Concise Epidemiologic Principle And Concepts Second Edition
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Concise Epidemiologic Principle And Concepts Second Edition
DOWNLOAD
Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-20
Concise Epidemiologic Principle And Concepts Second Edition written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-20 with Medical categories.
Concise Epidemiologic Principles and Concepts - Aberrant Epigenomic Modulations Implication We often conceive epidemiology in either simplistic or complex terms, and neither of these is accurate. To illustrate this, the complexities in epidemiology could be achieved by considering a study to determine the correlation between serum lipid profile as total cholesterol, HDL, LDL, triglyceride, and total body fatness or obesity measured by BMI in children. Two laboratories measured serum lipid profiles, and one observed a correlation with BMI, while the other did not. Which is the reliable finding? To address this question, one needs to examine the context of blood drawing since fasting blood level may provide a better indicator of serum lipid. Epidemiologic studies could be easily derailed given the inability to identify and address possible confounding. Therefore, understanding the principles and concepts used in epidemiologic studies designed and conducted to answer clinical research questions facilitates e accurate and reliable findings in these areas. Another similar example in a health fair setting involves geography and health, termed health-o-graphy. The risk of dying in one zip code A was 59.5 per 100,000, and in the other zip code B was 35.4 per 100,000. There is a common sense and non-epidemiologic tendency to conclude that there is an increased risk of dying in zip code A. To arrive at such inference, one must first find out the age distribution of these two zip codes since advancing age is associated with increased mortality. Indeed, zip code A is comparable to the United States population while, zip code B is the Mexican population. These two examples are indicative of the need to understand epidemiologic concepts such as confounding by age or effect measure modification prior to undertaking clinical research. This textbook describes the basics of research in medical and clinical settings, as well as the concepts and application of epidemiologic designs in research. Design transcends statistical techniques, and no matter how sophisticated statistical modeling, errors of design/sampling cannot be corrected. The author of this textbook has presented a complex field in a very simplified and reader-friendly manner with the intent that such a presentation will facilitate the understanding of the design process and epidemiologic thinking in clinical research. Additionally, this book provides a very basic explanation of how to examine the data collected for research conduct for the possibility of confounders and how to address such confounders, thus disentangling such effects for reliable and valid inference. Research is presented as an exercise around measurement, with measurement error inevitable in its conduct, hence the inherent uncertainties of all findings in clinical and medical research. Concise Epidemiologic Principles and Concepts (Second Edition) for Clinicians covers research conceptualization, namely research objectives, questions, hypothesis, design, implementation, data collection, analysis, results, and interpretation. While the primary focus of epidemiology is to assess the relationship between exposure (risk or predisposing factor) and outcome (disease or health-related event), the causal association is presented in a simplified manner, including the role of quantitative evidence synthesis (QES) in causal inference. Epidemiology has evolved over the past three decades, resulting in several fields being developed. This text presents, in brief, the perspectives and future of epidemiology in the era of the molecular basis of medicine, “3Ts,” and systems science, as well as Epigenomic Epidemiology. Epidemiologic evidence is more reliable if conceptualized and conducted within the context of translational, transdisciplinary, and team science. With molecular epidemiology, we are better equipped with tools to identify molecular biologic indicators of risk as well as biologic alterations in the early stages of disease, and with 3 Ts and systems science, we are more capable of providing accurate and reliable inference on causality and outcomes research. Further, the author argues that unless sampling error and confounding are identified and addressed, clinical research findings will remain largely inconsistent, implying an inconsequential epidemiologic approach. Appropriate knowledge of research conceptualization, design, and statistical inference is essential for conducting clinical and biomedical research. This knowledge is acquired through the understanding of epidemiologic/observational (non-experimental) and experimental designs and the choice of the appropriate test statistic for statistical inference. However, regardless of how sophisticated the statistical technique employed for statistical inference is, study conceptualization and design are the building blocks of valid scientific evidence. Since clinical research is performed to improve patients’ care, it remains relevant to assess not only the statistical significance but the clinical and biologic importance of the findings, for clinical decision-making in the care of an individual patient. Therefore, the aim of this book is to provide clinicians, biomedical researchers, graduate students in research methodology, students of public health, and all those involved in clinical/biomedical research with a simplified but concise overview of the principles and practice of epidemiology. In addition, the author stresses common flaws in the conduct, analysis, and interpretation of epidemiologic studies. Valid and reliable scientific research is that which considers the following elements in arriving at the truth from the data, namely biological relevance, clinical importance, and statistical stability and precision (statistical inference based on the p-value and the 90, 95, and 99 percent confidence interval). The interpretation of results of new research must rely on factual association or effect and the alternative explanation, namely systematic error, random error (precision), confounding, and effect measure modifier. Therefore, unless these perspectives are disentangled, the results from any given research