Pub Date : 2018-10-01DOI: 10.1093/med/9780198814726.003.0022
Alan J. Silman, Gary J. Macfarlane, Tatiana V Macfarlane
The previous chapter has discussed how to gather and evaluate existing evidence from epidemiological studies. Here further consideration is given to summarizing the identified evidence in such a way that it can be used for decision-making, including approaches to control for chance and potential bias. Meta-analysis refers to the statistical analysis of results from individual studies for integrating the findings. There are other terms related to meta-analysis such as quantitative review, combined analysis, pooled analysis, or quantitative synthesis. Some of them use different methods, for example, meta-analysis of published data considers each study as a unit of analysis while individual patient data analysis includes the original data from each study on a participant level. This chapter describes how to numerically summarize data through performing a meta-analysis using data from a systematic review of epidemiological studies. It also considers possible bias, reporting guidelines, and statistical software available for meta-analysis.
{"title":"Meta-analysis","authors":"Alan J. Silman, Gary J. Macfarlane, Tatiana V Macfarlane","doi":"10.1093/med/9780198814726.003.0022","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0022","url":null,"abstract":"The previous chapter has discussed how to gather and evaluate existing evidence from epidemiological studies. Here further consideration is given to summarizing the identified evidence in such a way that it can be used for decision-making, including approaches to control for chance and potential bias. Meta-analysis refers to the statistical analysis of results from individual studies for integrating the findings. There are other terms related to meta-analysis such as quantitative review, combined analysis, pooled analysis, or quantitative synthesis. Some of them use different methods, for example, meta-analysis of published data considers each study as a unit of analysis while individual patient data analysis includes the original data from each study on a participant level. This chapter describes how to numerically summarize data through performing a meta-analysis using data from a systematic review of epidemiological studies. It also considers possible bias, reporting guidelines, and statistical software available for meta-analysis.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117115631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1093/MED/9780198814726.003.0015
A. Silman, G. Macfarlane, T. Macfarlane
Although epidemiological studies are increasingly based on the analysis of existing data sets (including linked data sets), many studies still require primary data collection. Such data may come from patient questionnaires, interviews, abstraction from records, and/or the results of tests and measures such as weight or blood test results. The next stage is to analyse the data gathered from individual subjects to provide the answers required. Before commencing with the statistical analysis of any data set, the data themselves must be prepared in a format so that the detailed statistical analysis can achieve its goals. Items to be considered include the format the data are initially collected in and how they are transferred to an appropriate electronic form. This chapter explores how errors are minimized and the quality of the data set ensured. These tasks are not trivial and need to be planned as part of a detailed study methodology.
{"title":"Preparation of collected primary data for statistical analysis","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/MED/9780198814726.003.0015","DOIUrl":"https://doi.org/10.1093/MED/9780198814726.003.0015","url":null,"abstract":"Although epidemiological studies are increasingly based on the analysis of existing data sets (including linked data sets), many studies still require primary data collection. Such data may come from patient questionnaires, interviews, abstraction from records, and/or the results of tests and measures such as weight or blood test results. The next stage is to analyse the data gathered from individual subjects to provide the answers required. Before commencing with the statistical analysis of any data set, the data themselves must be prepared in a format so that the detailed statistical analysis can achieve its goals. Items to be considered include the format the data are initially collected in and how they are transferred to an appropriate electronic form. This chapter explores how errors are minimized and the quality of the data set ensured. These tasks are not trivial and need to be planned as part of a detailed study methodology.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"23 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113979711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1093/MED/9780198814726.003.0002
A. Silman, G. Macfarlane, T. Macfarlane
A key aspect of epidemiology is about understanding and exploring variations in disease. In order to do so it is important that the same definitions of disease occurrence are used, no matter where a study is being conducted, and that the same approach to measurement is taken. The epidemiologist needs to be aware of which measure to use in specific circumstances. Measuring disease occurrence is the basic activity of epidemiology, and the following section provides the background to choosing the most appropriate measure(s) of disease occurrence for the aims of the study. The term ‘disease’ can also be taken in this context to describe any personal attribute. Thus, the concepts described in this chapter apply equally well to assessing the occurrence of a symptom, the development of a particular disability, or the frequency of a medical intervention.
{"title":"Which measure of disease occurrence?","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/MED/9780198814726.003.0002","DOIUrl":"https://doi.org/10.1093/MED/9780198814726.003.0002","url":null,"abstract":"A key aspect of epidemiology is about understanding and exploring variations in disease. In order to do so it is important that the same definitions of disease occurrence are used, no matter where a study is being conducted, and that the same approach to measurement is taken. The epidemiologist needs to be aware of which measure to use in specific circumstances. Measuring disease occurrence is the basic activity of epidemiology, and the following section provides the background to choosing the most appropriate measure(s) of disease occurrence for the aims of the study. The term ‘disease’ can also be taken in this context to describe any personal attribute. Thus, the concepts described in this chapter apply equally well to assessing the occurrence of a symptom, the development of a particular disability, or the frequency of a medical intervention.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131962488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1093/med/9780198814726.003.0006
A. Silman, G. Macfarlane, T. Macfarlane
A key issue in epidemiology is to choose an appropriate study design. The design chosen will depend on the question of interest and some designs will offer clear advantages over others. However, other factors such as current state of knowledge will be important. The decision will be based not only on methodological but also on practical considerations, including the resources available, and the speed with which a result is required. For example, the most appropriate study may be too expensive or take too long to provide an answer. In such circumstances a compromise will need to be made—to undertake a study which can be conducted within the budget and time available, and which delivers information that is suitable for addressing the question of interest.
