A sequential logistic regression classifier based on mixed effects with applications to longitudinal data

Zhang, Xin; Jeske, Daniel R.; Li, Jun; Wong, Vance

doi:10.1016/j.csda.2015.08.009

Cited by 11 publications

(18 citation statements)

References 10 publications

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“…There is a rich literature in medical statistics covering the modelling of irregular univariate or multivariate repeated (longitudinal) data with mixed-effects models (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) as well as their application to derive more flexible approaches for discriminant analyses for clinical data (12)(13)(14)(15)(16)(31)(32)(33)(34)(35). Also, in recent years there have been studies that applied mixed-effects models to infer underlying structures of multivariate change or to derive flexible or even dynamic classification approaches (11,16,22,24,27) also in diagnosis of neurodegeneration (22,32,35). The approaches for sequential classification with mixed-effects models in resent studies (11,16) relied purely on acquired evidence without considering potential benefits of measurements.…”

Section: Discussionmentioning

confidence: 99%

Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia

Wyss

Ginsbourger

Shou

et al. 2021

Preprint

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Combining the right--potentially invasive and expensive, markers at the appropriate time is critical to obtain reliable yet economically sustainable decisions in the preclinical diagnosis of dementia. We propose a data-driven analytical framework to individualize the selection of prognostic biomarkers that balance accuracy, costs of opportunity due to delaying the decision, and cost of acquisition depending to prescribed cost parameters. We compared sequential and non-sequential decision strategies based on a linear mixed-effects classification model that integrates irregular, multi-variate longitudinal data. The framework was applied to separate participants that progress to Alzheimer's disease from the ones that do not within a time interval of three years. As expected, the highest accuracy was obtained by combining all available data from 20.9 measurements per subject on average that were acquired over 4.8 years on average. The proposed sequential algorithm empirically outperformed alternative methods by having lowest costs for a range of tested cost parameters. With the default cost parameters, the sequential algorithm reached an accuracy of 0.84, specificity of 0.86, and sensitivity of 0.82 (0.89, 0.91, and 0.88 with all available data, respectively) while requiring only 2.9 measurements on average (86 percent less observations than all available data) and a time interval of half a year on average (89 percent shorter than all time points). Our sequential algorithms established the decision based on individualized sequences of measurements with reduced process costs compared to non-sequential classification strategies while maintaining competitive accuracy.

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Section: Discussionmentioning

confidence: 99%

Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia

Wyss

Ginsbourger

Shou

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…It follows from (18) and (19) Substituting (17) and (20) into (16), it follows after some lengthy algebra that EðW $ jY $ Þ ¼…”

Section: Discussionmentioning

confidence: 99%

“…A sequential neutral zone classifier based on misclassification costs has previously been utilized for this application. 17 In that analysis, a classification decision was made at each of the seven time points, covering all 72 h. Neutral zones were implemented at the first six time points and a decision between ''dysfunction'' and ''no dysfunction'' was forced at the last time point. Here, our purpose is to illustrate the construction of a single-stage neutral zone classifier that controls the FNR and FPR.…”

Section: Kidney Dysfunctionmentioning

confidence: 99%

Maximizing the usefulness of statistical classifiers for two populations with illustrative applications

Jeske

Smith

2016

Stat Methods Med Res

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The usefulness of two-class statistical classifiers is limited when one or both of the conditional misclassification rates is unacceptably high. Incorporating a neutral zone region into the classifier provides a mechanism to refer ambiguous cases to follow-up where additional information might be obtained to clarify the classification decision. Through the use of the neutral zone region, the conditional misclassification rates can be controlled and the classifier becomes useful. Three real-life examples, including applications to prostate cancer and kidney dysfunction following heart surgery, are used to illustrate how neutral zone regions can extract utility from disappointing classifiers that might otherwise be abandoned.

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“…Ozkale dan Arican (2015) mengkaji masalah multikolinieritas pada regresi logistik. Zhang, Jeske, dan Wong (2016) Hasil pengamatan variabel random respon (y) mempunyai dua kategori yaitu 0 dan 1, sehingga mengikuti distribusi Bernoulli dengan fungsi probabilitas (Hosmer dkk., 2013).…”

Section: Pendahuluanunclassified

Regresi Logistik untuk Pemodelan Indeks Pembangunan Kesehatan Masyarakat Kabupaten/Kota di Pulau Kalimantan

Fathurahman¹

2018

Preprint

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Regresi logistik merupakan model regresi yang paling sering digunakan untuk pemodelan data kategorik. Pada penelitian ini dilakukan pemodelan regresi logistik dan penerapannya pada Indeks Pembangunan Kesehatan Masyarakat (IPKM) kabupaten/kota di Pulau Kalimantan tahun 2013. Metode Maximum Likelihood Estimation (MLE) digunakan untuk penaksiran parameter. Metode Likelihood Ratio Test (LRT) dan uji Wald digunakan untuk pengujian parameter. Hasil penelitian menunjukkan bahwa penaksir parameter dengan metode MLE berbentuk fungsi yang tidak eksplisit. Sehingga digunakan pendekatan numerik dengan metode Fisher Scoring. Berdasarkan metode LRT dan uji Wald, statistik uji untuk pengujian parameter mendekati distribusi chi-square dan distribusi normal standar. Berdasarkan model regresi logistik terbaik, faktor-faktor yang berpengaruh terhadap IPKM kabupaten/kota di Pulau Kalimantan tahun 2013 adalah Indeks Pembangunan Manusia (IPM), tingkat kepadatan penduduk dan persentase penduduk miskin.

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A sequential logistic regression classifier based on mixed effects with applications to longitudinal data

Cited by 11 publications

References 10 publications

Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia

Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia

Maximizing the usefulness of statistical classifiers for two populations with illustrative applications

Regresi Logistik untuk Pemodelan Indeks Pembangunan Kesehatan Masyarakat Kabupaten/Kota di Pulau Kalimantan

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