cannot be considered reliable. However, even with this disentanglement, all study findings remain inconclusive with some degree of uncertainty. This book presents a comprehensive guide on how to conduct clinical and medical research—mainly research question formulation, study implementation, hypothesis testing using appropriate test statistics to analyze the data, and results interpretation. In so doing, it attempts to illustrate the basic concepts used in study conceptualization, epidemiologic design, and appropriate test statistics for statistical inference from the data. Therefore, though statistical inference is emphasized throughout the presentation in this text, equal emphasis is placed on clinical relevance or importance and biological relevance in the interpretation of the study results. Specifically, this book describes in basic terms and concepts how to conduct clinical and medical research using epidemiologic designs. The author presents epidemiology as the main profession in the trans-disciplinary approach to the understanding of complex ecologic models of disease and health. Clinicians, even those without preliminary or infantile knowledge of epidemiologic designs, could benefit immensely from what, when, where, who, and how studies are conceptualized, data collected as planned with the scale of measurement of the outcome and independent variables, data edited, cleaned and processed prior to analysis, appropriate analysis based on statistical assumptions and rationale, results tabulation for scientific appraisal, results interpretation and inference. Unlike most epidemiologic texts, this is the first book that attempts to simplify complex epidemiologic methods for users of epidemiologic research, namely clinicians and allied health researchers. Additionally, it is rare to find a book with integrates of basic research methodology into epidemiologic designs. Finally, research innovation and the current challenges of epidemiology are presented in this book to reflect the currency of the materials and the approach, as well as the responses to the challenges of epidemiology today namely, epigenomic epidemiology in environmental and gene interaction disease determinants. Epidemiology Conceptualized - Epidemiologic investigation and practice are as old as the history of modern medicine. It dates back to Hippocrates (circa 2,400 years ago). In recommending the appropriate practice of medicine, Hippocrates appealed to the physicians’ ability to understand the role of environmental factors in predisposition to disease and health in the community. During the Middle Ages and the Renaissance, epidemiologic principles continued to influence the practice of medicine, as demonstrated in De Morbis Artificum (1713) by Ramazinni and the works on scrotal cancer in relation to chimney sweeps by Percival Pott in 1775. With the works of John Snow, a British physician (1854), on cholera mortality in London, the era of scientific epidemiology began. By examining the distribution/pattern of mortality and cholera in London, Snow postulated that cholera was caused by contaminated water. Epidemiology Today – Epigenomic Epidemiology There are several definitions of epidemiology, but a practical definition is necessary for the understanding of this science and art. Epidemiology is the basic science of public health. The objective of this profession is to assess the distribution and determinants of disease, disabilities, injuries, natural disasters (tsunamis, hurricanes, tornados, and earthquakes), and health- related events at the population level. Epidemiologic investigation or research focuses on a specific population. The basic issue is to assess the groups of people at higher risk: women, children, men, pregnant women, teenagers, whites, African Americans, Hispanics, Asians, poor, affluent, gay, lesbians, married, single, older individuals, etc. Epidemiology also examines how the frequency of the disease or the event of interest changes over time. In addition, epidemiology examines the variation of the disease of interest from place to place. Simply, descriptive epidemiology attempts to address the distribution of disease with respect to “who,” “when,” and “where.” For example, cancer epidemiologists attempt to describe the occurrence of prostate cancer by observing the differences in populations by age, socioeconomic status, occupation, geographic locale, race/ethnicity, etc. Epidemiology also attempts to address the association between the disease and exposure. For example, why are some men at high risk for prostate cancer? Does race/ethnicity increase the risk for prostate cancer? Simply, is the association causal or spurious? This process involves the effort to determine whether a factor (exposure) is associated with the disease (outcome). In the example of prostate cancer, such exposure includes a high-fat diet, race/ethnicity, advancing age, pesticides, family history of prostate cancer, and so on. Whether or not the association is factual or a result of chance remains the focus of epidemiologic research. The questions to be raised are as follows: Is prostate cancer associated with pesticides? Does pesticide cause prostate cancer? Epidemiology often goes beyond disease-exposure association or relationship to establish a causal association. In this process of causal inference, it depends on certain criteria, one of which is the strength or magnitude of association, leading to the recommendation of preventive measures. However, complete knowledge of the causal mechanism is not necessary prior to preventive measures for disease control. Further, findings from epidemiologic research facilitate the prioritization of health issues and the development and implementation of intervention programs for disease control and health promotion. Epidemiology today reflects the application of gene and environment interaction in disease causation, morbidity, prognosis, survival, and mortality in subpopulation health outcomes. The knowledge and understanding of subpopulation differentials in DNA methylation of specific genes and histone modification allows for the application of abnormal transcriptomes, impaired gene expression, protein synthesis dysfunctionality, and abnormal cellular functionality.
Concise Epidemiologic Principles And Concepts Second Edition
DOWNLOAD
Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-19
Concise Epidemiologic Principles And Concepts Second Edition written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-19 with Medical categories.
