Background: Multiple Sclerosis (MS) is an auto immune disease, unpredictable in its symptoms, with uncertain prognosis.The most common phenotype is Relapsing-Remitting (RRMS).Despite remissions, relapses lead to CNS damage, and less CNS function recovery from recurrence of relapses, leading to increasing debilitation.There is no cure, and medicines are used for prevention of relapse, with intervention reserved generally for relief from serious inflammatory symptoms.Methods: This statistical study examines a 7-year span of very detailed medical records of one RRMS patient.Seven clinically observable MS disease responses to distress are identified by mapping the magnitude of distress against duration of symptom sets.Natural divisions were identified in this mapping.Probability distributions, within these natural divisions, were formally constructed.Further investigation is warranted as to how these relate to physiologic, histologic, and biochemical processes involved in MS pathology.Results: This study establishes that stressors exacerbate and expose the presence of the disease and that distress, is the missing consideration in many clinical studies as it is an intermediate outcome between stressors and symptoms.The statistical analysis documents 4 distress characteristics, and 7 Disease Response characteristics.Distress or its absence can predictably induce an MS relapse or remission, respectively.Mathematical and statistical models between distress and relapse are derived that characterize the RRMS disease response.These formulae facilitate managing other patients' symptoms.This study recommends several approaches to modeling symptom set data for the purpose of yielding better, more consistent models.For example, how to utilize the results of survival functions, and EDDS.A Stress-Disease Meta Model is proposed.That, Stressors cause Susceptible patients to exhibit a Stress Response (or distress) that leads to Effectors causing tissue Injury evidenced as Disease Responses.The Disease Processes determine how healing and/or deterioration is evidenced in the disease as Disease Responses.This facilitates the structuring and tracking of triggers, symptoms, other factors and/or comorbidities, to yield more usable data for statistical analysis.Finally, a multi-process disease-wellness approach is proposed that should open further avenues for research.Conclusion: MS is considered an unpredictable disease because (1) MS symptom sets are triggered within 3 days of stress triggers, (2) The random arrival of stress triggers causes the appearance of random symptom sets, and (3) MS is also unpredictable as the underlying disease processes are dynamic processes that keep switching.
Testing effect size homogeneity is an essential part when conducting a meta-analysis. Comparative studies of effect size homogeneity tests in case of binary outcomes are found in the literature, but no test has come out as an absolute winner. A alternative approach would be to carry out multiple effect size homogeneity tests on the same meta-analysis and combine the resulting dependent p-values. In this article we applied the correlated Lancaster method for dependent statistical tests. To investigate the proposed approach’s performance, we applied eight different effect size homogeneity tests on a case study and on simulated datasets, and combined the resulting p-values. The proposed method has similar performance to that of tests based on the score function in the presence of a effect size when the number of studies is small, but outperforms these tests as the number of studies increases. However, the method’s performance is sensitive to the correlation coefficient value assumed between dependent tests, and only performs well when this value is high. More research is needed to investigate the method’s assumptions on correlation in case of effect size homogeneity tests, and to study the method’s performance in meta-analysis of continuous outcomes.
Methodological development and applications of joint models for longitudinal and survival data have mostly coupled a single longitudinal outcome-based mixed-effects model with normal distribution and Cox proportional hazards model.In practice, however, (i) normality of model error in longitudinal sub-models is a routine assumption, but it may be unrealistically violating data features of subject variations.(ii) The data collected are often featured by multivariate longitudinal outcomes which are significantly correlated, ignoring their correlation may lead to biased estimation.Additionally, a parametric specification may be inflexible to capture the complicated longitudinal pattern of biomarkers.(iii) It is of importance to investigate how multivariate longitudinal outcomes are associated with an event time of interest.Multilevel item response theory (MLIRT) models have been increasingly used to analyze the multivariate longitudinal data of mixed types (e.g., continuous and categorical) in clinical studies.In this article, we develop a multivariate joint model that consists of an extended MLIRT model for the mixed types of multivariate longitudinal data and a Cox proportional hazards model, linked through random-effects.The proposed models and method are applied to analyze longitudinalsurvival data arising from a primary biliary cirrhosis study.Simulation studies are conducted to evaluate the performance of the proposed models and method.
The one-sample t test compares a sample to a known average. The standard deviation (SD) is known for the sample but not for the known average. This study compares p-values from one-sample versus twosample t testing where SD is
