Estimation of Sample Size and Power For Quantile Regression

Quantile regression (QR) was first introduced by Roger Koenker and Gilbert Bassett in 1978. It is robust to outliers which affect least squares estimator on a large scale in linear regression. Instead of modeling mean of the response, QR provides an alternative way to model the relationship between quantiles of the response and covariates. Therefore, QR can be widely used to solve problems in econometrics, environmental sciences and health sciences. Sample size is an important factor in the planning stage of experimental design and observational studies. In ordinary linear regression, sample size may be determined based on either precision analysis or power analysis with closed form formulas. There are also methods that calculate sample size based on precision analysis for QR like C.Jennen-Steinmetz and S.Wellek (2005). A method to estimate sample size for QR based on power analysis was proposed by Shao and Wang (2009). In this paper, a new method is proposed to calculate sample size based on power analysis under hypothesis test of covariate effects. Even though error distribution assumption is not necessary for QR analysis itself, researchers have to make assumptions of error distribution and covariate structure in the planning stage of a study to obtain a reasonable estimate of sample size. In this project, both parametric and nonparametric methods are provided to estimate error distribution. Since the method proposed can be implemented in R, user is able to choose either parametric distribution or nonparametric kernel density estimation for error distribution. User also needs to specify the covariate structure and effect size to carry out sample size and power calculation. The performance of the method proposed is further evaluated using numerical simulation. The results suggest that the sample sizes obtained from our method provide empirical powers that are closed to the nominal power level, for example, 80%.

Randomized Trial for a Mobile Health Approach to Increase Uptake of Cervical Cancer Screening in Tanzania

Background: Largely due to low availability and uptake of screening in low- and middle-income countries, cervical cancer is the second ranked cancer among women in these countries. This is a tragedy because cervical cancer is one of the most preventable carcinomas. This thesis will investigate behaviour change methods, which capitalize on the recent exponential increase in ownership of mobile phones in Tanzania, to increase uptake of cervical cancer screening (CCS) in the Kilimanjaro region of Tanzania. Objectives: 1) To evaluate the effectiveness of behaviour change messages delivered via short message service (SMS) on the uptake of CCS in the Kilimanjaro region; 2) to evaluate the effectiveness of a transportation eVoucher on the uptake of CCS in the Kilimanjaro region; 3) to explore characteristics associated with CCS uptake in the Kilimanjaro region; and 4) to determine the attitudes towards and perceived benefit of behaviour change SMS messages and eVouchers intended to increase uptake of CCS. Methods: In the Kilimanjaro Region, 853 women participated in a randomized controlled trial. Baseline data was collected through self-report through systematic stratified random sampling. Participants were randomized to one of three groups: a control group, a group receiving behaviour change messages delivered via SMS, or a group receiving a travel eVoucher and identical SMS as the SMS group. A fieldworker recorded participants attending screening at the CCS clinics and administered a post-screening survey. The follow-up period was two months from the time of the participant’s enrolment. Logistic regression (both for the combined and stratified data sets) was used to determine associations between the behaviour change interventions, baseline characteristics and cervical cancer screening uptake. Results: All participants receiving SMS messages (SMS or eVoucher group) were more likely to attend cervical cancer screening in comparison with the control group. 83% of participants who attended screening shared the information contained in the messages with others. Conclusions: Behaviour change messages delivered via SMS and transportation eVouchers have the potential to increase uptake of cervical cancer screening in the Kilimanjaro region of Tanzania. Harnessing this potential will require implementing these interventions alongside other methods to achieve maximum impact.

Estimate of the (R)USLE rainfall erosivity factor from monthly precipitation data in mainland Spain

The need for continuous recording rain gauges makes it difficult to determine the rainfall erosivity factor (R-factor) of the (R)USLE model in areas without good temporal data coverage. In mainland Spain, the Nature Conservation Institute (ICONA) determined the R-factor at few selected pluviographs, so simple estimates of the R-factor are definitely of great interest. The objectives of this study were: (1) to identify a readily available estimate of the R-factor for mainland Spain; (2) to discuss the applicability of a single (global) estimate based on analysis of regional results; (3) to evaluate the effect of record length on estimate precision and accuracy; and (4) to validate an available regression model developed by ICONA. Four estimators based on monthly precipitation were computed at 74 rainfall stations throughout mainland Spain. The regression analysis conducted at a global level clearly showed that modified Fournier index (MFI) ranked first among all assessed indexes. Applicability of this preliminary global model across mainland Spain was evaluated by analyzing regression results obtained at a regional level. It was found that three contiguous regions of eastern Spain (Catalonia, Valencian Community and Murcia) could have a different rainfall erosivity pattern, so a new regression analysis was conducted by dividing mainland Spain into two areas: Eastern Spain and plateau-lowland area. A comparative analysis concluded that the bi-areal regression model based on MFI for a 10-year record length provided a simple, precise and accurate estimate of the R-factor in mainland Spain. Finally, validation of the regression model proposed by ICONA showed that R-ICONA index overpredicted the R-factor by approximately 19%.

Characterising granuloma regression and liver recovery in a murine model of schistosomiasis japonica

For hepatic schistosomiasis the egg-induced granulomatous response and the development of extensive fibrosis are the main pathologies. We used a Schistosoma japonicum-infected mouse model to characterise the multi-cellular pathways associated with the recovery from hepatic fibrosis following clearance of the infection with the anti-schistosomal drug, praziquantel. In the recovering liver splenomegaly, granuloma density and liver fibrosis were all reduced. Inflammatory cell infiltration into the liver was evident, and the numbers of neutrophils, eosinophils and macrophages were significantly decreased. Transcriptomic analysis revealed the up-regulation of fatty acid metabolism genes and the identification of Peroxisome proliferator activated receptor alpha as the upstream regulator of liver recovery. The aryl hydrocarbon receptor signalling pathway which regulates xenobiotic metabolism was also differentially up-regulated. These findings provide a better understanding of the mechanisms associated with the regression of hepatic schistosomiasis.

