Master of Sciences, Case Western Reserve University, 2017, Clinical Research

Background; Multiple sclerosis (MS) is a chronic immune-mediated demyelinating disease of the central nervous system that commonly affects young women of reproductive potential. As such, issues related to family planning are of critical importance. Objective; The objective of this study was to first determine both what are the risk factors of having an unplanned pregnancy and the risk factors of having a relapse while attempting to conceive. Secondly, we look at the rate of post-partum relapses as a function of labor anesthesia choice. Methods; A retrospective observational cohort study was created from a chart review. Multiple stepwise logistic regressions were used to determine the risk factors for a relapse while attempting, unplanned pregnancies, and DMT exposed pregnancies. Cox modeling was used to predict post-partum relapse Results; 150 patients were identified as having MS and concomitant pregnancy. The medical records of these patients were reviewed and 45 patients had both obstetric and MS care within the Cleveland Clinic hospital system. There were a total of 63 pregnancies (20 unplanned and 43 planned). Risk factors for unplanned pregnancy were younger age (p < 0.01), being on DMT (p < 0.001), and being unmarried (p < 0.001). Among those with planned pregnancies, the risk of relapse was dependent only on the time it took to conceive (p < 0.001). Of the total 63 deliveries, there were no significant differences in the number of post-partum relapses or time to relapse among the different anesthesia modality groups (p = 0.71). Conclusion; The rate of unplanned pregnancies is lower than previous though. Intervention at younger unmarried patient may lead to reduce rate of unplanned and DMT exposed pregnancies. MS patients should be referred to reproductive medicine sooner to avoid return of disease activity. Finally, MS should not affect anesthesia used during delivery.

Committee: Daniel Ontaneda (Advisor) Subjects: Neurology

Doctor of Philosophy, The Ohio State University, 2010, Statistics

Multiple sclerosis (MS) is an autoimmune disease in which the body's own immune system attacks the central nervous system. Relapsing remitting MS (RRMS) is an initial stage of the disease where the patient experiences distinct phases of relapse and remittance. Magnetic resonance imaging (MRI) is commonly used to monitor the RRMS disease progression. MRI scans of the brain are taken each month and the total number of new MRI lesions seen during the follow-up period is used as the response variable of interest. The Negative Binomial (NB) and the Poisson-Inverse Gaussian (P-IG) distributions have been shown to fit this over-dispersed data well. Currently, only nonparametric tests are being used to test for the treatment effect in RRMS trials, but the NB and P-IG distributions have been used for simulating the MRI data for the power analyses of these tests and determination of the associated sample sizes. We consider three different trial designs in our study, namely parallel group (PG), baseline vs. treatment (BVT), and parallel group with a baseline correction (PGB). We identify the treatment effect by the parameter γ, with 1-γ representing the proportion reduction in the mean count of new lesions. For these designs we investigate the finite-sample properties of likelihood based parametric tests such as the likelihood ratio test (LRT) and Rao's score test (RST) for γ, and Wald tests (WT) for g(γ) with g(γ) = γ, γ2, √γ, and log(γ). We use the NB and the P-IG models for PG trials and propose optimal likelihood based tests. Recently, tests based on the NB model have been proposed for PG trials; they rely on the chi-square approximation and do not maintain Type I error rates for small samples. We propose simulation based tests that maintain Type I error rates, and for the NB model we also consider the case of unequal dispersion parameters for the two groups. For BVT and PGB trials, assuming a bivariate NB (BNB) model, we investigate various parametric test (open full item for complete abstract)

Committee: Haikady N. Nagaraja PhD (Advisor); Jason C. Hsu PhD (Committee Member); Eloise Kaizar PhD (Committee Member); Thomas J. Santner PhD (Committee Member) Subjects: Biostatistics; Statistics

Doctor of Philosophy, The Ohio State University, 2006, Statistics

Relapsing remitting multiple sclerosis (RRMS) is a chronic and autoimmune disease where the disease states alternate between the relapse and remission. Magnetic resonance imaging (MRI) is widely used to monitor the pathological progression of this disease. The longitudinal T1-weighted Gadolinium-enhancing MRI lesion count sequences provide information on the onset and sojourn time of the lesion enhancement. We construct biologically interpretable queueing models for the longitudinal data of these lesion counts that describe the natural evolution of the lesions. The infinite-server queue with Poisson arrival process and exponential service (M/M/∞) is proposed for this purpose. The rate of the Poisson arrival process can also be allowed to be governed by a two-state hidden Markov chain. We describe the likelihood function for each model based on appropriate assumptions and fit these models to data from 9 RRMS patients. We obtain the maximum likelihood estimators of the parameters of interest arising from these models and study their asymptotic properties through simulation. We discuss the validation of the assumptions for the proposed models and examine the robustness of these estimators. We suggest the application of the models for characterizing the disease progression and testing treatment effect and discuss implication for planning of RRMS clinical trials.

Committee: Haikady Nagaraja (Advisor); Catherine Calder (Other); Kottil Rammohan (Other); Thomas Santner (Other) Subjects: Statistics