Physical health and function trajectories in adults with cancer: psychosocial predictors of class membership.

PURPOSE: To prospectively examine different trajectories of recovery, across different aspects of physical health and function and to examine trajectory class membership. METHODS: This prospective study enrolled 569 recently diagnosed adult cancer patients (Mage = 58.7) between 2019 and 2022 identified through the Rapid Case Ascertainment resource of The Yale Cancer Center. Patients were diagnosed with breast (63.8%), prostate (25.3%), or colorectal cancer (10.9%) within six-months of baseline assessment. Participants completed comprehensive psychosocial and health survey measures (SF-12) through REDCap at five time points. Growth mixture modeling examined unconditional distinct trajectories for four aspects of physical health and function. We fit logistic regression and multinomial logistic regression models to estimate associations between psychosocial predictors of trajectory class membership for each of the four aspects. RESULTS: We identified distinct trajectories of physical health and function. Over one-third (38.4%) of the sample experienced low and declining scores in their ability to accomplish work/regular daily activities due to physical health. Over half (54.9%) demonstrate moderately stable general health with no improvement over time. A small but significant subset of the sample (3%, 5.7%, 5%) was in the moderate and declining groups with sharp decline in physical function, bodily pain, and general health, after treatment. Different predictors of trajectory class membership were also found. CONCLUSIONS: Our results showed heterogeneity in physical health and function trajectories and different patterns of predictors for each aspect of physical health and function. Findings have the potential to inform screening and intervention efforts to help those who may need additional support.

Incomplete clustering analysis via multiple imputation.

Clustering analysis is a prevalent statistical method which divides populations into several subgroups of similar units. However, most existing clustering methods require complete data. One general method that addresses incomplete data is multiple imputation (MI) which avoids many limitations found in other single imputation-based methods and complete case analyses. Nevertheless, adopting MI framework to clustering analysis can be challenging since each imputed data might consist of a different number of clusters and there is not a unique parameter for clustering analysis. In response to this problem, we have developed MICA: Multiply Imputed Cluster Analysis. MICA is a framework for clustering incomplete data consisting of two clustering stages. We assess the properties of MICA and its superiority over other existing incomplete clustering strategies based on a simulation study under various data structures. In addition, we demonstrate the usage of MICA by applying it to the Youth Risk Behavior Surveillance System (YRBSS) 2019 data.

Symptom Patterning Across the Cancer Care Trajectory for Patients Receiving Chemoradiation for Head and Neck Cancer: A Retrospective Longitudinal Study Using Latent Transition Analysis.

BACKGROUND: Patients with head and neck cancer (HNC) experience a multitude of symptoms because of the tumor and its treatment. OBJECTIVE: To identify the symptom patterns present in cancer treatment and survivorship periods for patients with HNC using latent class analysis. METHODS: A retrospective longitudinal chart review was conducted to examine symptoms reported by patients who received concurrent chemoradiation for HNC in a regional Northeastern United States cancer institute. Latent class analysis was performed to identify the latent classes present across multiple timepoints during treatment and survivorship for the most commonly reported symptoms. RESULTS: In 275 patients with HNC, the latent transition analysis revealed 3 latent classes for both treatment and survivorship periods: (1) mild, (2) moderate, and (3) severe symptoms. Patients were more likely to report a greater number of symptoms in a more severe latent class. During treatment, moderate and severe classes had representation of all most common symptoms: pain, mucositis, taste alterations, xerostomia, dysphagia, and fatigue. Different symptom patterns emerged for survivorship, with prominence of taste alterations and xerostomia across all classes, and all symptoms present in the severe class. The probability of symptom expression varied more in the survivorship period compared with the treatment period. CONCLUSIONS: Patients reported numerous symptoms during active treatment persisting into survivorship. Patients tended to transition to more severe symptomatology as treatment progressed and to more moderate symptomatology as survivorship evolved. IMPLICATIONS FOR PRACTICE: Examining the trend of persistent moderate symptomatology into survivorship is useful to optimize symptom management.

A comparative study on the unified model based multifactor dimensionality reduction methods for identifying gene-gene interactions associated with the survival phenotype.

