Editing 2652: Proxy Variable

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In this comic, [[Hairy]] is discussing use of a proxy variable with [[Cueball]]. In statistics, a {{w|proxy variable}} is used as a stand-in for one or more other variables that are difficult to measure. In order to be useful as such, proxy variables must be correlated with what they are intended to represent. For example, a drug might aim to reduce deaths from a slow-acting disease. But testing if it reduces deaths might take many years, so researchers might test for a proxy outcome instead, like whether the drug appears to mitigate loss of bone density or cell-damage. Physicians use blood pressure as one of many proxies for cardiovascular health.
 
In this comic, [[Hairy]] is discussing use of a proxy variable with [[Cueball]]. In statistics, a {{w|proxy variable}} is used as a stand-in for one or more other variables that are difficult to measure. In order to be useful as such, proxy variables must be correlated with what they are intended to represent. For example, a drug might aim to reduce deaths from a slow-acting disease. But testing if it reduces deaths might take many years, so researchers might test for a proxy outcome instead, like whether the drug appears to mitigate loss of bone density or cell-damage. Physicians use blood pressure as one of many proxies for cardiovascular health.
  
βˆ’
Hairy is dismissing the question of whether they are studying the right variable as too expensive to answer. This is deeply ironic and thus satirical, because good {{w|experiment design}} requires sufficient attention to the robustness of all the involved parts of an experiment, even if the expense may be prohibitive. This comic might be referring to the recent discovery of [https://www.science.org/content/article/potential-fabrication-research-images-threatens-key-theory-alzheimers-disease nearly two decades] of allegedly fraudulent {{w|Alzheimer's disease}} research supporting a mistaken proxy hypothesis.
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Hairy is dismissing the question of whether they are studying the right variable as too expensive to answer. This is deeply ironic and thus satirical, because good {{w|experiment design}} requires sufficient attention to the robustness of all the involved parts of an experiment, even if the expense may be prohibitive. This comic might be referring to the recent discovery of [https://www.science.org/content/article/potential-fabrication-research-images-threatens-key-theory-alzheimers-disease nearly two decades] of likely fraudulent {{w|Alzheimer's disease}} research supporting a mistaken proxy hypothesis.
  
 
Choosing the wrong proxy variable might make the research misleading, irrelevant, or as the title text suggests, answer the wrong question. Separating correlation from {{w|Causality|causation}} is necessary when interpreting proxy variable results to make sure the question they answer is known. Mere correlation instead of {{w|Causal analysis|authentic causation}} yields weaker results. {{w|Exploratory causal analysis}} can assist with finding useful proxy variables, but is difficult for the layperson to interpret and can be misleading, because even if performed correctly, a {{w|combinatorial explosion}} of possible proxy variables can make traditional {{w|statistical significance}} analysis fail, requiring {{w|F-score}}s or similar measures. The history of pharmaceutical research is largely a graveyard of failed proxy hypotheses; that is one of the reasons for [https://clinicaltrials.gov/ct2/manage-recs/fdaaa experiment registration regulations.]
 
Choosing the wrong proxy variable might make the research misleading, irrelevant, or as the title text suggests, answer the wrong question. Separating correlation from {{w|Causality|causation}} is necessary when interpreting proxy variable results to make sure the question they answer is known. Mere correlation instead of {{w|Causal analysis|authentic causation}} yields weaker results. {{w|Exploratory causal analysis}} can assist with finding useful proxy variables, but is difficult for the layperson to interpret and can be misleading, because even if performed correctly, a {{w|combinatorial explosion}} of possible proxy variables can make traditional {{w|statistical significance}} analysis fail, requiring {{w|F-score}}s or similar measures. The history of pharmaceutical research is largely a graveyard of failed proxy hypotheses; that is one of the reasons for [https://clinicaltrials.gov/ct2/manage-recs/fdaaa experiment registration regulations.]

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