Simple change in logistic procedure improves response rate to QOL assessment: a report from the Japan Children's Cancer Group.
Ontology highlight
ABSTRACT:
SUBMITTER: Sato I
PROVIDER: S-EPMC7300165 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Simple change in logistic procedure improves response rate to QOL assessment: a report from the Japan Children's Cancer Group.
Sato Iori I Soejima Takafumi T Ishida Yasushi Y Maeda Miho M Koh Katsuyoshi K Kamibeppu Kiyoko K
Journal of patient-reported outcomes 20200617 1
<h4>Background</h4>Reducing non-completion of quality-of-life assessment in clinical trials is an important challenge in obtaining accurate data and unbiased interpretation of patients' quality-of-life for each regimen. We evaluated the effect of changing our questionnaire distribution procedure in a multicenter phase II/III trial on the response rate to a quality-of-life questionnaire.<h4>Methods</h4>In the trial, we distributed 1767 questionnaires and 1045 were returned. We adopted a regressio ...[more]
Similar Datasets
Project description:This study explores minority group status in relation to change in Quality of Life (QOL) among three population groups in Israel-Veteran Jews, Arab-Israelis and immigrants from the Former Soviet Union (FSU)-controlling for a set of known predictors. The study uses panel data from two waves (2009/10 and 2013) of the Israeli component of the Survey of Health, Ageing and Retirement in Europe, (N=1,590). A set of Ordinary Least Squares regressions is used to predict positive QOL change over the two waves. Interaction terms in a number of selected areas are considered. The results show that minority group status (Arab-Israelis and FSU immigrants) is negatively related to positive QOL change, compared to the majority group (Veteran Jews). Moreover, being employed was found to improve QOL for older FSU immigrants, underscoring the realm of work in the well-being of this population group. In comparison, it was exchange with family members that had a positive effect on QOL change among the Arab-Israelis, emphasizing the importance of that particular aspect of their lives in older age. In sum, the results highlight the risk of minority group status to well-being in late life and confirms the observation that positive QOL change correlates with characteristically different factors among different population groups.
| S-EPMC5313051 | biostudies-literature
Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration.
2009-06-25 | GSE11877 | GEO
Project description:In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) $p$-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. We propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical-variational Bayes framework. Simulations and applications to three cancer genomics studies and one Alzheimer metabolomics study show that, if the partitioning of the features is informative, classification performance, and feature selection are indeed enhanced.
| S-EPMC8596493 | biostudies-literature
Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration. Unsupervised clustering and supervised risk classification analyses of 207 diagnostic samples and associated clinical covariate data. See the Summary for greater details. The data were analyzed using Microarray Suite version 5.0 (MAS 5.0) in the Affymetrix Gene Chip Operating Software Version 1.4. Probe masking was used (see 9906_TT207_Affymetrix_probe_mask.msk, linked below as a supplementary file). Otherwise all Affymetrix default parameter settings were used. Global scaling as the normalization method, with the default target intensity of 500, was used.
2009-06-25 | E-GEOD-11877 | biostudies-arrayexpress
Project description:ObjectiveWomen with postpartum depressive symptoms (PDS) are at higher risk of postpartum depression (PPD) and require further assessment. Emerging evidence indicates a relationship between the total cholesterol (TC) level of blood and PPD but the results are inconsistent. In this study, we investigated the possible association of change in serum TC levels during pregnancy with the risk of PDS in a Japanese population.MethodsWe analyzed complete data on questionnaire responses and serum lipid profiles of pregnant women from 12 datasets obtained from the Japan Environment and Children's Study (n = 61,585 to n = 72,406; 103,063 pregnancies in total). TC was measured at 3 time points-during early pregnancy, during mid-late pregnancy, and after delivery-and we calculated changes in TC in 3 ways: by subtracting early pregnancy from mid-late pregnancy, subtracting mid-late pregnancy from delivery, and subtracting early pregnancy from delivery. These 6 factors were used as predictors to estimate the risk of PDS at 1 and 6 months after delivery.ResultsIn multivariable logistic regression analysis, point serum cholesterol level did not predict PDS at any time point except for during mid-late pregnancy in the second quintile, indicating an increased risk at 6 months. As for change in TC levels, subtracting mid-late pregnancy from delivery and early pregnancy from delivery showed significant risk reduction for PDS at 1 month. A significant risk reduction was also found for subtracting early pregnancy from mid-late pregnancy and early pregnancy from delivery at 6 months.ConclusionsIncreased cholesterol level during pregnancy is associated with reduced risk of PDS.
