Difference between revisions of "Stratified Intervention Assignment"

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<B>See</B> [[Intervention Assignment]], [[Stratification Method]], [[Stratified n-Fold Cross-Validation]], [[Non-Stratified Intervention Assignment]].
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A [[Stratified Intervention Assignment]] is a [[Intervention Assignment]] that is a [[Stratified Sampling Task]].
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* <B>See</B> [[Stratified Random Sampling Task]], [[Stratified K-Fold Cross-Validation]], [[Stratified Psychiatry]].
 
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== References ==
 
== References ==
  
=== 2013 ===
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=== 2017 ===
* http://www.drcath.net/toolkit/intervention.html
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* (Joyce et al., 2017) ⇒ [[Dan W. Joyce]], [[Angie A. Kehagia]], [[Derek K. Tracy]], [[Jessica Proctor]], and [[Sukhwinder S. Shergill]] (2017). [https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-016-1116-1 "Realising stratified psychiatry using multidimensional signatures and trajectories"]. Journal of translational medicine, 15(1), 15. [https://doi.org/10.1186/s12967-016-1116-1 DOI:10.1186/s12967-016-1116-1]
** [[Stratified Intervention Assignment|Stratification]] is the grouping of patients according to a known characteristic affecting the outcome before randomisation to groups. This ensures equal representation of the characteristics in each group.
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** QUOTE: For example, given a large number of [[patient]]s whose [[PANSS score]]s (a 30-[[item instrument]]) are used to [[populate]] a 30-[[dimensional space]], we might seek a set of [[prototype]]s that represent [[cluster]]s of [[patient]]s that are sufficiently similar such that [[Stratified Intervention Assignment|stratified assignment to intervention]] occurs by [[similarity]] in [[psychopathology]]. Of note, this represents exploration and discovery of [[candidate prototype]]s and the [[model]] is constructed by [[Unsupervised Learning|‘unsupervised’ learning]]. In [[#FIG1|Fig. 1b]], [[stratification]] of a new, previously unknown [[patient]] would be some [[function]] of the distance to [[discovered prototype]]s (solid green and red lines) <P>
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{|style="border:  0px;  text-align:center;  border-spacing:  1px;  margin:  1em  auto;  width:  90%px;"
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|<html><img src="https://media.springernature.com/full/springer-static/image/art%3A10.1186%2Fs12967-016-1116-1/MediaObjects/12967_2016_1116_Fig1_HTML.gif"</html>
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|+  align="bottom"  style="caption-side:  bottom;  text-align:  left;  font-weight:  normal;"  |'''Figure  1:''' '''a''' [[Schematic representation]] of two [[patient]]s’ signatures at time 1 (before [[intervention]]) and time 2 (after [[intervention]]) with connecting lines representing [[trajectory]]. '''b''' A [[space]] of [[patient signature]]s along [[dimension]]s of [[positive]] and [[negative symptom]]s, with black and grey points representing [[patient]]s belonging to two tentative [[cluster]]s of patients. Diamonds show the [[centroid]]s ([[prototype]]s) of the two [[patient categori]]es. Dotted lines represent between [[patient-signature similarity]] and solid lines represent [[class membership]] as [[proportional]] to the [[distance]] of a given [[patient]] to the nearest prototype/[[centroid]]. The blue solid line represents a [[linear discriminant]] separating the two classes such that the overall [[misclassification error]] is minimised, as might be estimated or learned by, for example, a [[support vector machine]] or [[linear discriminant analysis]].
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|}
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[[Category:Concept]]

Latest revision as of 01:21, 15 February 2020

A Stratified Intervention Assignment is a Intervention Assignment that is a Stratified Sampling Task.



References

2017

Figure 1: a Schematic representation of two patients’ signatures at time 1 (before intervention) and time 2 (after intervention) with connecting lines representing trajectory. b A space of patient signatures along dimensions of positive and negative symptoms, with black and grey points representing patients belonging to two tentative clusters of patients. Diamonds show the centroids (prototypes) of the two patient categories. Dotted lines represent between patient-signature similarity and solid lines represent class membership as proportional to the distance of a given patient to the nearest prototype/centroid. The blue solid line represents a linear discriminant separating the two classes such that the overall misclassification error is minimised, as might be estimated or learned by, for example, a support vector machine or linear discriminant analysis.