Risk groups defined by Recursive Partitioning Analysis of patients with colorectal adenocarcinoma treated with colorectal resection

Chang, Yun-Jau; Chen, Li‐Ju; Chang, Yao-Jen; Chung, Kuo‐Piao; Lai, Mei‐Shu

doi:10.1186/1471-2288-12-2

Cited by 21 publications

(23 citation statements)

References 28 publications

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“…Although such methods have been used in this way in other areas of medicine (Chang et al, 2012; Chao et al, 2012), applications to MDD have so far been based on samples too small to realize the potential of the methods (Andreescu et al, 2008a; Jain et al, 2013; Nelson et al, 2012; Rabinoff et al, 2011; Riedel et al, 2011). Yet promising preliminary results exist in clinical (Moos & Cronkite, 1999; Perlis, 2013) and community epidemiological (Angst et al, 2011; van Loo et al, 2014) studies designed to predict MDD persistence-severity.…”

Section: Introductionmentioning

confidence: 99%

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

Kessler

Loo²,

Wardenaar

et al. 2016

Epidemiol Psychiatr Sci

142

110

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Aims Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. Methods We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalized) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Results Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention versus control) or differential treatment outcomes (i.e., intervention A versus intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalized treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Conclusions Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

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Section: Introductionmentioning

confidence: 99%

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

Kessler

Loo²,

Wardenaar

et al. 2016

Epidemiol Psychiatr Sci

142

110

View full text Add to dashboard Cite

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“…Such methods have been used in other areas of medicine [17,18] and relatively simple applications have been used in psychiatry to predict depression treatment response [19-23] and suicidality. [24-26] …”

Section: Introductionmentioning

confidence: 99%

Major Depressive Disorder Subtypes to Predict Long-Term Course

et al. 2014

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Background Variation in course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. Methods Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs) followed by k-means cluster analysis, are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) Surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode; number of with an episode lasting most of the year) and severity (hospitalization for MDD; disability due to MDD). Results Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity and was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor. Conclusions Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data.

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“…The latter methods have been useful in discovering stable synergistic predictors of clinical outcomes in others areas of medicine (Chang et al ., 2012; Chao et al ., 2012). Other than small studies of depression treatment response (Andreescu et al ., 2008; Jain et al ., 2013; Nelson et al ., 2012; Rabinoff et al ., 2011; Riedel et al ., 2011), though, we are aware of only one previous study using machine learning to search for depression subtypes in predicting course of illness.…”

mentioning

confidence: 99%

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

et al. 2014

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Background Although variation in long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about comorbid conditions. The current report presents results on this question. Methods Data come from 8,261 respondents with lifetime DSM-IV MDD in the WHO World Mental Health (WMH) Surveys. Outcomes include four retrospectively-reported measures of persistence-severity of course (years in episode; years in chronic episodes, hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge, and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously-detected subtypes with information about prior comorbidity to predict these outcomes. Results Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found 3 clusters with consistently high, intermediate, or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization, and disability. This high-risk cluster had both higher sensitivity and likelihood-ratio positive (relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of comorbidity as predictors. Conclusions Although results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence-severity, replication is need with prospective data to confirm this preliminary conclusion.

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Risk groups defined by Recursive Partitioning Analysis of patients with colorectal adenocarcinoma treated with colorectal resection

Cited by 21 publications

References 28 publications

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

Major Depressive Disorder Subtypes to Predict Long-Term Course

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

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