Nonparametric tests for transition probabilities in nonhomogeneous Markov processes

Bakoyannis, Giorgos

doi:10.1080/10485252.2019.1705298

Cited by 7 publications

(8 citation statements)

References 21 publications

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“…To statistically evaluate the difference of the CIFs of NRM and GVHD between the 2 biomarker groups, while accounting for the left truncation at each landmark point, we used 2-sample nonparametric linear tests. 18 To perform a multivariable competing risks analysis, the biomarker values for all landmarks points were stacked into a single variable, and a semiparametric proportional cause-specific hazards model was fitted to the data. 17 Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported to demonstrate effect size.…”

Section: Discussionmentioning

confidence: 99%

Assessment of ST2 for risk of death following graft-versus-host disease in pediatric and adult age groups

Rowan

Pike

Cooke

et al. 2020

Blood

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Assessment of prognostic biomarkers of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation (HCT) in the pediatric age group is lacking. To address this need, we conducted a prospective cohort study with 415 patients at 6 centers: 170 were children age 10 years or younger and 245 were patients older than age 10 years (both children and adults were accrued from 2013 to 2018). The following 4 plasma biomarkers were assessed pre-HCT and at days +7, +14, and +21 post-HCT: stimulation-2 (ST2), tumor necrosis factor receptor 1 (TNFR1), regenerating islet–derived protein 3α (REG3α), and interleukin-6 (IL-6). We performed landmark analyses for NRM, dichotomizing the cohort at age 10 years or younger and using each biomarker median as a cutoff for high- and low-risk groups. Post-HCT biomarker analysis showed that ST2 (>26 ng/mL), TNFR1 (>3441 pg/mL), and REG3α (>25 ng/mL) are associated with NRM in children age 10 years or younger (ST2: hazard ratio [HR], 9.13; 95% confidence interval [CI], 2.74-30.38; P = .0003; TNFR1: HR, 4.29; 95% CI, 1.48-12.48; P = .0073; REG3α: HR, 7.28; 95% CI, 2.05-25.93; P = .0022); and in children and adults older than age 10 years (ST2: HR, 2.60; 95% CI, 1.15-5.86; P = .021; TNFR1: HR, 2.09; 95% CI, 0.96-4.58; P = .06; and REG3α: HR, 2.57; 95% CI, 1.19-5.55; P = .016). When pre-HCT biomarkers were included, only ST2 remained significant in both cohorts. After adjustment for significant covariates (race/ethnicity, malignant disease, graft, and graft-versus-host-disease prophylaxis), ST2 remained associated with NRM only in recipients age 10 years or younger (HR, 4.82; 95% CI, 1.89-14.66; P = .0056). Assays of ST2, TNFR1, and REG3α in the first 3 weeks after HCT have prognostic value for NRM in both children and adults. The presence of ST2 before HCT is a prognostic biomarker for NRM in children age 10 years or younger allowing for additional stratification. This trial was registered at www.clinicaltrials.gov as #NCT02194439.

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Section: Discussionmentioning

confidence: 99%

Assessment of ST2 for risk of death following graft-versus-host disease in pediatric and adult age groups

Rowan

Pike

Cooke

et al. 2020

Blood

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“…The file with one quarter of the randomly selected points in the first step and the file with the regularized time series generated in the second step are compared. The agreement between the separate and the new points produced by the regularization algorithm is checked through the Kolmogorov-Smirnov test (K-S Test) (Marsaglia et al 2003;Bakoyannis 2020;Sadhanala et al 2019;del Barrio et al 2020).…”

Section: Regularizing Time Seriesmentioning

confidence: 99%

Periodicity Detection in Agn With the Boosted Tree Method

Soltau

Botti

2021

RMxAA

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We apply a machine learning algorithm called XGBoost to explore the periodicity of two radio sources: PKS 1921-293 (OV 236) and PKS 2200+420 (BL Lac), both radio frequency datasets obtained from University of Michigan Radio Astronomy Observatory (UMRAO), at 4.8 GHz, 8.0 GHz, and 14.5 GHz, between 1969 to 2012. From this methods, we find that the XGBoost provides the opportunity to use a machine learning based methodology on radio datasets and to extract information with strategies quite different from those traditionally used to treat time series, as well as to obtain periodicity through the classification of recurrent events. The results were compared with other methods that examined the same datasets and exhibit a good agreement with them.

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“…Calculation of confidence bands and nonparametric two‐sample tests can be performed using the approaches by Bluhmki et al . (2018) and Bakoyannis (2020), respectively.…”

Section: Introductionmentioning

confidence: 99%

“…With independent observations, nonparametric estimation of the transition probabilities for such multistate processes can be performed using the Aalen-Johansen estimator ( Aalen and Johansen, 1978 ). Calculation of confidence bands and nonparametric two-sample tests can be performed using the approaches by Bluhmki et al (2018) and Bakoyannis (2020) , respectively.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in a study of COVID-19 progression, members of the same family are expected to have correlated outcomes. When the observations exhibit within-cluster dependence, the traditional Greenwood standard error estimators for the transition probabilities, the simultaneous confidence bands by Bluhmki et al (2018) , and the nonparametric tests by Bakoyannis (2020) are not valid.…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric analysis of nonhomogeneous multistate processes with clustered observations

Bakoyannis

2020

Biometrics

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Frequently, clinical trials and observational studies involve complex event history data with multiple events. When the observations are independent, the analysis of such studies can be based on standard methods for multistate models. However, the independence assumption is often violated, such as in multicenter studies, which makes standard methods improper. This work addresses the issue of nonparametric estimation and two‐sample testing for the population‐averaged transition and state occupation probabilities under general multistate models with cluster‐correlated, right‐censored, and/or left‐truncated observations. The proposed methods do not impose assumptions regarding the within‐cluster dependence, allow for informative cluster size, and are applicable to both Markov and non‐Markov processes. Using empirical process theory, the estimators are shown to be uniformly consistent and to converge weakly to tight Gaussian processes. Closed‐form variance estimators are derived, rigorous methodology for the calculation of simultaneous confidence bands is proposed, and the asymptotic properties of the nonparametric tests are established. Furthermore, I provide theoretical arguments for the validity of the nonparametric cluster bootstrap, which can be readily implemented in practice regardless of how complex the underlying multistate model is. Simulation studies show that the performance of the proposed methods is good, and that methods that ignore the within‐cluster dependence can lead to invalid inferences. Finally, the methods are illustrated using data from a multicenter randomized controlled trial.

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Nonparametric tests for transition probabilities in nonhomogeneous Markov processes

Cited by 7 publications

References 21 publications

Assessment of ST2 for risk of death following graft-versus-host disease in pediatric and adult age groups

Assessment of ST2 for risk of death following graft-versus-host disease in pediatric and adult age groups

Periodicity Detection in Agn With the Boosted Tree Method

Nonparametric analysis of nonhomogeneous multistate processes with clustered observations

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