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97 results on '"Boonstra, Philip S"'

52. Sentinel Lymph Node Biopsy in Head and Neck Melanoma: Long‐term Outcomes, Prognostic Value, Accuracy, and Safety

Author

Hanks, John E., primary, Kovatch, Kevin J., additional, Ali, S. Ahmed, additional, Roberts, Emily, additional, Durham, Alison B., additional, Smith, Joshua D., additional, Bradford, Carol R., additional, Malloy, Kelly M., additional, Boonstra, Philip S., additional, Lao, Christopher D., additional, and McLean, Scott A., additional

Published
2020
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58. Multicentre retrospective study of intravascular large B‐cell lymphoma treated at academic institutions within the United States

Author

Geer, Marcus, primary, Roberts, Emily, additional, Shango, Maryann, additional, Till, Brian G., additional, Smith, Stephen D., additional, Abbas, Hashim, additional, Hill, Brian T., additional, Kaplan, Jason, additional, Barr, Paul M., additional, Caimi, Paolo, additional, Stephens, Deborah M., additional, Lin, Emily, additional, Herrera, Alex F., additional, Rosenbaum, Evan, additional, Amengual, Jennifer E., additional, Boonstra, Philip S., additional, Devata, Sumana, additional, Wilcox, Ryan A., additional, Kaminski, Mark S., additional, and Phillips, Tycel J., additional

Published
2019
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60. Measures of the Degree of Departure from Ignorable Sample Selection.

Author

Little, Roderick J A, West, Brady T, Boonstra, Philip S, and Hu, Jingwei

Subjects
SELECTION bias (Statistics), BIG data, SENSITIVITY analysis
Abstract

With the current focus of survey researchers on "big data" that are not selected by probability sampling, measures of the degree of potential sampling bias arising from this nonrandom selection are sorely needed. Existing indices of this degree of departure from probability sampling, like the R-indicator, are based on functions of the propensity of inclusion in the sample, estimated by modeling the inclusion probability as a function of auxiliary variables. These methods are agnostic about the relationship between the inclusion probability and survey outcomes, which is a crucial feature of the problem. We propose a simple index of degree of departure from ignorable sample selection that corrects this deficiency, which we call the standardized measure of unadjusted bias (SMUB). The index is based on normal pattern-mixture models for nonresponse applied to this sample selection problem and is grounded in the model-based framework of nonignorable selection first proposed in the context of nonresponse by Don Rubin in 1976. The index depends on an inestimable parameter that measures the deviation from selection at random, which ranges between the values zero and one. We propose the use of a central value of this parameter, 0.5, for computing a point index, and computing the values of SMUB at zero and one to provide a range of the index in a sensitivity analysis. We also provide a fully Bayesian approach for computing credible intervals for the SMUB, reflecting uncertainty in the values of all of the input parameters. The proposed methods have been implemented in R and are illustrated using real data from the National Survey of Family Growth. [ABSTRACT FROM AUTHOR]

Published
2020
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61. Incorporating historical models with adaptive Bayesian updates.

Author

Boonstra, Philip S and Barbaro, Ryan P

Subjects
*EXTRACORPOREAL membrane oxygenation, *INFORMATION modeling, *DEATH forecasting, *PREDICTION models, *STATISTICS, *COMPUTER simulation, *RESEARCH, *MORTALITY, *RESEARCH methodology, *EVALUATION research, *MEDICAL cooperation, *RISK assessment, *COMPARATIVE studies, *STATISTICAL models, *PROBABILITY theory
Abstract

This article considers Bayesian approaches for incorporating information from a historical model into a current analysis when the historical model includes only a subset of covariates currently of interest. The statistical challenge is 2-fold. First, the parameters in the nested historical model are not generally equal to their counterparts in the larger current model, neither in value nor interpretation. Second, because the historical information will not be equally informative for all parameters in the current analysis, additional regularization may be required beyond that provided by the historical information. We propose several novel extensions of the so-called power prior that adaptively combine a prior based upon the historical information with a variance-reducing prior that shrinks parameter values toward zero. The ideas are directly motivated by our work building mortality risk prediction models for pediatric patients receiving extracorporeal membrane oxygenation (ECMO). We have developed a model on a registry-based cohort of ECMO patients and now seek to expand this model with additional biometric measurements, not available in the registry, collected on a small auxiliary cohort. Our adaptive priors are able to use the information in the original model and identify novel mortality risk factors. We support this with a simulation study, which demonstrates the potential for efficiency gains in estimation under a variety of scenarios. [ABSTRACT FROM AUTHOR]

