Imputation strategies when a continuous outcome is to be dichotomized for responder analysis: a simulation study

Background: In many clinical trials continuous outcomes are dichotomized to compare proportions of patients who respond. A common and recommended approach to handling missing data in responder analysis is to impute as non-responders, despite known biases. Multiple imputation is another natural choice but when a continuous outcome is ultimately dichotomized, the specifications of the imputation model come into question. Practitioners can either impute the missing outcome before dichotomizing or dichotomize then impute. In this study we compared multiple imputation of the continuous and dichotomous forms of the outcome, and imputing responder status as non-response in responder analysis.MethodsWe simulated four response profiles representing a two-arm randomized controlled trial with a continuous outcome at four time points. We omitted data using six missing at random mechanisms, and imputed missing observations three ways: 1) replacing as non-responder; 2) multiply imputing before dichotomizing; and 3) multiply imputing the dichotomized response. Imputation models included the continuous response at all timepoints, and additional auxiliary variables for some scenarios. We assessed bias, power, coverage of the 95% confidence interval, and type 1 error. Finally, we applied these methods to a longitudinal trial for patients with major depressive disorder. Results: Both forms of multiple imputation performed better than non-response imputation in terms of bias and type 1 error. When approximately 30% of responses were missing, bias was less than 7.3% for multiple imputation scenarios but when 50% of responses were missing, imputing before dichotomizing generally had lower bias compared to dichotomizing before imputing. Non-response imputation resulted in biased estimates, both underestimates and overestimates. In the example trial data, non-response imputation estimated a smaller difference in proportions than multiply imputed approaches. Conclusions: With moderate amounts of missing data, multiply imputing the continuous outcome variable prior to dichotomizing performed similar to multiply imputing the binary responder status. With higher rates of missingness, multiply imputing the continuous variable was less biased and had well-controlled coverage probabilities of the 95% confidence interval compared to imputing the dichotomous response. In general, multiple imputation using the longitudinally measured continuous outcome in the imputation model performed better than imputing missing observations as non-responders.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A Stochastic Version of the EM Algorithm to Analyze Multivariate Skew-Normal Data with Missing Responses

In this paper an algorithm called SEM, which is a stochastic version of the EM algorithm, is used to analyze multivariate skew-normal data with intermittent missing values. Also, a multivariate selection model framework for modeling of both missing and response mechanisms is formulated. By the SEM algorithm missing values of responses are inputed by the conditional distribution of missing values given observed data and then the log-likelihood of the pseudocomplete data is maximized. The algorithm is iterated until convergence of parameter estimates. Results of an application are also reported where a Bootstrap approach is used to compute the standard error of the parameter estimates

Analysis of Layered Social Networks

Prevention of near-term terrorist attacks requires an understanding of current terrorist organizations to include their composition, the actors involved, and how they operate to achieve their objectives. To aid this understanding, operations research, sociological, and behavioral theory relevant to the study of social networks are applied, thereby providing theoretical foundations for new methodologies to analyze non-cooperative organizations, defined as those trying to hide their structure or are unwilling to provide information regarding their operations. Techniques applying information regarding multiple dimensions of interpersonal relationships, inferring from them the strengths of interpersonal ties, are explored. A layered network construct is offered that provides new analytic opportunities and insights generally unaccounted for in traditional social network analyses. These provide decision makers improved courses of action designed to impute influence upon an adversarial network, thereby achieving a desired influence, perception, or outcome to one or more actors within the target network. This knowledge may also be used to identify key individuals, relationships, and organizational practices. Subsequently, such analysis may lead to the identification of exploitable weaknesses to either eliminate the network as a whole, cause it to become operationally ineffective, or influence it to directly or indirectly support National Security Strategy

Biomarker-Based Characterization of Chemical Exposures and Physiological Responses

The chemisome is the chemical components of the exposome, defined as the totality of all exposures and their impact on health. Most current approaches, however, are limited in addressing this “totality” by only studying one chemical or one chemical family at a time in one exposed population. In addition, studying the links between chemical exposures and health is challenging due to an incomplete understanding of how physiological responses are associated with adverse health outcomes. This challenge is further complicated due to how chemical exposures change with demographics such as age, sex, race, and occupation. Thus, this dissertation aims to address these challenges by applying an unbiased approach to datasets of chemical biomarker levels and physiological measurements to systematically identify susceptible populations using the National Health and Nutrition Examination Survey. In the first project, I use quadratic regression models to characterize non-linear, age-based trends of chemical exposure in a sample comprised of 74,942 participants. I screen across 141 chemicals to identify those of higher concentrations in children relative to the older population. Children exhibit higher exposures to chemicals in consumer products such as phthalates, brominated flame retardants, lead, and tungsten. In contrast, restricted and highly persistent chemicals such as polychlorinated biphenyls and dioxins are higher in the older population. In the second project, I apply generalized linear models to evaluate exposure disparities by race/ethnicity for 143 chemicals in a representative sample of 38,080 US women. Compared to non-Hispanic White women, significant disparities are observed for non-Hispanic Black, Mexican American, Other Hispanic, and Other Race/Multi-Racial women. These women have higher levels of pesticides, including 2,5-dichlorophenol and 2,4-dichlorophenol, compounds in personal care products, including parabens and mono-ethyl phthalate, and heavy metals, such as mercury and arsenic. These findings are being coupled with toxicological data to prioritize chemicals to evaluate their role in health disparities. In the third project, I develop a framework using hierarchical clustering to characterize occupational exposures and physiological responses among 26,186 blue- and white-collar workers across 20 employment sectors for 108 chemicals and 27 physiological indicators. Blue-collar workers have higher levels of toxicants such as lead, cadmium, volatile organic chemicals, and polycyclic aromatic hydrocarbons compared to white-collar workers. Moreover, blue-collar workers exhibit higher levels of alkaline phosphatase (indicative of liver disease) and C-reactive proteins (indicative of inflammation). Together, these results suggest that blue-collar workers are exposed to higher levels of toxicants, which may induce physiological dysfunction. In the final project, I implement 10-fold cross-validated regression models to characterize the linear and non-linear associations between all-cause mortality and 27 physiological indicators to identify directionalities indicative of increased mortality risk in a sample of 45,032 participants. Twenty-four out of 27 indicators show non-linear associations, while height, triglycerides, and 60-second pulse show linear associations. Cholesterol-related indicators and glomerular filtration rate unexpectedly show parabolic associations, implying that higher mortality risk is associated with measurements in either extreme of the distribution instead of in one extreme. These findings highlight a need to study associations between these indicators and other health endpoints to gain insights into the physiological profiles associated with adverse health outcomes. Together, this thesis contributes to a better understanding of how chemical exposures can impact human health across multiple subpopulations. It also enables further exploration of how chemical exposures can perturb physiologic function conducive to increasing the risk for adverse health outcomes.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162947/1/nguyenvy_1.pd