Concise Epidemiologic Principles & Concepts - Study Design, Conduct and Application We often conceive epidemiology in either simplistic or complex terms, and neither of these is accurate. To illustrate this, the complexities in epidemiology could be achieved by considering a study to determine the correlation between serum lipid profile as total cholesterol, HDL, LDL, triglyceride, and total body fatness or obesity measured by BMI in children. Two laboratories measured serum lipid profiles, and one observed a correlation with BMI, while the other did not. Which is the reliable finding? To address this question, one needs to examine the context of blood drawing since fasting blood level may provide a better indicator of serum lipid. Epidemiologic studies could be easily derailed given the inability to identify and address possible confounding. Therefore, understanding the principles and concepts used in epidemiologic studies designed and conducted to answer clinical research questions facilitates e accurate and reliable findings in these areas. Another similar example in a health fair setting involves geography and health, termed health-o-graphy. The risk of dying in one zip code A was 59.5 per 100,000, and in the other zip code B was 35.4 per 100,000. There is a common sense and non-epidemiologic tendency to conclude that there is an increased risk of dying in zip code A. To arrive at such inference, one must first find out the age distribution of these two zip codes since advancing age is associated with increased mortality. Indeed, zip code A is comparable to the United States population while, zip code B is the Mexican population. These two examples are indicative of the need to understand epidemiologic concepts such as confounding by age or effect measure modification prior to undertaking clinical research. This textbook describes the basics of research in medical and clinical settings, as well as the concepts and application of epidemiologic designs in research. Design transcends statistical techniques, and no matter how sophisticated statistical modeling, errors of design/sampling cannot be corrected. The author of this textbook has presented a complex field in a very simplified and reader-friendly manner with the intent that such a presentation will facilitate the understanding of the design process and epidemiologic thinking in clinical research. Additionally, this book provides a very basic explanation of how to examine the data collected for research conduct for the possibility of confounders and how to address such confounders, thus disentangling such effects for reliable and valid inference. Research is presented as an exercise around measurement, with measurement error inevitable in its conduct, hence the inherent uncertainties of all findings in clinical and medical research. Concise Epidemiologic Principles and Concepts (Second Edition) for Clinicians covers research conceptualization, namely research objectives, questions, hypothesis, design, implementation, data collection, analysis, results, and interpretation. While the primary focus of epidemiology is to assess the relationship between exposure (risk or predisposing factor) and outcome (disease or health-related event), the causal association is presented in a simplified manner, including the role of quantitative evidence synthesis (QES) in causal inference. Epidemiology has evolved over the past three decades, resulting in several fields being developed. This text presents, in brief, the perspectives and future of epidemiology in the era of the molecular basis of medicine, “3Ts,” and systems science, as well as Epigenomic Epidemiology. Epidemiologic evidence is more reliable if conceptualized and conducted within the context of translational, transdisciplinary, and team science. With molecular epidemiology, we are better equipped with tools to identify molecular biologic indicators of risk as well as biologic alterations in the early stages of disease, and with 3 Ts and systems science, we are more capable of providing accurate and reliable inference on causality and outcomes research. Further, the author argues that unless sampling error and confounding are identified and addressed, clinical research findings will remain largely inconsistent, implying an inconsequential epidemiologic approach. Appropriate knowledge of research conceptualization, design, and statistical inference is essential for conducting clinical and biomedical research. This knowledge is acquired through the understanding of epidemiologic/observational (non-experimental) and experimental designs and the choice of the appropriate test statistic for statistical inference. However, regardless of how sophisticated the statistical technique employed for statistical inference is, study conceptualization and design are the building blocks of valid scientific evidence. Since clinical research is performed to improve patients’ care, it remains relevant to assess not only the statistical significance but the clinical and biologic importance of the findings, for clinical decision-making in the care of an individual patient. Therefore, the aim of this book is to provide clinicians, biomedical researchers, graduate students in research methodology, students of public health, and all those involved in clinical/biomedical research with a simplified but concise overview of the principles and practice of epidemiology. In addition, the author stresses common flaws in the conduct, analysis, and interpretation of epidemiologic studies. Valid and reliable scientific research is that which considers the following elements in arriving at the truth from the data, namely biological relevance, clinical importance, and statistical stability and precision (statistical inference based on the p-value and the 90, 95, and 99 percent confidence interval). The interpretation of results of new research must rely on factual association or effect and the alternative explanation, namely systematic error, random error (precision), confounding, and effect measure modifier. Therefore, unless these perspectives are disentangled, the results from any given research cannot be considered reliable. However, even with this disentanglement, all study findings remain inconclusive with some degree of uncertainty. This book presents a comprehensive guide on how to conduct clinical and medical research—mainly research question formulation, study implementation, hypothesis testing using appropriate test statistics to analyze the data, and results interpretation. In so doing, it attempts to illustrate the basic concepts used in study conceptualization, epidemiologic design, and appropriate test statistics for statistical inference from the data. Therefore, though statistical inference is emphasized throughout the presentation in this text, equal emphasis is placed on clinical relevance or importance and biological relevance in the interpretation of the study results. Specifically, this book describes in basic terms and concepts how to conduct clinical and medical research using epidemiologic designs. The author presents epidemiology as the main profession in the trans-disciplinary