BACKGROUND: For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely employed to reduce multi-levels of gene-gene interactions into high- or low-risk groups using a binary attribute. For the survival phenotype, the Cox-MDR method has been proposed using a martingale residual of a Cox model since Surv-MDR was first proposed using a log-rank test statistic. Recently, the KM-MDR method was proposed using the Kaplan-Meier median survival time as a classifier. All three methods used the cross-validation procedure to identify single nucleotide polymorphism (SNP) using SNP interactions among all possible SNP pairs. Furthermore, these methods require the permutation test to verify the significance of the selected SNP pairs. However, the unified model-based multifactor dimensionality reduction method (UM-MDR) overcomes this shortcoming of MDR by unifying the significance testing with the MDR algorithm within the framework of the regression model. Neither cross-validation nor permutation testing is required to identify SNP by SNP interactions in the UM-MDR method. The UM-MDR method comprises two steps: in the first step, multi-level genotypes are classified into high- or low-risk groups, and an indicator variable for the high-risk group is defined. In the second step, the significance of the indicator variable of the high-risk group is tested in the regression model included with other adjusting covariates. The Cox-UMMDR method was recently proposed by combining Cox-MDR with UM-MDR to identify gene-gene interactions associated with the survival phenotype. In this study, we propose two simple methods either by combining KM-MDR with UM-MDR, called KM-UMMDR or by modifying Cox-UMMDR by adjusting for the covariate effect in step 1, rather than in step 2, a process called Cox2-UMMDR. The KM-UMMDR method allows the covariate effect to be adjusted for in the regression model of step 2, although KM-MDR cannot adjust for the covariate effect in the classification procedure of step 1. In contrast, Cox2-UMMDR differs from Cox-UMMDR in the sense that the martingale residuals are obtained from a Cox model by adjusting for the covariate effect in step 1 of Cox2-UMMDR whereas Cox-UMMDR adjusts for the covariate effect in the regression model in step 2. We performed simulation studies to compare the power of several methods such as KM-UMMDR, Cox-UMMDR, Cox2-UMMDR, Cox-MDR, and KM-MDR by considering the effect of covariates and the marginal effect of SNPs. We also analyzed a real example of Korean leukemia patient data for illustration and a short discussion is provided. RESULTS: In the simulation study, two different scenarios are considered: the first scenario compares the power of the cases with and without the covariate effect. The second scenario is to compare the power of cases with the main effect of SNPs versus without the main effect of SNPs. From the simulation results, Cox-UMMDR performs the best across all scenarios among KM-UMMDR, Cox2-UMMDR, Cox-MDR and KM-MDR. As expected, both Cox-UMMDR and Cox-MDR perform better than KM-UMMDR and KM-MDR when a covariate effect exists because the former adjusts for the covariate effect but the latter cannot. However, Cox2-UMMDR behaves similarly to KM-UMMDR and KM-MDR even though there is a covariate effect. This implies that the covariate effect would be more efficiently adjusted for in the regression model of the second step rather than under the classification procedure of the first step. When there is a main effect of any SNP, Cox-UMMDR, Cox2-UMMDR and KM-UMMDR perform better than Cox-MDR and KM-MDR if the main effects of SNPs are properly adjusted for in the regression model. From the simulation results of two different scenarios, Cox-UMMDR seems to be the most robust when there is either any covariate effect adjusting for or any SNP that has a main effect on the survival phenotype. In addition, the power of all methods decreased as the censoring fraction increased from 0.1 to 0.3, as heritability increased. The power of all methods seems to be greater under MAF = 0.2 than under MAF = 0.4. For illustration, both KM-UMMDR and Cox2-UMMDR were applied to identify SNP by SNP interactions with the survival phenotype to a real dataset of Korean leukemia patients. CONCLUSION: Both KM-UMMDR and Cox2-UMMDR were easily implemented by combining KM-MDR and Cox-MDR with UM-MDR, respectively, to detect significant gene-gene interactions associated with survival time without cross-validation and permutation testing. The simulation results demonstrate the utility of KM-UMMDR, Cox2-UMMDR and Cox-UMMDR, which outperforms Cox-MDR and KM-MDR when some SNPs with only marginal effects might mask the detection of causal epistasis. In addition, Cox-UMMDR, Cox2-UMMDR and Cox-MDR performed better than KM-UMMDR and KM-MDR when there were potentially confounding covariate effects.

Gene-Gene Interaction Analysis for the Survival Phenotype Based on the Kaplan-Meier Median Estimate.

In this study, we propose a simple and computationally efficient method based on the multifactor dimensional reduction algorithm to identify gene-gene interactions associated with the survival phenotype. The proposed method, referred to as KM-MDR, uses the Kaplan-Meier median survival time as a classifier. The KM-MDR method classifies multilocus genotypes into a binary attribute for high- or low-risk groups using median survival time and replaces balanced accuracy with log-rank test statistics as a score to determine the best model. Through intensive simulation studies, we compared the power of KM-MDR with that of Surv-MDR, Cox-MDR, and AFT-MDR. It was found that KM-MDR has a similar power to that of Surv-MDR, with less computing time, and has comparable power to that of Cox-MDR and AFT-MDR, even when there is a covariate effect. Furthermore, we apply KM-MDR to a real dataset of ovarian cancer patients from The Cancer Genome Atlas (TCGA).