Citations

AbstractRecent research has highlighted the importance of the human microbiome in many human disease and health conditions. Most current microbiome association analyses focus on unrelated samples; such methods are not appropriate for analysis of data collected from more advanced study designs such as longitudinal and pedigree studies, where outcomes can be correlated. Ignoring such correlations can sometimes lead to suboptimal results or even possibly biased conclusions. Thus, new methods to handle correlated outcome data in microbiome association studies are needed. In this paper, we propose the correlated sequence kernel association test (CSKAT) to address such correlations using the linear mixed model. Specifically, random effects are used to account for the outcome correlations and a variance component test is used to examine the microbiome effect. Compared to existing genetic association tests for longitudinal and family samples, we implement a correction procedure to better calibrate the null distribution of the score test statistic to accommodate the small sample size nature of data collected from a typical microbiome study. Comprehensive simulation studies are conducted to demonstrate the validity and efficiency of our method, and we show that CSKAT achieves a higher power than existing methods while correctly controlling the Type I error rate. We also apply our method to a microbiome data set collected from a UK twin study to illustrate its potential usefulness. A free implementation of our method in R software is available at https://github.com/jchen1981/SSKAT.

Purpose SWOG S0016 was a phase III randomized study that compared the safety and efficacy of R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) with CHOP-RIT (CHOP followed by consolidation with iodine-133–tositumomab radioimmunotherapy) for previously untreated patients with follicular lymphoma. Understanding the long-term outcome of patients provides a benchmark for novel treatment regimens for FL. Patients and Methods Between 2001 and 2008, 531 previously untreated patients with FL were randomly assigned to receive either six cycles of R-CHOP or six cycles of CHOP-RIT. Patients with advanced-stage disease (bulky stage II, III, or IV) of any pathologic grade (1, 2, or 3) were eligible. Results After a median follow-up of 10.3 years, 10-year estimates of progression-free and overall survival were 49% and 78% among all patients, respectively. Patients in the CHOP-RIT arm had significantly better 10-year progression-free survival compared with patients in the R-CHOP arm (56% v 42%; P = .01), but 10-year overall survival was not different between the two arms (75% v 81%; P = .13). There was no significant difference between the CHOP-RIT and R-CHOP arms in regard to incidence of second malignancies (15.1% v 16.1%; P = .81) or myelodysplastic syndrome or acute myeloid leukemia (4.9% v 1.8%; P = .058). The estimated 10-year cumulative incidences of death resulting from second malignancies were not different (7.1% v 3.2%; P = .16), but cumulative incidence of death resulting from myelodysplastic syndrome or acute myeloid leukemia was higher in the CHOP-RIT arm compared with the R-CHOP arm (4% v 0.9%; P = .02). Conclusion Given these outstanding outcomes, immunochemotherapy should remain the standard induction approach for patients with high-risk FL until long-term follow-up of alternative approaches demonstrates superiority.

Purpose To compare the risks of serious health outcomes among hematopoietic cell transplantation (HCT) survivors versus a matched population of patients with cancer who did not undergo HCT, where the primary difference may be exposure to HCT. Methods Two-year HCT survivors treated at a comprehensive cancer center from 1992 through 2009 who were Washington State residents (n = 1,792; 52% allogeneic and 90% hematologic malignancies) were frequency matched by demographic characteristics and underlying cancer diagnosis (as applicable) to non-HCT 2-year cancer survivors, using the state cancer registry (n = 5,455) and the general population (n = 16,340) using driver’s license files. Late outcomes for all three cohorts were ascertained from the state hospital discharge and death registries; subsequent cancers were ascertained from the state cancer registry. Results After median follow-up of 7.1 years, HCT survivors experienced significantly greater rates of hospitalization compared with matched non-HCT cancer survivors (280 v 173 episodes per 1,000 person-years, P < .001) and greater all-cause mortality (hazard ratio [HR], 1.1; 95% CI, 1.01 to 1.3). HCT survivors had more hospitalizations or death with infections (10-year cumulative incidence, 31% v 22%; HR, 1.4; 95% CI, 1.3 to 1.6) and respiratory complications (cumulative incidence, 27% v 20%; HR, 1.4; 95% CI, 1.2 to 1.5). Risks of digestive, skin, and musculoskeletal complications also were greater among HCT versus non-HCT cancer survivors. The two groups had similar risks of circulatory complications and second cancers. Both HCT and non-HCT cancer survivors had significantly greater 10-year cumulative incidences of all major organ-system outcomes versus the general population. Conclusion History of HCT was associated with late morbidity and mortality among cancer survivors. In particular, clinicians who care for HCT survivors should be aware of their high rates of late respiratory and infectious complications.

AbstractKernel machine based association tests (KAT) have been increasingly used in testing the association between an outcome and a set of biological measurements due to its power to combine multiple weak signals of complex relationship with the outcome through the specification of a relevant kernel. Human genetic and microbiome association studies are two important applications of KAT. However, the classic KAT framework relies on large sample theory, and conservativeness has been observed for small sample studies, especially for microbiome association studies. The common approach for addressing the small sample problem relies on computationally intensive resampling methods. Here, we derive an exact test for KAT with continuous traits, which resolve the small sample conservatism of KAT without the need for resampling. The exact test has significantly improved power to detect association for microbiome studies. For binary traits, we propose a similar approximate test, and we show that the approximate test is very powerful for a wide range of kernels including common variant‐ and microbiome‐based kernels, and the approximate test controls the type I error well for these kernels. In contrast, the sequence kernel association tests have slightly inflated genomic inflation factors after small sample adjustment. Extensive simulations and application to a real microbiome association study are used to demonstrate the utility of our method.

Background and objectives Diagnosis of multiple sclerosis (MS) requires exclusion of diseases that could better explain the clinical and paraclinical findings. A systematic process for exclusion of alternative diagnoses has not been defined. An International Panel of MS experts developed consensus perspectives on MS differential diagnosis. Methods Using available literature and consensus, we developed guidelines for MS differential diagnosis, focusing on exclusion of potential MS mimics, diagnosis of common initial isolated clinical syndromes, and differentiating between MS and non-MS idiopathic inflammatory demyelinating diseases. Results We present recommendations for 1) clinical and paraclinical red flags suggesting alternative diagnoses to MS; 2) more precise definition of “clinically isolated syndromes” (CIS), often the first presentations of MS or its alternatives; 3) algorithms for diagnosis of three common CISs related to MS in the optic nerves, brainstem, and spinal cord; and 4) a classification scheme and diagnosis criteria for idiopathic inflammatory demyelinating disorders of the central nervous system. Conclusions Differential diagnosis leading to MS or alternatives is complex and a strong evidence base is lacking. Consensus-determined guidelines provide a practical path for diagnosis and will be useful for the non-MS specialist neurologist. Recommendations are made for future research to validate and support these guidelines. Guidance on the differential diagnosis process when MS is under consideration will enhance diagnostic accuracy and precision.