Published
2020
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62. Ruxolitinib Is Active and Well Tolerated in Adult Patients with Secondary Hemophagocytic Lymphohistiocytosis: Preliminary Results of an Ongoing, Open-Label, Single Center Study

Author

Alsawah, Fares, primary, Merrill, Samuel A., additional, Bockenstedt, Paula L., additional, Campagnaro, Erica L., additional, Devata, Sumana, additional, Gitlin, Scott D., additional, Kaminski, Mark S, additional, Cusick, Alice M., additional, Phillips, Tycel J., additional, Sood, Suman L., additional, Talpaz, Moshe, additional, Quiery, Albert Thomas, additional, Boonstra, Philip S, additional, Ahmed, Asra Z., additional, and Wilcox, Ryan A., additional

Published
2018
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66. Prediction of Radiation Esophagitis in Non–Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels

Author

Hawkins, Peter G., primary, Boonstra, Philip S., additional, Hobson, Stephen T., additional, Hayman, James A., additional, Ten Haken, Randall K., additional, Matuszak, Martha M., additional, Stanton, Paul, additional, Kalemkerian, Gregory P., additional, Lawrence, Theodore S., additional, Schipper, Matthew J., additional, Kong, Feng-Ming (Spring), additional, and Jolly, Shruti, additional

Published
2018
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67. Genetic anticipation in Swedish Lynch syndrome families

Author

von Salome, Jenny, Boonstra, Philip S., Karimi, Masoud, Silander, Gustav, Stenmark-Askmalm, Marie, Gebre-Medhin, Samuel, Aravidis, Christos, Nilbert, Mef, Lindblom, Annika, Lagerstedt-Robinson, Kristina, von Salome, Jenny, Boonstra, Philip S., Karimi, Masoud, Silander, Gustav, Stenmark-Askmalm, Marie, Gebre-Medhin, Samuel, Aravidis, Christos, Nilbert, Mef, Lindblom, Annika, and Lagerstedt-Robinson, Kristina

Abstract

Among hereditary colorectal cancer predisposing syndromes, Lynch syndrome (LS) caused by mutations in DNA mismatch repair genes MLH1, MSH2, MSH6 or PMS2 is the most common. Patients with LS have an increased risk of early onset colon and endometrial cancer, but also other tumors that generally have an earlier onset compared to the general population. However, age at first primary cancer varies within families and genetic anticipation, i.e. decreasing age at onset in successive generations, has been suggested in LS. Anticipation is a well-known phenomenon in e.g neurodegenerative diseases and several reports have studied anticipation in heritable cancer. The purpose of this study is to determine whether anticipation can be shown in a large cohort of Swedish LS families referred to the regional departments of clinical genetics in Lund, Stockholm, Linkoping, Uppsala and Umea between the years 1990-2013. We analyzed a homogenous group of mutation carriers, utilizing information from both affected and non-affected family members. In total, 239 families with a mismatch repair gene mutation (96 MLH1 families, 90 MSH2 families including one family with an EPCAM-MSH2 deletion, 39 MSH6 families, 12 PMS2 families, and 2 MLH1+PMS2 families) comprising 1028 at-risk carriers were identified among the Swedish LS families, of which 1003 mutation carriers had available follow-up information and could be included in the study. Using a normal random effects model (NREM) we estimate a 2.1 year decrease in age of diagnosis per generation. An alternative analysis using a mixed-effects Cox proportional hazards model (COX-R) estimates a hazard ratio of exp(0.171), or about 1.19, for age of diagnosis between consecutive generations. LS-associated gene-specific anticipation effects are evident for MSH2 (2.6 years/generation for NREM and hazard ratio of 1.33 for COX-R) and PMS2 (7.3 years/generation and hazard ratio of 1.86). The estimated anticipation effects for MLH1 and MSH6 are smaller.

