.2 Poisson Methods for Unstratified Survival Data; 10.3 Poisson Methods for Stratified Survival Data; 11. Odds Ratio Methods for Case-Control Data; 11.1 Justification of the Odds Ratio Approach; 11.2 Odds Ratio Methods for Matched-Pairs Case-Control Data; 11.3 Odds Ratio Methods for (1 : M) Matched Case-Control Data; 12. Standardized Rates and Age-Period-Cohort Analysis; 12.1 Population Rates; 12.2 Directly Standardized Death Rate; 12.3 Standardized Mortality Ratio 12.4 Age-Period-Cohort Analysis |
| Record Nr. | UNINA-9910877140403321 |
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Newman Stephen C. <1952->
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| New York, : John Wiley & Sons, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biostatistics and Epidemiology : A Primer for Health and Biomedical Professionals / / by Sylvia Wassertheil-Smoller, Jordan Smoller
| Biostatistics and Epidemiology : A Primer for Health and Biomedical Professionals / / by Sylvia Wassertheil-Smoller, Jordan Smoller |
| Autore | Wassertheil-Smoller Sylvia |
| Edizione | [4th ed. 2015.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XX, 260 p. 37 illus., 11 illus. in color.) |
| Disciplina | 614.4072 |
| Soggetto topico |
Statistics
Diagnosis, Laboratory Epidemiology Public health Statistics for Life Sciences, Medicine, Health Sciences Laboratory Medicine Public Health |
| ISBN | 1-4939-2134-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | The Scientific Method -- Probability -- Statistics -- Epidemiology.-Screening -- Clinical Trials -- Quality of Life -- Genetic Epidemiology -- Risk Prediction and Reclassification -- Research Ethics and Statistics -- Postscript -- Appendix A -- Appendix B -- Appendix C -- Appendix D -- Appendix E -- Appendix F -- References -- Suggested Readings -- Index. |
| Record Nr. | UNINA-9910299777903321 |
|
Wassertheil-Smoller Sylvia
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| New York, NY : , : Springer New York : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cohort Studies in Health Sciences / / edited by René Mauricio Barría
| Cohort Studies in Health Sciences / / edited by René Mauricio Barría |
| Autore | Barría R. Mauricio (René Mauricio) |
| Pubbl/distr/stampa | IntechOpen, 2018 |
| Descrizione fisica | 1 online resource (72 pages) |
| Disciplina | 614.4072 |
| Soggetto topico | Health Services |
| Soggetto non controllato | Medicine: general issues |
| ISBN |
1-83881-545-7
1-78923-695-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910317789403321 |
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Barría R. Mauricio (René Mauricio)
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| IntechOpen, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Disease surveillance [[electronic resource] ] : a public health informatics approach / / edited by Joseph S. Lombardo, David L. Buckeridge
| Disease surveillance [[electronic resource] ] : a public health informatics approach / / edited by Joseph S. Lombardo, David L. Buckeridge |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (484 p.) |
| Disciplina |
362.1
614.4072 |
| Altri autori (Persone) |
LombardoJoseph S. <1946->
BuckeridgeDavid Llewellyn <1970-> |
| Soggetto topico |
Public health surveillance
Medical informatics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-118-56905-9
1-280-85517-7 9786610855179 0-470-13188-8 0-470-13187-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
DISEASE SURVEILLANCE : A Public Health Informatics Approach; Contents; Contributors; Preface; Acknowledgments; 1 Disease Surveillance, a Public Health Priority; 1.1 Introduction; 1.2 The Emerging Role of Informatics in Public Health Practice; 1.3 Early Use of Technology for Public Health Practice; 1.3.1 Early Use of Analytics, Visualization, and Communications; 1.3.2 Early Informatics Applications in Medicine & Public Health; 1.3.3 Public Health Records Archiving; 1.4 Guiding Principles for Development of Public Health Applications
1 .5 Information Requirements for Automated Disease Surveillance1.6 Historical Impact of Infectious Disease Outbreaks; 1.6.1 Smallpox; 1.6.2 Plague; 1.6.3 Spanish Influenza, 1918; 1.6.4 Influenza Pandemics after 1918; 1.7 Disease as a Weapon; 1.7.1 Bioterrorism; 1.8 Modern Disease Surveillance Applications; 1.8.1 Components of an Early Recognition Disease Surveillance System; 1.8.2 Modern Surveillance Applications for Use by State and Local Health Departments; 1.8.3 National Disease Surveillance Initiatives; 1.9 Summary; References; Part I: System Design and Implementation 2 Understanding the Data: Health Indicators in Disease Surveillance2.1 Data Source Concepts; 2.2 Data from Pharmacy Chains; 2.3 Data from EMS and 911; 2.4 Data from Telephone Triage Hotlines; 2.5 Data from School Absenteeism and School Nurses; 2.6 Data from Hospital Visits; 2.7 Data from Physicians' Office Visits; 2.8 Laboratories Role in pre-diagnostic Surveillance; 2.9 Other Health Indicator Data; 2.9.1 Environmental Data; 2.9.2 Animal Health Data; 2.10 Data Source Evaluation; 2.10.1 Approach and Methodology; 2.10.2 Example: Wildfires (October 2003) 2.10.3 Example: Influenza Outbreak (December 2003)2.10.4 Example: Gastrointestinal Illness (January-February 2004); 2.10.5 Conclusions; 2.11 Study Questions; References; 3 Obtaining the Data; 3.1 Introduction to Data Collection and Archiving; 3.1.1 The Internet: Universal Connectivity; 3.1.2 Databases: Flexible Data Storage; 3.1.3 Summary; 3.2 Obtaining Access to Surveillance Data; 3.2.1 Sharing Health Indicator Data; 3.2.2 Data-Sharing Issues; 3.2.3 HIPAA and Disease Surveillance; 3.2.4 Summary of Data Sharing; 3.3 The Role of Standards in Data Exchange; 3.3.1 Types of Standards 3.3.2 Standards Development3.3.3 Standards for Health Indicator Data in Biosurveillence; 3.3.4 National Health Information Systems - Implementing Standards; 3.4 Establishing the Data Feeds; 3.4.1 Information Systems of the Data Provider or Source; 3.4.2 Setting Up the Data Feed; 3.4.3 Data Characteristics; 3.4.4 Data Fields or Elements; 3.4.5 Data Transfer Format; 3.4.6 Data Transfer Protocol; 3.4.7 Security Considerations; 3.4.8 Data Import Methods; 3.4.9 Data Cleaning; 3.4.10 Data Quality; 3.4.11 Summary; 3.5 Study Questions; References; 4 Alerting Algorithms for Biosurveillance 4.1 Statistical Alerting Algorithms |
| Record Nr. | UNINA-9910143721203321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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