{"title":"Which type of epidemiological study?","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/med/9780198814726.003.0006","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0006","url":null,"abstract":"A key issue in epidemiology is to choose an appropriate study design. The design chosen will depend on the question of interest and some designs will offer clear advantages over others. However, other factors such as current state of knowledge will be important. The decision will be based not only on methodological but also on practical considerations, including the resources available, and the speed with which a result is required. For example, the most appropriate study may be too expensive or take too long to provide an answer. In such circumstances a compromise will need to be made—to undertake a study which can be conducted within the budget and time available, and which delivers information that is suitable for addressing the question of interest.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116109029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1093/MED/9780198814726.003.0021
A. Silman, G. Macfarlane, T. Macfarlane
The preceding chapters have focused on the generation of scientific evidence using epidemiological studies. Here the emphasis is on bringing together evidence (evidence synthesis) to inform future research and health policies. The chapter considers levels and quality of scientific evidence and describes in detail how to conduct a systematic review. It reviews the ways of summarizing and evaluating evidence from epidemiological studies. Summary of evidence is needed in everyday clinical practice and for public health. We live in a time of information overload, and it is impossible to read all the available scientific journals, even on a narrow scientific topic. Simply using search terms such as ‘cancer’ will result in millions of results in Google Scholar or PubMed (a service of the US National Library of Medicine®) database. Given the ever-increasing volume of medical literature and time constraints, summary of evidence plays a big role in decision-making.
{"title":"Reviews of evidence","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/MED/9780198814726.003.0021","DOIUrl":"https://doi.org/10.1093/MED/9780198814726.003.0021","url":null,"abstract":"The preceding chapters have focused on the generation of scientific evidence using epidemiological studies. Here the emphasis is on bringing together evidence (evidence synthesis) to inform future research and health policies. The chapter considers levels and quality of scientific evidence and describes in detail how to conduct a systematic review. It reviews the ways of summarizing and evaluating evidence from epidemiological studies. Summary of evidence is needed in everyday clinical practice and for public health. We live in a time of information overload, and it is impossible to read all the available scientific journals, even on a narrow scientific topic. Simply using search terms such as ‘cancer’ will result in millions of results in Google Scholar or PubMed (a service of the US National Library of Medicine®) database. Given the ever-increasing volume of medical literature and time constraints, summary of evidence plays a big role in decision-making.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117192245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1093/med/9780198814726.003.0008
A. Silman, G. Macfarlane, T. Macfarlane
The most important aspect that will influence the validity of any epidemiological study is the careful selection of the subjects for investigation. Separate issues relate to the sampling of subjects for disease status in case–control studies, and sampling by exposure status in cohort studies. In simplest terms, the issues are who should be the cases and, given that, who should be the controls. Thus, in each instance the needs are to identify the sampling frame and then what should be the process for selecting the specific sample or subsamples needed for study. In addition to consideration of who to study, other factors such as how to identify and verify plus the size of the planned study are all topics to be addressed.
{"title":"Studies of disease causation","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/med/9780198814726.003.0008","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0008","url":null,"abstract":"The most important aspect that will influence the validity of any epidemiological study is the careful selection of the subjects for investigation. Separate issues relate to the sampling of subjects for disease status in case–control studies, and sampling by exposure status in cohort studies. In simplest terms, the issues are who should be the cases and, given that, who should be the controls. Thus, in each instance the needs are to identify the sampling frame and then what should be the process for selecting the specific sample or subsamples needed for study. In addition to consideration of who to study, other factors such as how to identify and verify plus the size of the planned study are all topics to be addressed.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"1984 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123817745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1093/MED/9780198814726.003.0011
A. Silman, G. Macfarlane, T. Macfarlane
Validity in respect of information that is collected in an epidemiological study is a measure of how accurate the data collected are, relative to an underlying truth. Classical approaches aim to ensure that the approach is both sensitive (low false negatives) and specific (low false positives) as, for example, in determining the presence or absence of a disease. A modified approach is needed where the value of an attribute is collected. Underpinning both these approaches is the need to consider what is the test of the underlying truth. The reader may already be aware of the application of validity tests in assessing the performance of diagnostic and screening tests. It is conventional to evaluate this performance in terms of the two indicators, sensitivity and specificity.