Evaluating statistical and machine learning methods to predict risk of in-hospital child mortality in Uganda

Thesis (Master's)--University of Washington, 2016-08

On the Use of Regression and Verbal Protocol Analysis in Modeling Analysts’ Behavior in an Unstructured Task Environment: A Methodological Note

This paper discusses areas for future research opportunities by addressing accounting issues faced by management accountants practicing in hospitality organizations. Specifically, the article focuses on the use of the uniform system of accounts by operating properties, the usefulness of allocating support costs to operated departments, extending our understanding of operating costs and performance measurement systems and the certification of practicing accountants.

Data-Adaptive Modeling using Convex Regression

Thesis (Ph.D.)--University of Washington, 2016-08

Loan quality, ownership and efficiency of Indian banks : A bootstrap truncated regression approach

Registration-by-regression of coronary CTA and X-ray angiography

We evaluate the integration of 3D preoperative computed tomography angiography of the coronary arteries with intraoperative 2D X-ray angiographies by a recently proposed novel registration-by-regression method. The method relates image features of 2D projection images to the transformation parameters of the 3D image. We compared different sets of features and studied the influence of preprocessing the training set. For the registration evaluation, a gold standard was developed from eight X-ray angiography sequences from six different patients. The alignment quality was measured using the 3D mean target registration error (mTRE). The registration-by-regression method achieved moderate accuracy (median mTRE of 15 mm) on real images. It does therefore not provide yet a complete solution to the 3D–2D registration problem but it could be used as an initialisation method to eliminate the need for manual initialisation.

Flexible regression models for functional neuroimaging

Current practice for analysing functional neuroimaging data is to average the brain signals recorded at multiple sensors or channels on the scalp over time across hundreds of trials or replicates to eliminate noise and enhance the underlying signal of interest. These studies recording brain signals non-invasively using functional neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) generate complex, high dimensional and noisy data for many subjects at a number of replicates. Single replicate (or single trial) analysis of neuroimaging data have gained focus as they are advantageous to study the features of the signals at each replicate without averaging out important features in the data that the current methods employ. The research here is conducted to systematically develop flexible regression mixed models for single trial analysis of specific brain activities using examples from EEG and MEG to illustrate the models. This thesis follows three specific themes: i) artefact correction to estimate the `brain' signal which is of interest, ii) characterisation of the signals to reduce their dimensions, and iii) model fitting for single trials after accounting for variations between subjects and within subjects (between replicates). The models are developed to establish evidence of two specific neurological phenomena - entrainment of brain signals to an $\alpha$ band of frequencies (8-12Hz) and dipolar brain activation in the same $\alpha$ frequency band in an EEG experiment and a MEG study, respectively.

Spontaneous Cirrhosis Regression in an IFN-beta-induced AIH-like Syndrome Following Drug Withdrawal: Art of Facts or Artifacts?

Autoimmune hepatitis (AIH) is a disease of unknown aetiology with drug-induced AIH being the most complex and not fully understood type. We present the case of a 57-year-old female patient with acute icteric hepatitis after interferon-beta-1b (IFNβ-1b) administration for multiple sclerosis (MS). Based on liver autoimmune serology, histology and appropriate exclusion of other liver diseases, a diagnosis of AIH-related cirrhosis was established. Following discontinuation of IFNβ-1b, a complete resolution of biochemical activity indices was observed and the patient remained untreated on her own decision. However, 3 years later, after a course of intravenous methylprednisolone for MS, a new acute transaminase flare was recorded which subsided again spontaneously after 3 weeks. Liver biopsy and elastography showed significant fibrosis regression (F2 fibrosis). To our knowledge, this is the first report showing spontaneous cirrhosis regression in an IFNβ-1b-induced AIH-like syndrome following drug withdrawal, suggesting that cirrhosis might be reversible if the offending fibrogenic stimulus is withdrawn.

Explaining dinophysis cf. acuminata abundance in Antifer (Normandy, France) using dynamic linear regression

Classical regression analysis can be used to model time series. However, the assumption that model parameters are constant over time is not necessarily adapted to the data. In phytoplankton ecology, the relevance of time-varying parameter values has been shown using a dynamic linear regression model (DLRM). DLRMs, belonging to the class of Bayesian dynamic models, assume the existence of a non-observable time series of model parameters, which are estimated on-line, i.e. after each observation. The aim of this paper was to show how DLRM results could be used to explain variation of a time series of phytoplankton abundance. We applied DLRM to daily concentrations of Dinophysis cf. acuminata, determined in Antifer harbour (French coast of the English Channel), along with physical and chemical covariates (e.g. wind velocity, nutrient concentrations). A single model was built using 1989 and 1990 data, and then applied separately to each year. Equivalent static regression models were investigated for the purpose of comparison. Results showed that most of the Dinophysis cf. acuminata concentration variability was explained by the configuration of the sampling site, the wind regime and tide residual flow. Moreover, the relationships of these factors with the concentration of the microalga varied with time, a fact that could not be detected with static regression. Application of dynamic models to phytoplankton time series, especially in a monitoring context, is discussed.