Misclassification rates of dimension reduction classifiers using the trimmed datasets.

<p>Mean misclassification rates for each of the dimension reduction-based methods using the trimmed dataset to build the classification model. <b>A</b>) Is from the OC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Lee1" target="_blank">[16]</a>, <b>B</b>) is from the Gaucher disease dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Hendriks1" target="_blank">[46]</a>, <b>C</b>) is from the LC datasets and <b>D</b>) is from the CRC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Schleif1" target="_blank">[14]</a>. Blue circles illustrate PLS-LDA classification results, red triangles are from a PLS-RF classifier and purple crosses show results obtained from a PCA-LDA classifier.</p

Comparison of PLS and PCA for dimension reduction.

<p>These plots demonstrate the capacity PLS has to separate classes based on the top 30 variables (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone-0024973-g004" target="_blank"><i>Figure 4A</i></a>) in the Gaucher dataset when compared to PCA (Note that this class separation is being heavily influenced by the loadings highlighted in Blue. Additionally, the vectors highlighted in red explain the within class variation in the control group. This is a key advantage PLS has over other methods.</p

SVM tuning results.

<p>The performance summary (<b>MCR</b> = Misclassification rate) of a SVM-based classifier for both the full dataset (“full”) and the trimmed dataset (“trimmed”) that underwent variable selection using a univariate moderated t-statistic. These are mean values based on 1000 bootstrap samples for each dataset except for the OC data which used 200 bootstrap samples.</p

Misclassification rates of dimension reduction classifiers using the untrimmed datasets.

<p>Mean misclassification rates for each of the dimension reduction-based methods using the full dataset (all variables) in the dataset to build the classification model. <b>A</b>) Is from the OC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Lee1" target="_blank">[16]</a>, <b>B</b>) is from the Gaucher disease dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Hendriks1" target="_blank">[46]</a>, <b>C</b>) is from the LC datasets and <b>D</b>) is from the CRC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Schleif1" target="_blank">[14]</a>. Blue circles illustrate PLS-LDA classification results, red triangles are from a PLS-RF classifier and purple crosses show results obtained from a PCA-LDA classifier.</p

Dimension Reduction Classifier Performance Summary.

<p>The performance summary (MCR = Misclassification rate, AUC = Area under the curve, Sens = Sensitivity, Spec = Specificity, No. Components = the number of components used in the model) of each classifier for both the full dataset (“full”) and the trimmed dataset (“trimmed”) that underwent variable selection using a univariate moderated t-statistic. These are mean values based on 1000 bootstrap samples for each dataset except the OC data which used 200 bootstrap samples.</p

Association between clinical targets of care and progression of CKD.

<p>Association between clinical targets of care and progression of CKD.</p

Association between process of care and progression of CKD.

<p>Association between process of care and progression of CKD.</p

CKD stage progression from the first observation to the last observation.

<p><b>(1,630 patients).</b> Shaded bars represent stable patients whereas those below the shaded bars represent improved CKD stage, and those above the bars represent patients whose CKD stage progressed.</p

Baseline of characteristics of patients with the two or more and with only a single observation.

<p>Baseline of characteristics of patients with the two or more and with only a single observation.</p

Study flow.

<p>Study flow.</p