{"title":"Obtaining valid information","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/MED/9780198814726.003.0011","DOIUrl":"https://doi.org/10.1093/MED/9780198814726.003.0011","url":null,"abstract":"Validity in respect of information that is collected in an epidemiological study is a measure of how accurate the data collected are, relative to an underlying truth. Classical approaches aim to ensure that the approach is both sensitive (low false negatives) and specific (low false positives) as, for example, in determining the presence or absence of a disease. A modified approach is needed where the value of an attribute is collected. Underpinning both these approaches is the need to consider what is the test of the underlying truth. The reader may already be aware of the application of validity tests in assessing the performance of diagnostic and screening tests. It is conventional to evaluate this performance in terms of the two indicators, sensitivity and specificity.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114295019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1093/MED/9780198814726.003.0017
A. Silman, G. Macfarlane, T. Macfarlane
This chapter builds on the previous one on the analysis of descriptive epidemiological studies and illustrates statistical methods appropriate for analysis of analytical epidemiological studies. It mainly focuses on data obtained from case–control and cohort studies, but also considers other study designs presented in Chapter 6. There are also several practical examples to help with the analysis and interpretation of the results of analytical epidemiological studies. In practice, relatively little mathematical calculation is done without computers. In this chapter, however, formulae are presented for the main measures of effect together with worked examples. Indeed, when data are available in tabulated form, as opposed to raw data files, it is frequently an easy task to calculate the important measures ‘by hand’. The formulae presented will permit the reader, for example, to check or further explore data published by others.
{"title":"Introductory data analysis","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/MED/9780198814726.003.0017","DOIUrl":"https://doi.org/10.1093/MED/9780198814726.003.0017","url":null,"abstract":"This chapter builds on the previous one on the analysis of descriptive epidemiological studies and illustrates statistical methods appropriate for analysis of analytical epidemiological studies. It mainly focuses on data obtained from case–control and cohort studies, but also considers other study designs presented in Chapter 6. There are also several practical examples to help with the analysis and interpretation of the results of analytical epidemiological studies. In practice, relatively little mathematical calculation is done without computers. In this chapter, however, formulae are presented for the main measures of effect together with worked examples. Indeed, when data are available in tabulated form, as opposed to raw data files, it is frequently an easy task to calculate the important measures ‘by hand’. The formulae presented will permit the reader, for example, to check or further explore data published by others.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117013725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1093/med/9780198814726.003.0001
A. Silman, G. Macfarlane, T. Macfarlane
Epidemiology can be considered as a suite of methodologies designed to inform on aspects of health and disease in human populations. They have been designed to cope with the variability between people in the risk for diseases and their course and response to treatments. There are many aspects of epidemiological study that are challenging and the successful study requires attention to the design, conduct of the study, and analysis of the data generated. There is another view of epidemiology, which is that it is a tool for studying diseases as they occur in human populations. The focus can also be on diagnosis, outcome, and results of interventions, as well as on causes. This broader definition allows a greater scope for the kinds of question that can be addressed both by those studying the health of populations and by those whose main focus is the study of disease in patient groups.
{"title":"Scope of epidemiological enquiry and overview of main problem areas","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/med/9780198814726.003.0001","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0001","url":null,"abstract":"Epidemiology can be considered as a suite of methodologies designed to inform on aspects of health and disease in human populations. They have been designed to cope with the variability between people in the risk for diseases and their course and response to treatments. There are many aspects of epidemiological study that are challenging and the successful study requires attention to the design, conduct of the study, and analysis of the data generated. There is another view of epidemiology, which is that it is a tool for studying diseases as they occur in human populations. The focus can also be on diagnosis, outcome, and results of interventions, as well as on causes. This broader definition allows a greater scope for the kinds of question that can be addressed both by those studying the health of populations and by those whose main focus is the study of disease in patient groups.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"542 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133523750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1093/MED/9780198814726.003.0013
A. Silman, G. Macfarlane, T. Macfarlane
There are two major reasons to maximize participation in an epidemiological study. Firstly, low participation can lead to bias and secondly, inadequate numbers recruited can limit the power of the study. All other things being equal, lower participation will increase the influence of non-response bias in a study. However, it should be noted that even in studies with high participation there exists a possibility of selection bias if non-participants are markedly different from participants in aspects that are key to the conditions being studied. There are several ways of boosting participation which should be embedded into the study design from the start, since chasing up to increase participation is resource-expensive.
{"title":"Participation in epidemiology studies","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/MED/9780198814726.003.0013","DOIUrl":"https://doi.org/10.1093/MED/9780198814726.003.0013","url":null,"abstract":"There are two major reasons to maximize participation in an epidemiological study. Firstly, low participation can lead to bias and secondly, inadequate numbers recruited can limit the power of the study. All other things being equal, lower participation will increase the influence of non-response bias in a study. However, it should be noted that even in studies with high participation there exists a possibility of selection bias if non-participants are markedly different from participants in aspects that are key to the conditions being studied. There are several ways of boosting participation which should be embedded into the study design from the start, since chasing up to increase participation is resource-expensive.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133325621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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