No more hide and seek:strategies to optimize diagnosis and endoscopic treatment of complex colorectal neoplasms

Large non-pedunculated colorectal polyps and the occurrence of post-colonoscopy colorectal cancer are the subjects of the thesis “No more hide and seek”. The thesis shows that non-pedunculated colorectal cancers consist of a morphologically heterogeneous group (a group with high variety in tissue characteristics), with a different risk of cancer cells at diagnosis for each subtype. Accurate prior assessment of this risk could prevent suboptimal treatment. Furthermore, this thesis shows that patients with large flat polyps develop more polyps in the future than patients with other polyp types. Flat polyps are harder to detect than other polyps. It is suggested that they can be more easily missed during colonoscopy with the risk of malignant transformation afterwards, which should be prevented by colonoscopy. This could result in the occurrence of so-called post-colonoscopy colorectal cancer. Faster growth by different mutations was hypothesized as another factor in post-colonoscopy colorectal cancer occurrence. This thesis examined the genetic profile of post-colonoscopy colorectal cancers in comparison with common colorectal cancer. The results showed no unique mutations in post-colonoscopy colorectal cancers, but they did show more often the features as seen in a more subtle and flat category of flat bowel polyps. Improvements in detection of these polyps remain important in post-colonoscopy colorectal cancer prevention

EXPOSURES TO MULTIPLE ENVIRONMENTAL CHEMICALS (LEAD, METHYLMERCURY AND POLYCHLORINATED BIPHENYLS) AMONG CHILDBEARING-AGED WOMEN IN THE U.S.

It is estimated that 5 to 20% of neurodevelopmental disabilities in children are caused by environmental toxic exposures. Lead, methylmercury and polychlorinated biphenyls (PCBs) are known to have neurobehavioral and neurodevelopmental consequences in animal models and human population studies. Bioaccumulation and exposures during gestation transfer from mother to fetus via the placenta and to an infant and young child through lactation. Little is known about multiple environmental chemical exposures, especially among childbearing-aged women. This descriptive and exploratory study involved analysis of existing data from the National Health and Nutrition Examination Survey (NHANES), a national probability sample. Lead, methylmercury and the summed value of four lipid-adjusted PCB congeners (118, 138/158, 153, 180) were measured in the blood or serum of childbearing-aged females aged 16 to 49 of diverse races and ethnicities who were living in the U.S. 1999 to 2004, including a subset of pregnant women. Exposure was defined as two or more xenobiotic blood levels at or above the geometric mean. Sexton, Olden and Johnson’s modified environmental health paradigm (1993) guided the selection of 62 measures of vulnerability (susceptibility- and exposure-related attributes, socioeconomic factors and race-ethnicity). Findings were reported for weighted (adjusted) data. The prevalence of exposures was widespread among childbearing-aged women, one fifth of whom had xenobiotic blood levels at or above the geometric mean for all three chemicals. Overall, pregnant women had lower prevalence rates. Best-fit logistic regression exposure model contained 13 variables. Three were notable. Any fish consumption in past 30 days tripled the risk. A non-linear relationship was demonstrated with increasing age, exponential at ages 40 to 49. Past and current breastfeeding was protective for these women. Current pregnancy was protective with regard to individual chemical exposures only. Statistically significant two-way interactions were identified even though the paradigm could not be fully tested. Further research on exposures to multiple environmental chemicals using the modified environmental health paradigm is needed. Xenobiotic biomonitoring in conjunction with risk communication among childbearing-aged women is encouraged. Precautionary level interventions aimed at eliminating or minimizing exposures are urgently needed. Bioaccumulation and transgenerational consequences of exposures should be addressed in public health policy

The Case for Case Studies

This volume demonstrates how to conduct case study research that is both methodologically rigorous and useful to development policy. It will interest scholars and students across the social sciences using case studies, and provide constructive guidance to practitioners in development and public administration