Difference between revisions of "Peer review"
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<section begin=glossary /> | <section begin=glossary /> | ||
| − | :'''Peer review''' in [[open assessment]] is a [[method]] for evaluating [[uncertainty|uncertainties]] that are not explicitly captured in the [[definition]] of an object (typically an [[assessment]] or a [[variable]]). Technically, it is a [[discussion]] on the Talk page of the object | + | :'''Peer review''' in [[open assessment]] is a [[method]] for evaluating [[uncertainty|uncertainties]] that are not explicitly captured in the [[definition]] of an object (typically an [[assessment]] or a [[variable]]). Technically, it is a [[discussion]] on the Talk page of the object. In the case of a variable, it has the following [[statement]]: |
:: "The definition of this object is based on the state-of-the-art scientific knowledge and methods. The data used is representative and unbiased. The causalities are described in a well-founded way. The formula correctly describes how the result can be calculated based on the data and causalities. Overall, the information in the definition reflects the current scientific understanding and is unlikely to substantially change because of some existing information that is omitted." | :: "The definition of this object is based on the state-of-the-art scientific knowledge and methods. The data used is representative and unbiased. The causalities are described in a well-founded way. The formula correctly describes how the result can be calculated based on the data and causalities. Overall, the information in the definition reflects the current scientific understanding and is unlikely to substantially change because of some existing information that is omitted." | ||
<section end=glossary /> | <section end=glossary /> | ||
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* It is based on an evaluation of the object by peer researchers. | * It is based on an evaluation of the object by peer researchers. | ||
* It is not in conflict with [[open assessment]]. | * It is not in conflict with [[open assessment]]. | ||
| − | * It | + | * It evaluates the relationship of the [[scope]] and the [[definition]]. The question is whether the relationship is well-founded according to the current scientific information. |
==Definition== | ==Definition== | ||
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===Output=== | ===Output=== | ||
| − | The output is a statement about the | + | The output is a statement about the relationship of the scope and the definition in the light of the current scientific information. |
===Rationale=== | ===Rationale=== | ||
| + | |||
| + | In the case of a variable, the definition (the quality of content of the data, causalities, and formula attributes) is evaluated against the scope, which is fixed. In the case of a nugget, the scope is evaluated against the definition (i.e. the scientific work performed), which is fixed. Thus, the question is about how much it is possible to generalise from the results of a study. | ||
==Result== | ==Result== | ||
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In the numerical VOI analysis, the result distribution is divided into n equally probable bins. The discrepancy test asks, what the probability is that the result is in a higher (lower) bin than the external reference. If both probabilities are fairly high, it is unlikely that the result is falsely too narrow and biased. What is "fairly high", remains to be determined. | In the numerical VOI analysis, the result distribution is divided into n equally probable bins. The discrepancy test asks, what the probability is that the result is in a higher (lower) bin than the external reference. If both probabilities are fairly high, it is unlikely that the result is falsely too narrow and biased. What is "fairly high", remains to be determined. | ||
| + | |||
| + | |||
| + | '''Peer review of the scope of a nugget | ||
| + | |||
| + | [[Nugget]] is a special kind of object in the sense that the definition typically describes a particular study that has been performed. Therefore, it is not possible to evaluate the definition as such, because what was done was done. Instead, the interesting question is about the generalisability of the results. If they are not at all generalisable, the study is worthless. Let's take an example: there is a study that is an epidemiological case-control study with a questionnaire from all and and blood measurements of a pollutant from a subset of patients. If the blood measurements were done with an unreliable method, the results cannot be generalised even to the patient that was studied, i.e. we don't learn anything about the patient's pollutant levels even if we know the blood test result. If the blood test is good, we can believe that it reflects the patient's true pollutant level in blood. The next question is whether the patient is representative of his/her group, i.e. whether the result can be generalised to the whole group of cases or controls. In this case, blood was drawn only from a fraction of those who returned the questionnaire, and there is doubt whether the subgroup was somehow a biased sample of the bigger group. If it is biased, we cannot generalise to the group of cases or controls. There is also the question whether the group in the study actually reflects the population of interest. If the controls are drawn from a different population than the cases, it is doubtful whether they can be used as controls to compute odds ratios of the pollutant causing the disease of concern. | ||
| + | |||
| + | Thus, the peer review of a nugget aims to answer this question: "To what question(s) does the nugget actually answer reliably, based on the current scientific understanding?" | ||
===Management=== | ===Management=== | ||
Revision as of 19:21, 1 February 2009
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This page is about peer review in open assessment. For other uses, see Peer review in Wikipedia.
<section begin=glossary />
- Peer review in open assessment is a method for evaluating uncertainties that are not explicitly captured in the definition of an object (typically an assessment or a variable). Technically, it is a discussion on the Talk page of the object. In the case of a variable, it has the following statement:
- "The definition of this object is based on the state-of-the-art scientific knowledge and methods. The data used is representative and unbiased. The causalities are described in a well-founded way. The formula correctly describes how the result can be calculated based on the data and causalities. Overall, the information in the definition reflects the current scientific understanding and is unlikely to substantially change because of some existing information that is omitted."