approach to the understanding of complex ecologic models of disease and health. Clinicians, even those without preliminary or infantile knowledge of epidemiologic designs, could benefit immensely from what, when, where, who, and how studies are conceptualized, data collected as planned with the scale of measurement of the outcome and independent variables, data edited, cleaned and processed prior to analysis, appropriate analysis based on statistical assumptions and rationale, results tabulation for scientific appraisal, results interpretation and inference. Unlike most epidemiologic texts, this is the first book that attempts to simplify complex epidemiologic methods for users of epidemiologic research, namely clinicians and allied health researchers. Additionally, it is rare to find a book with integrates of basic research methodology into epidemiologic designs. Finally, research innovation and the current challenges of epidemiology are presented in this book to reflect the currency of the materials and the approach, as well as the responses to the challenges of epidemiology today namely, epigenomic epidemiology in environmental and gene interaction disease determinants. A study could be statistically significant but biologically and clinically irrelevant since the statistical stability of a study does not rule out bias and confounding. The p-value is deemphasized, while the use of effect size or magnitude and confidence intervals in the interpretation of results for application in clinical decision- making is recommended. The use of p-value could lead to an erroneous interpretation of the effectiveness of treatment. For example, studies with large sample sizes and very little or insignificant effects of no clinical importance may be statistically significant, while studies with small samples though a large magnitude of effects are labeled “negative result.”i Such results are due to low statistical power and increasing variability, hence the inability to pass the arbitrary litmus test of the 5 percent significance level. Epidemiology Conceptualized Epidemiologic investigation and practice are as old as the history of modern medicine. It dates back to Hippocrates (circa 2,400 years ago). In recommending the appropriate practice of medicine, Hippocrates appealed to the physicians’ ability to understand the role of environmental factors in predisposition to disease and health in the community. During the Middle Ages and the Renaissance, epidemiologic principles continued to influence the practice of medicine, as demonstrated in De Morbis Artificum (1713) by Ramazinni and the works on scrotal cancer in relation to chimney sweeps by Percival Pott in 1775. With the works of John Snow, a British physician (1854), on cholera mortality in London, the era of scientific epidemiology began. By examining the distribution/pattern of mortality and cholera in London, Snow postulated that cholera was caused by contaminated water. Epidemiology Today – Epigenomic Epidemiology There are several definitions of epidemiology, but a practical definition is necessary for the understanding of this science and art. Epidemiology is the basic science of public health. The objective of this profession is to assess the distribution and determinants of disease, disabilities, injuries, natural disasters (tsunamis, hurricanes, tornados, and earthquakes), and health- related events at the population level. Epidemiologic investigation or research focuses on a specific population. The basic issue is to assess the groups of people at higher risk: women, children, men, pregnant women, teenagers, whites, African Americans, Hispanics, Asians, poor, affluent, gay, lesbians, married, single, older individuals, etc. Epidemiology also examines how the frequency of the disease or the event of interest changes over time. In addition, epidemiology examines the variation of the disease of interest from place to place. Simply, descriptive epidemiology attempts to address the distribution of disease with respect to “who,” “when,” and “where.” For example, cancer epidemiologists attempt to describe the occurrence of prostate cancer by observing the differences in populations by age, socioeconomic status, occupation, geographic locale, race/ethnicity, etc. Epidemiology also attempts to address the association between the disease and exposure. For example, why are some men at high risk for prostate cancer? Does race/ethnicity increase the risk for prostate cancer? Simply, is the association causal or spurious? This process involves the effort to determine whether a factor (exposure) is associated with the disease (outcome). In the example of prostate cancer, such exposure includes a high-fat diet, race/ethnicity, advancing age, pesticides, family history of prostate cancer, and so on. Whether or not the association is factual or a result of chance remains the focus of epidemiologic research. The questions to be raised are as follows: Is prostate cancer associated with pesticides? Does pesticide cause prostate cancer? Epidemiology often goes beyond disease-exposure association or relationship to establish a causal association. In this process of causal inference, it depends on certain criteria, one of which is the strength or magnitude of association, leading to the recommendation of preventive measures. However, complete knowledge of the causal mechanism is not necessary prior to preventive measures for disease control. Further, findings from epidemiologic research facilitate the prioritization of health issues and the development and implementation of intervention programs for disease control and health promotion. Epidemiology today reflects the application of gene and environment interaction in disease causation, morbidity, prognosis, survival, and mortality in subpopulation health outcomes. The knowledge and understanding of subpopulation differentials in DNA methylation of specific genes and histone modification allows for the application of abnormal transcriptomes, impaired gene expression, protein synthesis dysfunctionality, and abnormal cellular functionality. This book is conceptually organized into three sections. Section I deals with research methods, section II epidemiologic designs, as well as causal inference and perspectives in epidemiology, while section III delves into perspectives, epidemiologic challenges, and special topics in epidemiology, namely epidemiologic tree, challenges, emerging fields, the consequentialist perspective of epidemiology and epidemiologic role in health and healthcare policy formulation, as well as epigenomic epidemiology and epigenomic determinants of health (EDH). Throughout this book, attempts are made to describe the research methods and non- experimental as well as experimental designs. Section I comprises research methods with an attempt to describe the following: Research objectives and purposes, Research questions, Hypothesis statements: null and alternative, Rationales for research, clinical reasoning, and diagnostic tests, as well as Study conceptualization and conduct—research question, data collection, data management, hypothesis testing, data analysis.