Published
2017
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68. Genetic anticipation in Swedish Lynch syndrome families

Author

von Salomé, Jenny, primary, Boonstra, Philip S., additional, Karimi, Masoud, additional, Silander, Gustav, additional, Stenmark-Askmalm, Marie, additional, Gebre-Medhin, Samuel, additional, Aravidis, Christos, additional, Nilbert, Mef, additional, Lindblom, Annika, additional, and Lagerstedt-Robinson, Kristina, additional

Published
2017
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70. Dopamine D2/D3 imbalance during migraine attack and allodynia in vivo

Author

DaSilva, Alexandre F., primary, Nascimento, Thiago D., additional, Jassar, Hassan, additional, Heffernan, Joseph, additional, Toback, Rebecca L., additional, Lucas, Sarah, additional, DosSantos, Marcos F., additional, Bellile, Emily L., additional, Boonstra, Philip S., additional, Taylor, Jeremy M.G., additional, Casey, Kenneth L., additional, Koeppe, Robert A., additional, Smith, Yolanda R., additional, and Zubieta, Jon-Kar, additional

Published
2017
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73. Predictive models for splenic response to JAK-inhibitor therapy in patients with myelofibrosis.

Author

Menghrajani, Kamal, Boonstra, Philip S., Mercer, Jessica A., Perkins, Cecelia, Gowin, Krisstina L., Weber, Alissa A., Mesa, Ruben, Gotlib, Jason R., Wang, Lixia, Singer, Jack W., and Talpaz, Moshe

Subjects
*MYELOFIBROSIS, *PREDICTION models, *ALTERNATIVE medicine, *SPLEEN, *HEMOGLOBINS
Abstract

JAK inhibitors for myelofibrosis (MF) reduce spleen size, control constitutional symptoms, and may improve survival. We studied the clinical characteristics of 548 MF patients treated with JAK inhibitors from 2008 to 2016 to better understand predictors of splenic response. Response was defined as a 50% decrease in spleen size at early (3–4 months on therapy) and late (5–12 months) timepoints after therapy initiation. Early response positively correlated with higher doses of JAK inhibitor, baseline spleen size 5–10 cm, and hemoglobin. Early response negatively correlated with baseline spleen size >20 cm and high WBC. The strongest predictor of late response was whether a patient had a response at the earlier timepoint (OR 8.88). Our response models suggest that clinical factors can be used to predict which patients are more likely to respond to JAK inhibitors, and those who do not achieve an early response, i.e. within 3–4 months, should consider alternative treatments. [ABSTRACT FROM AUTHOR]

Published
2019
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77. Low-Cost Intervention to Increase Influenza Vaccination Rate at a Comprehensive Cancer Center

Author

Grivas, Petros D., primary, Devata, Sumana, additional, Khoriaty, Rami, additional, Boonstra, Philip S., additional, Ruch, Joshua, additional, McDonnell, Kevin, additional, Hernandez-Aya, Leonel, additional, Wilfong, Joshua, additional, Smerage, Jeffrey, additional, Ison, Michael G., additional, Eisenberg, Joseph N. S., additional, Silveira, Maria, additional, Cooney, Kathleen A., additional, and Worden, Francis P., additional

Published
2016
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79. Low-Cost Intervention to Increase Influenza Vaccination Rate at a Comprehensive Cancer Center.

Author

Grivas, Petros, Devata, Sumana, Khoriaty, Rami, Boonstra, Philip, Ruch, Joshua, McDonnell, Kevin, Hernandez-Aya, Leonel, Wilfong, Joshua, Smerage, Jeffrey, Ison, Michael, Eisenberg, Joseph, Silveira, Maria, Cooney, Kathleen, Worden, Francis, Grivas, Petros D, Boonstra, Philip S, Ison, Michael G, Eisenberg, Joseph N S, Cooney, Kathleen A, and Worden, Francis P

Subjects
INFLUENZA prevention, CANCER treatment, IMMUNIZATION, INFLUENZA vaccines, QUALITY assurance, SEASONS, SPECIALTY hospitals
Abstract