<section end=glossary />
Contents
Scope
What is such a method for gaining acceptance to an object from the scientific community that fulfils the following criteria?
- It is based on an evaluation of the object by peer researchers.
- It is not in conflict with open assessment.
- It evaluates the relationship of the scope and the definition. The question is whether the relationship is well-founded according to the current scientific information.
Definition
Input
The input is an object to-be-evaluated.
Output
The output is a statement about the relationship of the scope and the definition in the light of the current scientific information.
Rationale
In the case of a variable, the definition (the quality of content of the data, causalities, and formula attributes) is evaluated against the scope, which is fixed. In the case of a nugget, the scope is evaluated against the definition (i.e. the scientific work performed), which is fixed. Thus, the question is about how much it is possible to generalise from the results of a study.
Result
Procedure
Peer review of the definition
Peer review in open assessment is a method for evaluating uncertainties that are not explicitly captured in the definition of an object (typically an assessment or a variable). Technically, it is a discussion on the Talk page of the object and has the following statement:
- "The definition of this object is based on the state-of-the-art scientific knowledge and methods. The data used is representative and unbiased. The causalities are described in a well-founded way. The formula correctly describes how the result can be calculated based on the data and causalities. Overall, the information in the definition reflects the current scientific understanding and is unlikely to substantially change because of some existing information that is omitted."
The following classification can be used for each attribute:
- The attribute description is according to the state-of-the-art.
- The attribute description has minor deficiencies.
- The attribute description is unreliable because of its major deficiencies.
- Cannot be evaluated.
Peer review of the result based on an external reference
Peer review can be performed for the result, if the peer has an alternative way of deriving the result. Then, it can be used as an external reference for evaluating the discrepancy between the result and the external reference. Of course, the validity of this review is totally dependent on the validity of the external reference. Informativeness and calibration can be evaluated against the reference.
In addition, a discrepancy test can be performed. The aim of this test is to evaluate whether it is credible to believe the result of a value of information (VOI) analysis related to the variable. The VOI analysis gives low values if the need to improve the model is low. A problem with the VOI analysis is that if the model is too bad, it might also give low VOI estimates, thus falsely implying a good model. Fortunately there are a few indirect methods to evaluate this. One way is to do a peer review of the definition. Another one is to do a discrepancy test. It measures whether the external reference is essentially included in the current result of the variable. If this is the case, it is unlikely that the VOI will be underestimated. the Kolmogorov–Smirnov test is a relevant discrepancy test.
In the numerical VOI analysis, the result distribution is divided into n equally probable bins. The discrepancy test asks, what the probability is that the result is in a higher (lower) bin than the external reference. If both probabilities are fairly high, it is unlikely that the result is falsely too narrow and biased. What is "fairly high", remains to be determined.
Peer review of the scope of a nugget
Nugget is a special kind of object in the sense that the definition typically describes a particular study that has been performed. Therefore, it is not possible to evaluate the definition as such, because what was done was done. Instead, the interesting question is about the generalisability of the results. If they are not at all generalisable, the study is worthless. Let's take an example: there is a study that is an epidemiological case-control study with a questionnaire from all and and blood measurements of a pollutant from a subset of patients. If the blood measurements were done with an unreliable method, the results cannot be generalised even to the patient that was studied, i.e. we don't learn anything about the patient's pollutant levels even if we know the blood test result. If the blood test is good, we can believe that it reflects the patient's true pollutant level in blood. The next question is whether the patient is representative of his/her group, i.e. whether the result can be generalised to the whole group of cases or controls. In this case, blood was drawn only from a fraction of those who returned the questionnaire, and there is doubt whether the subgroup was somehow a biased sample of the bigger group. If it is biased, we cannot generalise to the group of cases or controls. There is also the question whether the group in the study actually reflects the population of interest. If the controls are drawn from a different population than the cases, it is doubtful whether they can be used as controls to compute odds ratios of the pollutant causing the disease of concern.
Thus, the peer review of a nugget aims to answer this question: "To what question(s) does the nugget actually answer reliably, based on the current scientific understanding?"
Management
The peer review discussion has the following form:
Peer review
| Statements:
Resolution: Resolution not yet found. (A stable resolution, when found, should be updated to the main page.) |
| Argumentation:
←1: The data used is representative and unbiased. --Jouni 11:37, 16 January 2009 (EET) ←2 The causalities are described in a well-founded way. --Jouni 23:04, 19 January 2009 (EET)
←3: The formula correctly describes how the result can be calculated based on the data and causalities. --Jouni 23:04, 19 January 2009 (EET) ⇤5: The issue described in argument 4 is missing. --Jouni 11:37, 16 January 2009 (EET)
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