Concise Biostatistical Principles And Concepts 2nd Edition
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Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-18
Concise Biostatistical Principles And Concepts 2nd Edition written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Medical categories.
Concise Biostatistical Principles and Concepts - Statistical Reality in Evidence Discovery Clinical medicine or surgery continues to make advances through evidence that is judged to be objectively drawn from the care of individual patients. The natural observation of individuals remains the basis for our researchable questions’ formulation and the subsequent hypothesis testing. Evidence-based medicine or surgery depends on how critical we are in evaluating evidence in order to inform our practice. These evaluations no matter how objective are never absolute but probabilistic, as we will never know with absolute certainty how to treat future patients who were not a part of our study. Despite the obstacles facing us today in an attempt to provide an objective evaluation of our patients, since all our decisions are based on a judgment of some evidence, we have progressed from expert opinion to the body of evidence from randomized controlled clinical trials, as well as cohort investigations, prospective and retrospective. The conduct of clinical trials though termed the “gold standard”, which yields more reliable and valid evidence from the data relative to non-experimental or observational designs, depends on how well it is designed and conducted prior to outcomes data collection, analysis, results, interpretation, and dissemination. The designs and the techniques used to draw statistical inferences are often beyond the average clinician’s understanding. A text that brings hypothesis formulation, analysis, and how to interpret the results of the findings is long overdue and highly anticipated. Statistical modeling which is fundamentally a journey from sample to the application of findings is essential to evidence discovery. The four past decades have experienced modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery. While the application of novel statistical techniques to our data is necessary and fundamental to research, the selection of a sample and sampling method that reflects the representativeness of that sample to the targeted population is even more important. Since one of the rationale behind research conduct is to generate new knowledge and apply it to improve life situations including the improvement of patient and population health, sampling, sample size, and power estimations remain the basis for such inference. With the essential relevance of sample and sampling technique to how we come to make sense of data, the design of the study transcends statistical technique, since no statistical tool no matter how sophisticated can correct the errors of sampling. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample, randomization process when applicable prior to the selection of the analytic tool. In addition, it stresses the importance of biological and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is a random sample. When studies are conducted without random samples as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value. However, in the absence of a random sample, the p-value can be applied to designs that utilize consecutive samples, and disease registries, since these samples reflect the population of interest, and hence representative sample, justifying inference and generalization. Essential to the selection of test statistics is the understanding of the scale of the measurement of the variables, especially the response, outcome or dependent variable, type of sample (independent or correlated), hypothesis, and normality assumption. In terms of the selection of statistical tests, this text is based on the scale of measurement (binary), type of sample (single, independent), and relationship (linear). For example, if the scale of measurement of the outcome variable is binary, repeated measure, and normality is not assumed, the repeated measure logistic regression model remains a feasible model for evidence discovery in using the independent variables to predict the repeated outcome. This book presents a simplified approach to evidence discovery by recommending the graphic illustration of data and normality test for continuous (ratio/interval scale) data prior to statistical test selection. Unlike current text in biostatistics, the approach taken to present these materials is very simple. First, this text uses applied statistics by illustrating what, when, where, and why a test is appropriate. Where a text violates the normality assumption, readers are presented with a non-parametric alternative. The rationale for the test is explained with a limited mathematical formula and is intended in order to stress the applied nature of biostatistics. Attempts have been made in this book to present the most commonly used statistical model in biomedical or clinical research. We believe since no book is complete to have covered the basics that will facilitate the understanding of scientific evidence discovery. We hope this book remains a useful guide, which is our intention in bridging the gap between theoretical statistical models and reality in the statistical modeling of biomedical and clinical research data. As researchers we all make mistakes and we believe we have learned from our mistakes during the past three decades hence the need to examine flaws and apply reality in the statistical modeling of biomedical and research data. We hope this text results in increased reliability in the conduct, analysis
Concise Epidemiologic Principles And Concepts
DOWNLOAD
Author : Laurens Holmes Jr.
language : en
Publisher: AuthorHouse
Release Date : 2013-09-11
Concise Epidemiologic Principles And Concepts written by Laurens Holmes Jr. and has been published by AuthorHouse this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-11 with Education categories.
