Pub Date : 2018-10-01DOI: 10.1093/med/9780198814726.003.0012
Alan J. Silman, Gary J. Macfarlane, Tatiana V Macfarlane
There are many sources of variability in measures in epidemiology. Subjects can vary in their response because of real change. Alternatively, the method of collecting data may be subject to variability because of differences in techniques or the inherent difficulty in some measures in ensuring consistency. Too much variability can introduce ‘noise’, making it difficult to find differences between populations. There may be a variety of reasons for failure to obtain consistent or reproducible results. This chapter describes various methods which can be employed to calculate the level of variability in measures used and how to minimize the sources of this.
{"title":"Repeatability","authors":"Alan J. Silman, Gary J. Macfarlane, Tatiana V Macfarlane","doi":"10.1093/med/9780198814726.003.0012","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0012","url":null,"abstract":"There are many sources of variability in measures in epidemiology. Subjects can vary in their response because of real change. Alternatively, the method of collecting data may be subject to variability because of differences in techniques or the inherent difficulty in some measures in ensuring consistency. Too much variability can introduce ‘noise’, making it difficult to find differences between populations. There may be a variety of reasons for failure to obtain consistent or reproducible results. This chapter describes various methods which can be employed to calculate the level of variability in measures used and how to minimize the sources of this.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"27 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":"127975998","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/9780198814726.003.0024
A. Silman, G. Macfarlane, T. Macfarlane
Epidemiological studies can be very expensive, especially from large populations with multicentre recruitment. The researcher will need to ensure that there are adequate resources, allowing for the fact that things will not always go to plan, but making sure that the research is value for money. What is considered a reasonable cost will also depend on how strong the rationale is for conducting the study. Although in theory the study design influences the costs, in practice the resources available will often constrain the methodological choices. Costing an epidemiological study accurately at the start is vital. There are several ways to maximize the use of resources to ensure the study is efficient.
{"title":"The costs of an epidemiological study","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/med/9780198814726.003.0024","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0024","url":null,"abstract":"Epidemiological studies can be very expensive, especially from large populations with multicentre recruitment. The researcher will need to ensure that there are adequate resources, allowing for the fact that things will not always go to plan, but making sure that the research is value for money. What is considered a reasonable cost will also depend on how strong the rationale is for conducting the study. Although in theory the study design influences the costs, in practice the resources available will often constrain the methodological choices. Costing an epidemiological study accurately at the start is vital. There are several ways to maximize the use of resources to ensure the study is efficient.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"182 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":"133587073","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/9780198814726.003.0003
A. Silman, G. Macfarlane, T. Macfarlane
In comparing rates between populations, it is important that one is comparing ‘like with like’. One population may be considerably older than a population to which it is compared and therefore it would not be surprising that mortality rates were higher. Instead it is more useful to make comparisons taking account of differences in characteristics such as age or gender. The same considerations apply to examining disease rates over time in a given population. If the characteristics of the population change over time (e.g. the population gets older), this needs to be considered. To formulate hypotheses, the rate of a disease under study in a population may be compared with the rate in other populations, or in the same population at difierent time points. If the rates vary significantly between populations or are changing within a population, then this provides impetus for investigating the reasons underlying these differences or changes.
{"title":"Comparing rates between and within populations","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/med/9780198814726.003.0003","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0003","url":null,"abstract":"In comparing rates between populations, it is important that one is comparing ‘like with like’. One population may be considerably older than a population to which it is compared and therefore it would not be surprising that mortality rates were higher. Instead it is more useful to make comparisons taking account of differences in characteristics such as age or gender. The same considerations apply to examining disease rates over time in a given population. If the characteristics of the population change over time (e.g. the population gets older), this needs to be considered. To formulate hypotheses, the rate of a disease under study in a population may be compared with the rate in other populations, or in the same population at difierent time points. If the rates vary significantly between populations or are changing within a population, then this provides impetus for investigating the reasons underlying these differences or changes.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"15 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":"115256426","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/9780198814726.003.0023
A. Silman, G. Macfarlane, T. Macfarlane
There are several major ethical issues that face an epidemiological study. There is always the challenge in studying free-living individuals in the modern society, of balancing the needs for robust methods with individual freedoms. The key concerns reflect ensuring an appropriate consent process, maintaining confidentiality, and minimizing any negative consequences for a participant. The most commonly collected information for an epidemiological study come either from material already available in databases, material such as hard copies of records that can have key data items extracted, or data that is gathered directly from the subject. Occasionally a limited physical examination is undertaken. Much less often, there is a requirement to take samples of biological fluid such as blood and urine, or to undergo simple investigations such as electrocardiography or plain radiography, but even such investigations are typically associated with trivial risk to health.