Influenza morbidity and mortality can be severe and costly. Vaccination rates remain suboptimal in cancer patients due to provider- and patient-related factors. The objective of this study was to evaluate whether low-cost provider- and patient-focused interventions would increase influenza vaccination rates at the University of Michigan Comprehensive Cancer Center (UMCCC). This quality improvement project included all patients without documentation of influenza vaccination prior to their first outpatient appointment during the 2011-2012 and 2012-2013 influenza seasons. The multi-stepped intervention included provider and patient reminders. Influenza vaccination rates were compiled using CPT-4 codes. Same-day (with appointment) vaccination rates during the intervention seasons were compared to historical (2005-2011 seasons) controls; vaccination rates were also compared to contemporary control population at the University of Michigan Health System (UMHS). Reasons for non-adherence with vaccination were explored. The cumulative same-day vaccination rate in eligible adults was 10.1 % (2011-2012) and 9.4 % (2012-2013) compared to an average 6.9 % during influenza seasons 2005-2011. Based on logistic regression analysis, there was a 37.6 % (95 % CI 35-40.3 %) and 56.1 % (95 % CI 40.9-73 %) relative increase in the adult vaccination rate associated with the intervention, with 399 and 697 additional vaccinations, respectively, for each season. During the 2012-2013 season, the UMCCC adult vaccination rate was higher compared to the remainder of that of the UMHS. The intervention was well accepted by providers. Reasons for no vaccination were provider- and patient-related. Increasing provider and patient awareness with a simple, inexpensive intervention was associated with higher influenza vaccination rates at a large academic cancer center. The intervention is permanently implemented during influenza seasons. [ABSTRACT FROM AUTHOR]

Published
2017
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80. Multicenter Retrospective Study of Features and Outcomes of Patients with Intravascular DLBCL Treated at Academic Institutions within the United States

Author

Geer, Marcus, Shango, Maryann, Till, Brian G., Smith, Stephen D., Abbas, Hashim, Hill, Brian T., Kaplan, Jason, Barr, Paul M., Caimi, Paolo, Stephens, Deborah M., Lin, Emily, Herrera, Alex F., Rosenbaum, Evan, Amengual, Jennifer E, Roberts, Emily, Boonstra, Philip S, Devata, Sumana, Wilcox, Ryan A., Kaminski, Mark S, and Phillips, Tycel J.

Published
2017
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82. Low-cost stepped intervention to increase influenza vaccination rates at a Comprehensive Cancer Center.

Author

Grivas, Petros, primary, Devata, Sumana, additional, Khoriaty, Rami, additional, Boonstra, Philip S., additional, Ruch, Joshua Michael, additional, McDonnell, Kevin, additional, Hernandez-Aya, Leonel Fernando, additional, Wilfong, Joshua Murray, additional, Smerage, Jeffrey B., additional, Ison, Michael G, additional, Eisenberg, Joseph NS, additional, Silveira, Maria, additional, Cooney, Kathleen A., additional, and Worden, Francis P., additional

Published
2015
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86. Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome

Author

Boonstra, Philip S, Mukherjee, Bhramar, Taylor, Jeremy M G, Nilbert, Mef, Moreno, Victor, Gruber, Stephen B, Boonstra, Philip S, Mukherjee, Bhramar, Taylor, Jeremy M G, Nilbert, Mef, Moreno, Victor, and Gruber, Stephen B

Abstract

Summary Genetic anticipation, described by earlier age of onset (AOO) and more aggressive symptoms in successive generations, is a phenomenon noted in certain hereditary diseases. Its extent may vary between families and/or between mutation subtypes known to be associated with the disease phenotype. In this article, we posit a Bayesian approach to infer genetic anticipation under flexible random effects models for censored data that capture the effect of successive generations on AOO. Primary interest lies in the random effects. Misspecifying the distribution of random effects may result in incorrect inferential conclusions. We compare the fit of four-candidate random effects distributions via Bayesian model fit diagnostics. A related statistical issue here is isolating the confounding effect of changes in secular trends, screening, and medical practices that may affect time to disease detection across birth cohorts. Using historic cancer registry data, we borrow from relative survival analysis methods to adjust for changes in age-specific incidence across birth cohorts. Our motivating case study comes from a Danish cancer register of 124 families with mutations in mismatch repair (MMR) genes known to cause hereditary nonpolyposis colorectal cancer, also called Lynch syndrome (LS). We find evidence for a decrease in AOO between generations in this article. Our model predicts family-level anticipation effects that are potentially useful in genetic counseling clinics for high-risk families.