This textbook describes the basics of research in medical, clinical, and biomedical settings as well as the concepts and application of epidemiologic designs in research conduct. Design transcends statistical techniques, and no matter how sophisticated a statistical modeling, errors of design/sampling cannot be corrected. The authors of this textbook have presented a complex field in a very simplified and reader-friendly manner with the intent that such presentation will facilitate the understanding of design process and epidemiologic thinking in clinical and biomedical research. Covers these relevant topics in epidemiology: Case-Cohort Design Prospective Case-Control Quantitative Evidence Synthesis (QES) Instant Cohort Design & Case-Crossover Design Effect Modification & Interaction Epidemiologic Tree - Molecular Epidemiology & Health Disparities Epidemiologic Challenge Big Data, mHealth, Social Media 3 Ts - Team Science, Transdisciplinary Research, Translational Research Bias, Random error, Confounding Systems Science & Evidence Discovery Research is presented as an exercise around measurement, with measurement error inevitable in its conducthence the inherent uncertainties of all findings in clinical and biomedical research. Concise Epidemiologic Principles and Concepts covers research conceptualization, namely research objectives, questions, hypothesis, design, implementation, data collection, analysis, results, and interpretation. While the primary focus of epidemiology is to assess the relationship between exposure (risk or predisposing factor) and outcome (disease or health-related event), causal association is presented in a simplified manner, including the role of quantitative evidence synthesis (meta-analysis) in causal inference. Epidemiology has evolved over the past three decades resulting in several fields being developed. This text presents in brief the perspectives and future of epidemiology in the era of the molecular basis of medicine. With molecular epidemiology, we are better equipped with tools to identify molecular biologic indicators of risk as well as biologic alterations in the early stages of disease.
Concise Biostatistical Principles And Concepts
DOWNLOAD
Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-18
Concise Biostatistical Principles And Concepts written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Medical categories.
Concise Biostatistical Principles and Concepts, 2nd Edition Clinical medicine or surgery continues to make advances through evidence that is judged to be objectively drawn from the care of individual patients. The natural observation of individuals remains the basis for our researchable questions’ formulation and the subsequent hypothesis testing. Evidence-based medicine or surgery depends on how critical we are in evaluating evidence in order to inform our practice. These evaluations no matter how objective are never absolute but probabilistic, as we will never know with absolute certainty how to treat future patients who were not a part of our study. Despite the obstacles facing us today in an attempt to provide an objective evaluation of our patients, since all our decisions are based on a judgment of some evidence, we have progressed from expert opinion to the body of evidence from randomized controlled clinical trials, as well as cohort investigations, prospective and retrospective. The conduct of clinical trials though termed the “gold standard”, which yields more reliable and valid evidence from the data relative to non-experimental or observational designs, depends on how well it is designed and conducted prior to outcomes data collection, analysis, results, interpretation, and dissemination. The designs and the techniques used to draw statistical inferences are often beyond the average clinician’s understanding. A text that brings hypothesis formulation, analysis, and how to interpret the results of the findings is long overdue and highly anticipated. Statistical modeling which is fundamentally a journey from sample to the application of findings is essential to evidence discovery. The four past decades have experienced modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery. While the application of novel statistical techniques to our data is necessary and fundamental to research, the selection of a sample and sampling method that reflects the representativeness of that sample to the targeted population is even more important. Since one of the rationale behind research conduct is to generate new knowledge and apply it to improve life situations including the improvement of patient and population health, sampling, sample size, and power estimations remain the basis for such inference. With the essential relevance of sample and sampling technique to how we come to make sense of data, the design of the study transcends statistical technique, since no statistical tool no matter how sophisticated can correct the errors of sampling. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample, randomization process when applicable prior to the selection of the analytic tool. In addition, it stresses the importance of biological and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is a random sample. When studies are conducted without random samples as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value. However, in the absence of a random sample, the p-value can be applied to designs that utilize consecutive samples, and disease registries, since these samples reflect the population of interest, and hence representative sample, justifying inference and generalization. Essential to the selection of test statistics is the understanding of the scale of the measurement of the variables, especially the response, outcome or dependent variable, type of sample (independent or correlated), hypothesis, and normality assumption. In terms of the selection of statistical tests, this text is based on the scale of measurement (binary), type of sample (single, independent), and relationship (linear). For example, if the scale of measurement of the outcome variable is binary, repeated measure, and normality is not assumed, the repeated measure logistic regression model remains a feasible model for evidence discovery in using the independent variables to predict the repeated outcome. This book presents a simplified approach to evidence discovery by recommending the graphic illustration of data and normality test for continuous (ratio/interval scale) data prior to statistical test selection. Unlike current text in biostatistics, the approach taken to present these materials is very simple. First, this text uses applied statistics by illustrating what, when, where, and why a test is appropriate. Where a text violates the normality assumption, readers are presented with a non-parametric alternative. The rationale for the test is explained with a limited mathematical formula and is intended in order to stress the applied nature of biostatistics. Attempts have been made in this book to present the most commonly used statistical model in biomedical or clinical research. We believe since no book is complete to have covered the basics that will facilitate the understanding of scientific evidence discovery. We hope this book remains a useful guide, which is our intention in bridging the gap between theoretical statistical models and reality in the statistical modeling of biomedical and clinical research data. As researchers we all make mistakes and we believe we have learned from our mistakes during the past three decades hence the need to examine flaws and apply reality in the statistical modeling of biomedical and research data. We hope this text results in increased reliability in the conduct, analysis,
Concise Handbook Of Epidemiology Second Edition
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Author : Jalal-Eddeen Abubakar Saleh
language : en
Publisher: Bentham Science Publishers
Release Date : 2024-12-11
Concise Handbook Of Epidemiology Second Edition written by Jalal-Eddeen Abubakar Saleh and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-11 with Medical categories.