{"title":"Ethical issues in epidemiology","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/MED/9780198814726.003.0023","DOIUrl":"https://doi.org/10.1093/MED/9780198814726.003.0023","url":null,"abstract":"There are several major ethical issues that face an epidemiological study. There is always the challenge in studying free-living individuals in the modern society, of balancing the needs for robust methods with individual freedoms. The key concerns reflect ensuring an appropriate consent process, maintaining confidentiality, and minimizing any negative consequences for a participant. The most commonly collected information for an epidemiological study come either from material already available in databases, material such as hard copies of records that can have key data items extracted, or data that is gathered directly from the subject. Occasionally a limited physical examination is undertaken. Much less often, there is a requirement to take samples of biological fluid such as blood and urine, or to undergo simple investigations such as electrocardiography or plain radiography, but even such investigations are typically associated with trivial risk to health.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"30 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":"130326116","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/9780198814726.003.0009
A. Silman, G. Macfarlane, T. Macfarlane
Primary data collection is challenging and with increasing electronic data capture in routine healthcare and other aspects of life, it is possible to address several epidemiological questions by robust analysis of such ‘secondary data’. There are considerable advantages in terms of scope, size, and speed of study to be balanced against the quality and depth of using primary data. Even when such direct contact is not required, there is often the need to extract necessary information from individual subject records such as medical files. There is often no alternative source of information, although the greater digitization of information is changing that scenario with the potential that the availability of such information might preclude the need for primary data.
{"title":"Use of secondary data","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/med/9780198814726.003.0009","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0009","url":null,"abstract":"Primary data collection is challenging and with increasing electronic data capture in routine healthcare and other aspects of life, it is possible to address several epidemiological questions by robust analysis of such ‘secondary data’. There are considerable advantages in terms of scope, size, and speed of study to be balanced against the quality and depth of using primary data. Even when such direct contact is not required, there is often the need to extract necessary information from individual subject records such as medical files. There is often no alternative source of information, although the greater digitization of information is changing that scenario with the potential that the availability of such information might preclude the need for primary data.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"11 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":"131896455","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/9780198814726.003.0010
A. Silman, G. Macfarlane, T. Macfarlane
Collecting accurate and comprehensive information both direct from the participants, or indirectly from records or other data is one of the major challenges to a successful epidemiological study. Epidemiological information comes from a variety of sources. These may be conveniently divided into those that are available from previously documented data and those that require the gathering of new information. Examples of the former include extracting information about individuals from their medical records, occupational records, and similar data sources. Design and choice of delivery of patient data capture forms by direct interview or telephone, by post, email, or other electronic means all require considerable thought and pilot testing. Attention to the specific wording of certain questions is crucial. This chapter therefore focuses on the issues surrounding the collection of information that otherwise would not be available: primary data collection.
{"title":"Collecting information","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/med/9780198814726.003.0010","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0010","url":null,"abstract":"Collecting accurate and comprehensive information both direct from the participants, or indirectly from records or other data is one of the major challenges to a successful epidemiological study. Epidemiological information comes from a variety of sources. These may be conveniently divided into those that are available from previously documented data and those that require the gathering of new information. Examples of the former include extracting information about individuals from their medical records, occupational records, and similar data sources. Design and choice of delivery of patient data capture forms by direct interview or telephone, by post, email, or other electronic means all require considerable thought and pilot testing. Attention to the specific wording of certain questions is crucial. This chapter therefore focuses on the issues surrounding the collection of information that otherwise would not be available: primary data collection.\u0000","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"71 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":"115649724","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/9780198814726.003.0007
A. Silman, G. Macfarlane, T. Macfarlane
Having conducted a specific study design, the task is then to use the information collected to provide an effect measure which quantifies the magnitude of the association between the exposure(s) of interest and the disease under study. Epidemiological studies, and in particular their subsequent analysis, are therefore aimed at quantifying the level of increased risk when exposed to a particular factor, as this chapter explains. The effect measure which can be obtained to quantify the strength of the association, varies according to the type of study conducted. Just because there is a relationship between exposure and disease (even a strong one) does not mean that the relationship is causal.
