Application of the Misclassification Simulation Extrapolation (Mc-Simex) Procedure to Log-Logistic Accelerated Failure Time (Aft) Models In Survival Analysis

Survival analysis is the study of time to event outcomes. Accelerated Failure Time models (AFT) serve as a useful tool in survival analysis to study the time of occurrence of an event and its relation to the covariates of interest. The accuracy of estimation of parameters in a model depends upon the correct measurement of covariates. Considering that perfect measurement of covariates is highly unlikely, it is imperative that the performance of the existing bias-correction methods be analyzed in AFT models. However, certain areas of bias-correction in AFT models still remain unexplored. One of these unexplored areas, is a situation where the survival times follow a log-logistic distribution. In this dissertation, we evaluate the performance of the Misclassification simulation extrapolation (MC-SIMEX) procedure, a well-known procedure for bias-correction due to misclassification, in AFT models where the survival times follow a standard log-logistic distribution. In addition, a modified version of the MC-SIMEX procedure is also proposed, that provides an advantage in situations where the sensitivity and specificity of classification are unknown. Lastly, the performance of the original MC-SIMEX procedure in lung cancer data provided by the North Central Cancer Treatment Group (NCCTG), is also evaluated

Quasi-likelihood Ratio Tests for Homoscedasticity of Variance in Linear Regression

Two quasi-likelihood ratio tests are proposed for the homoscedasticity assumption in the linear regression models. They require few assumptions than the existing tests. The properties of the tests are investigated through simulation studies. An example is provided to illustrate the usefulness of the new proposed tests

Mobile mammography in New York City: analysis of 32,350 women utilizing a screening mammogram program

Mobile mammography vans (mammovans) may help close the gap to access of breast cancer screening by providing resources to underserved communities. Minimal data exists on the populations served, the ability of mammovans to reach underserved populations, and the outcomes of participants. We sought to determine the demographic characteristics, number of breast cancers diagnosed, and number of women who used the American Italian Cancer Foundation (AICF) Mobile, No-Cost Breast Cancer Screening Program within the five boroughs of New York City. Data were collected by the AICF from 2014 to 2019 on a voluntary basis from participants at each screening location. Women aged 40 to 79 years who had not had a mammogram in the previous 12 months were invited to participate. Each participant underwent a clinical breast exam by a nurse practitioner followed by a screening mammogram. Images were read by a board-certified radiologist contracted by the AICF from Multi Diagnostic Services. There were 32,350 participants in this study. Sixty-three percent reported an annual household income ≤$25,000, and 30% did not have health insurance. More than half of participants identified as either African American (28%) or Hispanic (27%). Additional testing was performed for 5359 women found to have abnormal results on screening. In total, 68 cases of breast cancer were detected. Breast cancer disparities are multifactorial, with the greatest factor being limited access to care. Mobile, no-cost mammogram screening programs show great promise in helping to close the gap to screening access

A Systematic Review of Diarrheal Disease: Its Differential Burden between Genders and the Role of Women in the Abatement of This Epidemic?

Even though research on diarrheal diseases has been done in the past, some aspects have remained unexplored. One of these aspects is the differential disease burden and vulnerability to disease between males and females in addition to the unique causal/behavioral pathways through which each gender can get infected. We try to shed light on these important issues by performing a systematic review of relevant articles chosen from the literature. We searched PubMed for peer-reviewed articles, and included grey literature from the World Health Organization, Water and Sanitation for the Urban Poor and Water Supply and Sanitation Collaborative Council. All articles that deal with the public health relevance of diarrheal diseases, focus on access to clean water and care taker role in access to clean water, role of gender in sanitation, Water Sanitation and Hygiene (WASH) interventions, helminth infections, discrepancies in health care with regard to diarrheal diseases in rural and urban environments and differential gender burden with regard to infectious diarrheal diseases are included in this study. Articles which do not address diarrheal diseases, topics that are not relevant to diarrheal diseases, do not address the role of sanitation, access to clean water or WASH interventions in diarrheal diseases or helminth infections, articles that are not epidemiologically linked or articles that deal with rare pathogens or diseases, pathogens that are mainly prevalent in the immuno-compromised population, therapeutic regimens or diagnostic techniques, molecular genetics, drug resistance or seasonal variations were excluded from the study. From our systematic review, we concluded that the burden of diarrheal disease falls more on females qualitatively than males. Women empowerment in making household and community level decisions with regard to sanitation may be of greater benefit to the well-being of society in developing countries. This will require strong government support and sustainable policies at the community and state levels. Some limitations of our study are: The study participants in most of the studies belonged to either the adolescent or the preadolescent age group, which could have resulted in age bias. Secondly, because of the huge amount of articles that were retrieved, there is a small but very unlikely chance that any relevant articles might have been missed. Lastly, we provide qualitative evidence of differential burden of diarrheal disease between genders. A quantitative study will help consolidate our current findings