Published
2011

87. A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome

Author

Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, Departments of Epidemiology and Human Genetics, University of Michigan, Ann Arbor, Michigan ; Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan ; 1420 Washington Hgts, Ann Arbor, MI 48103, The Danish HNPCC-register, Hvidovre University Hospital, Hvidovre, Denmark, Clinical Research Centre, Copenhagen University Hospital, Hvidovre, Denmark, Boonstra, Philip S., Gruber, Stephen B., Raymond, Victoria M., Huang, Shu-Chen, Timshel, Susanne, Nilbert, Mef, Mukherjee, Bhramar, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, Departments of Epidemiology and Human Genetics, University of Michigan, Ann Arbor, Michigan ; Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan ; 1420 Washington Hgts, Ann Arbor, MI 48103, The Danish HNPCC-register, Hvidovre University Hospital, Hvidovre, Denmark, Clinical Research Centre, Copenhagen University Hospital, Hvidovre, Denmark, Boonstra, Philip S., Gruber, Stephen B., Raymond, Victoria M., Huang, Shu-Chen, Timshel, Susanne, Nilbert, Mef, and Mukherjee, Bhramar

Abstract

Anticipation, manifested through decreasing age of onset or increased severity in successive generations, has been noted in several genetic diseases. Statistical methods for genetic anticipation range from a simple use of the paired t -test for age of onset restricted to affected parent-child pairs to a recently proposed random effects model which includes extended pedigree data and unaffected family members [Larsen et al., 2009 ]. A naive use of the paired t -test is biased for the simple reason that age of onset has to be less than the age at ascertainment (interview) for both affected parent and child, and this right truncation effect is more pronounced in children than in parents. In this study, we first review different statistical methods for testing genetic anticipation in affected parent-child pairs that address the issue of bias due to right truncation. Using affected parent-child pair data, we compare the paired t -test with the parametric conditional maximum likelihood approach of Huang and Vieland [ 1997 ] and the nonparametric approach of Rabinowitz and Yang [ 1999 ] in terms of Type I error and power under various simulation settings and departures from the modeling assumptions. We especially investigate the issue of multiplex ascertainment and its effect on the different methods. We then focus on exploring genetic anticipation in Lynch syndrome and analyze new data on the age of onset in affected parent-child pairs from families seen at the University of Michigan Cancer Genetics clinic with a mutation in one of the three main mismatch repair (MMR) genes. In contrast to the clinic-based population, we re-analyze data on a population-based Lynch syndrome cohort, derived from the Danish HNPCC-register. Both datasets indicate evidence of genetic anticipation in Lynch syndrome. We then expand our review to incorporate recently proposed statistical methods that consider family instead of affected pairs as the sampling unit. These prospective censored regres

Published
2010

88. A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome

Author

Boonstra, Philip S, Gruber, Stephen B, Raymond, Victoria M, Huang, Shu-Chen, Timshel, Susanne, Nilbert, Mef, Mukherjee, Bhramar, Boonstra, Philip S, Gruber, Stephen B, Raymond, Victoria M, Huang, Shu-Chen, Timshel, Susanne, Nilbert, Mef, and Mukherjee, Bhramar

Abstract

Udgivelsesdato: 2010-Nov, Anticipation, manifested through decreasing age of onset or increased severity in successive generations, has been noted in several genetic diseases. Statistical methods for genetic anticipation range from a simple use of the paired t-test for age of onset restricted to affected parent-child pairs to a recently proposed random effects model which includes extended pedigree data and unaffected family members [Larsen et al., 2009]. A naive use of the paired t-test is biased for the simple reason that age of onset has to be less than the age at ascertainment (interview) for both affected parent and child, and this right truncation effect is more pronounced in children than in parents. In this study, we first review different statistical methods for testing genetic anticipation in affected parent-child pairs that address the issue of bias due to right truncation. Using affected parent-child pair data, we compare the paired t-test with the parametric conditional maximum likelihood approach of Huang and Vieland [1997] and the nonparametric approach of Rabinowitz and Yang [1999] in terms of Type I error and power under various simulation settings and departures from the modeling assumptions. We especially investigate the issue of multiplex ascertainment and its effect on the different methods. We then focus on exploring genetic anticipation in Lynch syndrome and analyze new data on the age of onset in affected parent-child pairs from families seen at the University of Michigan Cancer Genetics clinic with a mutation in one of the three main mismatch repair (MMR) genes. In contrast to the clinic-based population, we re-analyze data on a population-based Lynch syndrome cohort, derived from the Danish HNPCC-register. Both datasets indicate evidence of genetic anticipation in Lynch syndrome. We then expand our review to incorporate recently proposed statistical methods that consider family instead of affected pairs as the sampling unit. These prospective censored regression mod