| S-EPMC9303962 | biostudies-literature
Project description:Neuroblastomas (NBs) exhibit broad and divergent clinical behaviors and tumor risk classification at diagnosis is crucial for the selection of an appropriate therapeutic strategy for each patient. The present study aimed to validate the clinical relevance of International Neuroblastoma Risk Group (INRG) prognostic and genomic markers in a Japanese NB cohort using a retrospective analysis. Follow-up data based on 30 common INRG queries in 605 NB cases diagnosed in Japan between 1990 and 2014 were collected and the genome signature of each tumor sample was integrated. As previously indicated, age, tumor stage, MYCN, DNA ploidy, the adrenals as the primary tumor site, serum ferritin and lactate dehydrogenase (LDH) levels, segmental chromosome aberrations, and the number of chromosome breakpoints (BP) correlated with lower survival rates, while the thorax as the primary tumor site and numerical chromosome aberrations correlated with a favorable prognosis. In the patient group with stage 4, MYCN non-amplified tumors (n = 225), one of the challenging subsets for risk stratification, age ≥ 18 months, LDH ≥ 1400 U/L, and BP ≥ 7 correlated with lower overall and event-free survival rates (p < 0.05). The genome subgroup GG-P2s (partial chromosome gain/loss type with 1p/11q losses and 17q gain, n = 30) was strongly associated with a lower overall survival rate (5-year survival rate: 34%, p < 0.05). Therefore, the combination of the tumor genomic pattern (GG-P2s and BP ≥ 7) with age at diagnosis and LDH will be a promising predictor for MYCN-non-amplified high-risk NBs in patient subsets, in accordance with previous findings from the INRG project.
| S-EPMC8774029 | biostudies-literature
Project description:We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints.
| S-EPMC5099121 | biostudies-other
Project description:A threshold effect takes place in situations where the relationship between an outcome variable and a predictor variable changes as the predictor value crosses a certain threshold/change point. Threshold effects are often plausible in a complex biological system, especially in defining immune responses that are protective against infections such as HIV-1, which motivates the current work. We study two hypothesis testing problems in change point models. We first compare three different approaches to obtaining a p-value for the maximum of scores test in a logistic regression model with change point variable as a main effect. Next, we study the testing problem in a logistic regression model with the change point variable both as a main effect and as part of an interaction term. We propose a test based on the maximum of likelihood ratios test statistic and obtain its reference distribution through a Monte Carlo method. We also propose a maximum of weighted scores test that can be more powerful than the maximum of likelihood ratios test when we know the direction of the interaction effect. In simulation studies, we show that the proposed tests have a correct type I error and higher power than several existing methods. We illustrate the application of change point model-based testing methods in a recent study of immune responses that are associated with the risk of mother to child transmission of HIV-1.
| S-EPMC4390452 | biostudies-literature
Project description:Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.
| S-EPMC5047523 | biostudies-other
Project description:The augmented BFM regimen improves outcome for children with NCI high acute lymphoblastic leukemia (ALL). Patient age, sex, and presenting white blood cell count (WBC) can be used to identify a subset of approximately 12% of children with B-precursor ALL that had a 5-year continuous complete remission (CCR) rate of only about 50% on earlier Pediatric Oncology Group (POG) trials.Children's Oncology Group trial P9906 evaluated a modified augmented BFM regimen in 267 patients with particularly high risk B-precursor ALL. Minimal residual disease (MRD) was assessed in blood at day 8 and in marrow at day 29 of induction and correlated with outcome.The 5-year CCR probability for patients in P9906 was significantly better than that observed for similar patients on POG trials 8602/9006 (62.2 ± 3.7% vs. 50.6 ± 2.4%; P = 0.0007) but similar to POG 9406 (63.5 ± 2.4%; P = 0.81). Interim analysis showed poor central nervous system (CNS) control, especially in patients with initial WBC ? 100,000/microliter. Day 29 marrow MRD positive (? 0.01%) vs. negative patients had 5 year CCR rates of 37.1 ± 7.4% vs. 72.6 ± 4.3%; day 8 blood MRD positive vs. negative patients had 5 year CCR rates of 57.1 ± 4.6% vs.83.6 ± 6.3%. End induction marrow MRD predicted marrow but not CNS relapse. In multivariate analysis, day 29 MRD > 0.01%, initial WBC ? 100,000/µl, male gender, and day 8 blood MRD > 0.01% were significant prognostic factors.Augmented BFM therapy improved outcome for children with higher risk ALL. Day 8 blood and day 29 marrow MRD were strong prognostic factors in these patients.
| S-EPMC3136564 | biostudies-literature