A Systematic Review of Sex-based Differences in Diarrheal Disease and Helminthic Infections

Background/Objective: Qualitative evidence suggests that inadequate water, sanitation, and hygiene (a risk factor for diarrheal and helminthic pathogens) affect women disproportionately. The main objective of our systematic review is to quantify the burden of helminthic and diarrheal pathogens between sexes. Methods: We systematically searched PubMed in June 2014 and searched the World Health Organization website. Articles dealing with the public health relevance of helminthic and diarrheal diseases, focusing on access to clean water and the primary caretakers role in access to clean water, and highlighting the role of gender in water, hygiene and sanitation were included. Findings: In studies of individuals aged 5 years and above, cholera showed significantly lower prevalence in males (OR 0.56; 95% CI (0.34, 0.94)), while S. mansoni, hookworm and all forms of infectious diarrhea showed a significantly higher prevalence in males (OR 1.50; 95% CI (1.22, 1.84), 1.66; 95% CI (1.19, 2.31), 1.26; 95% CI (1.09, 1.46) respectively). When studies included participants of all ages, only S. mansoni showed a significant association of prevalence with gender (OR 1.55; 95% CI (1.41, 1.70)). Odds ratios of prevalence for Ascaris and Trichiuris showed significant effect modification with the location of study (continent). Significance: Effect modification seen in the case of Ascaris and Trichiuris may be reflective of the difference in social norms and occupational cultures between continents and thereby suggests that policy level changes at the regional level may be effective in ameliorating gender related disparities in prevalence of helminths and infectious diarrheal diseases

Simulation Extrapolation Method for Measurement Error: A Review

Measurement error is pervasive in statistics due to the non-availability of authentic data. The reasons for measurement error mainly relate to cost, convenience, and human error. Measurement error can result in non-negligible bias due to attenuated estimates, reduced power of statistical tests, and lower coverage probabilities of the coefficient estimators in a regression model. Several methods have been proposed to correct for measurement error, all of which can be grouped into two broad categories based on the underlying model—functional and structural. Functional models provide flexibility and robustness to estimators by placing minimal or no assumptions on the distribution of the mismeasured covariate or by treating them as a fixed entity, as opposed to a structural model which treats the underlying mismeasured covariates as random with a specified structure. The simulation extrapolation method is one method that is used for the partial correction of measurement error in both structural and functional models. Reviews of measurement error correction techniques are available in the literature. However, none of the previously conducted reviews has exclusively focused on simulation extrapolation and its application in continuous measurement error models, despite its widespread use and ease of application. We attempt to close this gap in the literature by highlighting its development over the past two and a half decades

Misclassification Simulation Extrapolation Procedure for Log-Logistic Survival Data

Presentation given by Georgia Southern faculty members Lili Yu, Hani Samawi, and Rochani D. Haresh with student Varadan Sevilimedu at the Spring meeting of the Eastern North American Region (ENAR) of the International Biometric Societ

Misclassification Simulation Extrapolation Method for a Weibull Accelerated Failure Time Model

The problem of misclassification in covariates is ubiquitous in survival data and often leads to biased estimates. The misclassification simulation extrapolation method is a popular method to correct this bias. However, its impact on Weibull accelerated failure time models has not been studied. In this paper, we study the bias caused by misclassification in one or more binary covariates in Weibull accelerated failure time models and explore the use of the misclassification simulation extrapolation in correcting for this bias, along with its asymptotic properties. Simulation studies are carried out to investigate the numerical properties of the resulting estimator for finite samples. The proposed method is then applied to colon cancer data obtained from the cancer registry at Memorial Sloan Kettering Cancer Center

Application of the Misclassification Simulation Extrapolation Procedure to Log-Logistic Accelerated Failure Time Models in Survival Analysis

Presentation given at ICSA symposium