Published
2010

91. Su2003 Cancer Spectrum, Parent of Origin Effect, and Anticipation in a Cohort of Lynch Syndrome Kindreds

Author

Walshe, Margaret B., primary, Farrell, Michael P., additional, Lyons, Tomas G., additional, Gogarty, Darragh S., additional, Dorman, Naoise M., additional, Hughes, David J., additional, O'Brien, Loretta, additional, Schmid, Jasmin, additional, Boonstra, Philip S., additional, Mukherjee, Bhramar, additional, Gallagher, David J., additional, and Mathuna, Padraic Mac, additional

Published
2013
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95. Propensity score-based diagnostics for categorical response regression models.

Author

Boonstra, Philip S., Bondarenko, Irina, Park, Sung Kyun, Vokonas, Pantel S., and Mukherjee, Bhramar

Abstract

For binary or categorical response models, most goodness-of-fit statistics are based on the notion of partitioning the subjects into groups or regions and comparing the observed and predicted responses in these regions by a suitable chi-squared distribution. Existing strategies create this partition based on the predicted response probabilities, or propensity scores, from the fitted model. In this paper, we follow a retrospective approach, borrowing the notion of balancing scores used in causal inference to inspect the conditional distribution of the predictors, given the propensity scores, in each category of the response to assess model adequacy. We can use this diagnostic under both prospective and retrospective sampling designs, and it may ascertain general forms of misspecification. We first present simple graphical and numerical summaries that can be used in a binary logistic model. We then generalize the tools to propose model diagnostics for the proportional odds model. We illustrate the methods with simulation studies and two data examples: (i) a case-control study of the association between cumulative lead exposure and Parkinson's disease in the Boston, Massachusetts, area and (ii) and a cohort study of biomarkers possibly associated with diabetes, from the VA Normative Aging Study. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Published
2014
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97. Shrinkage Methods Utilizing Auxiliary Information to Improve High-Dimensional Prediction Models.

Author

Boonstra, Philip S.

Subjects
Genomics, Missing Data, Measurement Error, Ridge Regression, Penalized Likelihood, EM Algorithm
Abstract

With advancements in genomic technologies, it is common to have two high-dimensional datasets, each measuring one underlying biological phenomenon with different techniques. We consider predicting a continuous outcome Y using X, a set of p markers that best measure the underlying biological process. This same process is also measured by W, coming from prior technology but correlated with X. We have (Y,X,W) on a moderately-sized sample and (Y,W) on a larger sample. We utilize the data on W to boost prediction of Y by X when p is large. Our work is motivated by a dataset containing gene-expression measurements from both quantitative real-time polymerase chain reaction and microarray technologies. First, we propose a class of targeted ridge (TR) estimators that shrink the regression coefficients of Y on X toward targets derived using the larger dataset and give two specific TR estimators. A hybrid estimator combines multiple TR estimators, data-adaptively balancing efficiency and robustness. Next, we view the problem from a Bayesian perspective. Hyperparameters control the shrinkage of the model parameters, giving flexibility in terms of what to shrink and to what extent. All unknown quantities – the missing X’s from the larger sample, the model parameters, and the shrinkage parameters – are iteratively sampled. Alternatively, we show how Empirical Bayes methods which maximize marginal likelihoods can estimate the shrinkage parameters. Finally, we consider estimating the tuning parameter of a ridge regression, particularly when the sample size is small relative to the number of predictors. A proposed corrected generalized cross-validation criterion is not subject to overfitting but remains asymptotically optimal. We also define a hyperpenalty that shrinks the tuning parameter itself, protecting against over- or underfitting. Maximizing the "hyperpenalized" likelihood can yield smaller prediction error than many common alternatives. Embedding the hyperpenalty into the penalized EM algorithm yields a hyperpenalized EM algorithm, which may be applied to the original missing data prediction problem. All of the approaches are compared via simulation studies and applied to the motivating gene-expression dataset. This dissertation therefore contributes to the literature on missing data and measurement error methods as they relate to prediction in high-dimensional models.

Published
2012

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