Concise Handbook of Epidemiology is an essential resource introducing readers to core principles, models, and research methods of modern epidemiology. This comprehensive guide covers foundational concepts like health and disease, disease occurrence, epidemiological models, and study designs. It also talks about disease surveillance, outbreak investigations, statistical methods, and advanced epidemiological techniques, including the impact of genomics and community-based epidemiology. Each chapter incorporates real-world case studies, offering readers practical insights and applications. Ethical considerations, innovative methods, and future directions in the field ensure readers are equipped with foundational knowledge and forward-looking perspectives. Key Features: - Foundational Overview: Covers health, disease occurrence, and key epidemiological frameworks. - Practical Tools: Offers research designs, statistical methods, and real-world case studies. - Advanced Topics: Explores genomics, statistical advances, and challenges in modern epidemiology. - Future Focus: Discusses ethics and evolving innovations shaping epidemiological practices.
Epidemiology And The People S Health
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Author : Nancy Krieger
language : en
Publisher: Oxford University Press
Release Date : 2024
Epidemiology And The People S Health written by Nancy Krieger and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Medical categories.
"Theory. Traced to its Greek roots, "theory" means to see inwards; to theorize is to use our mind's eye systematically, following articulated principles, to discern meaningful patterns among observations and ideas (Oxford English Dictionary [OED] 2022). The implication is that without theory, observation is blind and explanation is impossible"--
Information Resources In Toxicology Volume 1 Background Resources And Tools
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Author :
language : en
Publisher: Academic Press
Release Date : 2020-05-16
Information Resources In Toxicology Volume 1 Background Resources And Tools written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-16 with Technology & Engineering categories.
This new fifth edition of Information Resources in Toxicology offers a consolidated entry portal for the study, research, and practice of toxicology. Both volumes represents a unique, wide-ranging, curated, international, annotated bibliography, and directory of major resources in toxicology and allied fields such as environmental and occupational health, chemical safety, and risk assessment. The editors and authors are among the leaders of the profession sharing their cumulative wisdom in toxicology's subdisciplines. This edition keeps pace with the digital world in directing and linking readers to relevant websites and other online tools.Due to the increasing size of the hardcopy publication, the current edition has been divided into two volumes to make it easier to handle and consult. Volume 1: Background, Resources, and Tools, arranged in 5 parts, begins with chapters on the science of toxicology, its history, and informatics framework in Part 1. Part 2 continues with chapters organized by more specific subject such as cancer, clinical toxicology, genetic toxicology, etc. The categorization of chapters by resource format, for example, journals and newsletters, technical reports, organizations constitutes Part 3. Part 4 further considers toxicology's presence via the Internet, databases, and software tools. Among the miscellaneous topics in the concluding Part 5 are laws and regulations, professional education, grants and funding, and patents. Volume 2: The Global Arena offers contributed chapters focusing on the toxicology contributions of over 40 countries, followed by a glossary of toxicological terms and an appendix of popular quotations related to the field.The book, offered in both print and electronic formats, is carefully structured, indexed, and cross-referenced to enable users to easily find answers to their questions or serendipitously locate useful knowledge they were not originally aware they needed. Among the many timely topics receiving increased emphasis are disaster preparedness, nanotechnology, -omics, risk assessment, societal implications such as ethics and the precautionary principle, climate change, and children's environmental health. - Introductory chapters provide a backdrop to the science of toxicology, its history, the origin and status of toxicoinformatics, and starting points for identifying resources - Offers an extensive array of chapters organized by subject, each highlighting resources such as journals, databases,organizations, and review articles - Includes chapters with an emphasis on format such as government reports, general interest publications, blogs, and audiovisuals - Explores recent internet trends, web-based databases, and software tools in a section on the online environment - Concludes with a miscellany of special topics such as laws and regulations, chemical hazard communication resources, careers and professional education, K-12 resources, funding, poison control centers, and patents - Paired with Volume Two, which focuses on global resources, this set offers the most comprehensive compendium of print, digital, and organizational resources in the toxicological sciences with over 120 chapters contributions by experts and leaders in the field
Concepts Of Epidemiology
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Author : Raj S. Bhopal
language : en
Publisher: Oxford University Press
Release Date : 2016
Concepts Of Epidemiology written by Raj S. Bhopal and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Medical categories.