{"title":"Quantifying the association between exposures and diseases","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/med/9780198814726.003.0007","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0007","url":null,"abstract":"Having conducted a specific study design, the task is then to use the information collected to provide an effect measure which quantifies the magnitude of the association between the exposure(s) of interest and the disease under study. Epidemiological studies, and in particular their subsequent analysis, are therefore aimed at quantifying the level of increased risk when exposed to a particular factor, as this chapter explains. The effect measure which can be obtained to quantify the strength of the association, varies according to the type of study conducted. Just because there is a relationship between exposure and disease (even a strong one) does not mean that the relationship is causal.","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":"130094867","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/9780198814726.003.0018
Alan J. Silman, Gary J. Macfarlane, Tatiana V Macfarlane
An apparent relationship between a disease and a risk factor may be explained (confounded) by their joint association with an intermediate third ‘true’ risk factor. The issue here is not one of impaired validity; if the study had been carefully conducted then the relationship observed was correct and the problem of confounding is therefore one of interpretation. Confounding can only be proved after appropriate analysis. Further confounding as an explanation of an association is not (or is only extremely rarely) an all-or-nothing phenomenon. The effect of confounding will be, more usually, to alter the strength of an apparent relationship between two variables (e.g. risk factor and disease).
{"title":"Confounding","authors":"Alan J. Silman, Gary J. Macfarlane, Tatiana V Macfarlane","doi":"10.1093/med/9780198814726.003.0018","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0018","url":null,"abstract":"An apparent relationship between a disease and a risk factor may be explained (confounded) by their joint association with an intermediate third ‘true’ risk factor. The issue here is not one of impaired validity; if the study had been carefully conducted then the relationship observed was correct and the problem of confounding is therefore one of interpretation. Confounding can only be proved after appropriate analysis. Further confounding as an explanation of an association is not (or is only extremely rarely) an all-or-nothing phenomenon. The effect of confounding will be, more usually, to alter the strength of an apparent relationship between two variables (e.g. risk factor and disease).","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"12 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":"117218958","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/9780198814726.003.0019
Alan J. Silman, Gary J. Macfarlane, Tatiana V Macfarlane
There are several reasons (e.g. sources of bias), why a study’s results may deviate from the truth. Recruitment and/or follow-up, which is non-random as to who does not provide data is a major source of bias. Other sources include observer preferences and the subject’s answers being informed, for example, by their current or prior status. It is not easy to undertake the perfect, bias-free, study. The challenge is to design the study to minimize the likelihood of bias and to undertake the necessary subanalyses, where appropriate, to obtain a sense of how important a particular bias might have been in explaining the results obtained.
{"title":"Bias","authors":"Alan J. Silman, Gary J. Macfarlane, Tatiana V Macfarlane","doi":"10.1093/med/9780198814726.003.0019","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0019","url":null,"abstract":"There are several reasons (e.g. sources of bias), why a study’s results may deviate from the truth. Recruitment and/or follow-up, which is non-random as to who does not provide data is a major source of bias. Other sources include observer preferences and the subject’s answers being informed, for example, by their current or prior status. It is not easy to undertake the perfect, bias-free, study. The challenge is to design the study to minimize the likelihood of bias and to undertake the necessary subanalyses, where appropriate, to obtain a sense of how important a particular bias might have been in explaining the results obtained.","PeriodicalId":186966,"journal":{"name":"Epidemiological Studies: A Practical Guide","volume":"104 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":"127148871","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/9780198814726.003.0005
A. Silman, G. Macfarlane, T. Macfarlane
This chapter reviews the options for population selection in undertaking investigations aimed at estimating the occurrence of a disease. In terms of measuring disease occurrence, an important issue will be determining the appropriate target population. The choice will depend on being able to access the population, how representative it is, the size of the population, and the population data accuracy. It will not always be possible to use an ‘ideal’ population, there will be different options and the advantages and disadvantages of each will need to be determined. The same principles apply, however, if the object of the study is to investigate the occurrence of a risk factor, such as cigarette smoking, or other human attributes.
{"title":"Studies of disease occurrence","authors":"A. Silman, G. Macfarlane, T. Macfarlane","doi":"10.1093/med/9780198814726.003.0005","DOIUrl":"https://doi.org/10.1093/med/9780198814726.003.0005","url":null,"abstract":"This chapter reviews the options for population selection in undertaking investigations aimed at estimating the occurrence of a disease. In terms of measuring disease occurrence, an important issue will be determining the appropriate target population. The choice will depend on being able to access the population, how representative it is, the size of the population, and the population data accuracy. It will not always be possible to use an ‘ideal’ population, there will be different options and the advantages and disadvantages of each will need to be determined. The same principles apply, however, if the object of the study is to investigate the occurrence of a risk factor, such as cigarette smoking, or other human attributes.","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":"126441785","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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