Describes and illustrates epidemiology and its applications to policy making, health service planning, and health promotion. The book emphasises interactive learning, with each chapter including learning objectives, theoretical and numerical exercises, questions and answers, and a summary.
Modern Biostatistical Principles And Conduct
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Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-14
Modern Biostatistical Principles And Conduct written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-14 with Medical categories.
Modern Biostatistical Principles & Conduct - Clinical Medicine and Public/Population Health Assessment Clinical medicine or surgery continues to make advances through evidence that is judged to be objectively drawn from the care of individual patients. The natural observation of individuals remains the basis for our researchable questions’ formulation and the subsequent hypothesis testing. Evidence-based medicine or surgery depends on how critical we are in evaluating evidence in order to inform our practice. These evaluations no matter how objective are never absolute but probabilistic, as we will never know with absolute certainty how to treat future patients who were not a part of our study. Despite the obstacles facing us today in an attempt to provide an objective evaluation of our patients, since all our decisions are based on a judgment of some evidence, we have progressed from expert opinion to the body of evidence from randomized controlled clinical trials, as well as cohort investigations, prospective and retrospective. The conduct of clinical trials though termed the “gold standard”, which yields more reliable and valid evidence from the data relative to non-experimental or observational designs, depends on how well it is designed and conducted prior to outcomes data collection, analysis, results, interpretation, and dissemination. The designs and the techniques used to draw statistical inferences are often beyond the average clinician’s understanding. A text that brings hypothesis formulation, analysis, and how to interpret the results of the findings is long overdue and highly anticipated. Statistical modeling which is fundamentally a journey from sample to the application of findings is essential to evidence discovery. This text, Modern Biostatistics for Clinical, Biomedical and Population-Based Researchers has filled this gap, not only in the way complex modeling is explained but the simplification of statistical techniques in a way that had never been explained before. This text has been prepared intentionally at the rudimentary level to benefit clinicians without sophisticated mathematical backgrounds or previous advanced knowledge of biostatics as applied statistics in health and medicine. Also, biomedical researchers who may want to conduct clinical research, as well as consumers of research products may benefit from the sampling techniques, their relevance to scientific evidence discovery as well a simplified approach to statistical modeling of clinical and biomedical research data. It is with this expectation and enthusiasm that we recommend this text to clinicians in all fields of clinical and biomedical research. One’s experience with biomedical research and how the findings in this arm are translated to the clinical environment signals the need for the application of biological, and clinical relevance of findings prior to statistical inference. The examples provided by the author to simplify research methods are familiar to orthopedic surgeons as well as clinicians in other specialties of medicine and surgery. Whereas statistical inference is essential in our application of the research findings to clinical decision-making regarding the care of our patients, statistical inference without clinical relevance or importance can be very misleading, and meaningless. The authors have attempted to deemphasize the p-value in the interpretation of clinical and biomedical research findings, by stressing the importance of confidence intervals, which allow for the quantification of evidence. For example, a large study due to a large sample size that minimizes variability may show a statistically significant difference while in reality, the difference is too insignificant to warrant any clinical importance. In contrast, a small study as frequently seen in most clinical trials or surgical research may have a large effect size of clinical relevance but not statistically significant at (p > 0.05). Thus, without considering the magnitude of the effect size with the confidence interval, we tend to regard these studies as negative findings, which is erroneous, since the absence of evidence, simply on the basis of an arbitrary significance level of 5% does not necessarily mean evidence of absence.1 In effect, clinical research results, cannot be adequately interpreted without first considering the biological and clinical significance of the data, before the statistical stability of the findings (p-value and 95% Confidence Interval), since the p-value as observed by the authors merely reflects the size of the study and not the measure of evidence. In recommending this text, it is one’s inclination that this book will benefit clinicians, research fellows, clinical fellows, postdoctoral students in biomedical and clinical settings, nurses, clinical research coordinators, physical therapists, and all those involved in clinical research design, conduct, and analysis of research data for statistical and clinical relevance. Convincingly, knowledge gained from this text will lead to our improvement of patient care through well-conceptualized research. Therefore, with the knowledge that no book is complete, no matter its content or volume, especially a book of this nature, which is prepared to guide clinicians on sampling, statistical modeling of data, and interpretation of findings, this book will benefit clinicians who are interested in applying appropriate statistical technique to scientific evidence discovery. Finally, we are optimistic that this book will bridge the gap in knowledge and practice of clinical and biomedical research, especially for clinicians in busy practice who are passionate about making a difference in their patient's care through scientific research initiatives.