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Multiple Outcomes (multiple + outcome)
Selected AbstractsComparison of Fracture, Cardiovascular Event, and Breast Cancer Rates at 3 Years in Postmenopausal Women with OsteoporosisJOURNAL OF AMERICAN GERIATRICS SOCIETY, Issue 9 2004Stuart L. Silverman MD Objectives: To compare event rates for osteoporotic fractures, cardiovascular events, and breast cancer in postmenopausal women with osteoporosis. Design: A prospective, observational study of the placebo group in the double-blind, randomized Multiple Outcomes of Raloxifene Evaluation trial. Setting: One hundred eighty clinical research centers in 25 countries. Participants: Postmenopausal women (n=2,565, mean age=67) with osteoporosis were given calcium (500 mg/d) and vitamin D (400,600 IU/d) supplements. Measurements: The occurrence of at least one new fracture, cardiovascular event, or breast cancer diagnosis at 3 years was identified and adjudicated. Results: The occurrence of any fracture was the most common event in these women. In women without prevalent vertebral fractures (n=1,627), the event rates per 1,000 patient-years were 45.4 for any fracture, 15.2 for vertebral fracture, 4.7 for clinical vertebral fracture, 0.9 for hip fracture, 8.3 for any cardiovascular event, and 5.2 for all breast cancer. In women with prevalent vertebral fractures (n=938), the event rates per 1,000 patient-years were 117.4 for any new fracture, 77.1 for new vertebral fracture, 25.7 for clinical vertebral fracture, 5.8 for hip fracture, 15.1 for any cardiovascular event, and 2.6 for all breast cancer. The effect of prevalent fracture status on event rates was not dependent on whether women were older or younger than 65, but women aged 65 and older had a 3.6 times greater occurrence of cardiovascular events than younger women, irrespective of prevalent fracture status. Conclusion: These data on the relative incidence of clinically significant skeletal and extra-skeletal outcomes may be useful in choosing an agent for health maintenance for postmenopausal women with osteoporosis. [source] Associations Between Baseline Risk Factors and Vertebral Fracture Risk in the Multiple Outcomes of Raloxifene Evaluation (MORE) StudyJOURNAL OF BONE AND MINERAL RESEARCH, Issue 5 2004Olof Johnell Abstract Different risk factors may influence the effectiveness of osteoporosis therapies. The interaction of 30 baseline risk factors and the effectiveness of raloxifene in the MORE study were assessed. The efficacy of raloxifene in reducing vertebral fractures is largely independent of the presence of clinical risk factors for osteoporotic fractures. Introduction: The aim of this analysis was to determine the effect of different risk factors on the effectiveness of raloxifene to reduce vertebral fractures in the Multiple Outcomes of Raloxifene Evaluation (MORE) study using logistic regression models. Materials and Methods: The association was assessed using univariate analyses and a multivariate model between 30 potential risk factors at baseline and the risk of vertebral fractures after 3 years in the placebo group, as well as the interaction of risk factors with raloxifene therapy (at a dose of 60 or 120 mg/day). Results and Conclusions: In the univariate analysis of the placebo group, after adjusting for baseline lumbar spine BMD (LS BMD), short stature (odds ratio [OR] = 1.18), age (OR = 1.38), years since menopause (OR = 1.38), impaired cognitive function, visuospatial capabilities (OR = 1.19), impaired musculoskeletal strength (OR = 1.23), low femoral neck BMD (OR = 1.21), and prior vertebral fracture (OR = 4.95) were significantly associated with the incidence of new vertebral fractures. In the univariate analysis, significant interactions were observed between raloxifene treatment and age (p = 0.04), serum triglycerides (p = 0.03), LS BMD (p = 0.08), and diabetes mellitus (p = 0.04). In the multivariate analysis, the effectiveness of raloxifene was independent of almost all risk factors, with the exception of baseline serum triglyceride level and LS BMD, suggesting an increased efficacy of raloxifene in patients with increased triglyceride levels (p = 0.006) and lower LS BMD values (p = 0.008) at baseline. These data suggest that the efficacy of raloxifene in reducing vertebral fractures is largely independent of the presence of clinical risk factors for osteoporotic fractures. [source] Relationships Between Bone Mineral Density and Incident Vertebral Fracture Risk with Raloxifene Therapy,JOURNAL OF BONE AND MINERAL RESEARCH, Issue 1 2002Somnath Sarkar Ph.D. Abstract Although low absolute values of bone mineral density (BMD) predict increased fracture risk in osteoporosis, it is not certain how well increases in BMD with antiresorptive therapy predict observed reductions in fracture risk. This work examines the relationships between changes in BMD after 1 year or 3 years of raloxifene or placebo therapy and the risk for new vertebral fractures at 3 years. In the Multiple Outcomes of Raloxifene Evaluation (MORE) trial, 7705 postmenopausal women with osteoporosis were randomized to placebo or raloxifene 60 mg/day or 120 mg/day. Relationships between baseline BMD and changes in BMD from baseline with the risk of new vertebral fractures were analyzed in this cohort using logistic regression models with the raloxifene doses pooled. As has been observed in other populations, women with the lowest baseline lumbar spine or femoral neck BMD in the MORE cohort had the greatest risk for vertebral fractures. Furthermore, for any percentage change, either increase or decrease in femoral neck or lumbar spine BMD at 1 year or 3 years, raloxifene-treated patients had a statistically significantly lower vertebral fracture risk compared with placebo-treated patients. The decrease in fracture risk with raloxifene was similar across the range of percentage change in femoral neck BMD observed at 3 years; patients receiving raloxifene had a 36% lower risk of vertebral fracture compared with those receiving placebo. At any percentage change in femoral neck and lumbar spine BMD observed at 1 year, raloxifene treatment decreased the risks of new vertebral fractures at 3 years by 38% and 41%, respectively. The logistic regression model showed that the percentage changes in BMD with raloxifene treatment accounted for 4% of the observed vertebral fracture risk reduction, and the other 96% of the risk reduction remains unexplained. The present data show that the measured BMD changes observed with raloxifene therapy are poor predictors of vertebral fracture risk reduction with raloxifene therapy. [source] Discriminant Analysis for Longitudinal Data with Multiple Continuous Responses and Possibly Missing DataBIOMETRICS, Issue 1 2009Guillermo Marshall Summary Multiple outcomes are often used to properly characterize an effect of interest. This article discusses model-based statistical methods for the classification of units into one of two or more groups where, for each unit, repeated measurements over time are obtained on each outcome. We relate the observed outcomes using multivariate nonlinear mixed-effects models to describe evolutions in different groups. Due to its flexibility, the random-effects approach for the joint modeling of multiple outcomes can be used to estimate population parameters for a discriminant model that classifies units into distinct predefined groups or populations. Parameter estimation is done via the expectation-maximization algorithm with a linear approximation step. We conduct a simulation study that sheds light on the effect that the linear approximation has on classification results. We present an example using data from a study in 161 pregnant women in Santiago, Chile, where the main interest is to predict normal versus abnormal pregnancy outcomes. [source] Work redesign: Eight obstacles and opportunitiesHUMAN RESOURCE MANAGEMENT, Issue 4 2005Michael A. Campion Successful work-design initiatives must overcome many obstacles in order to have their intended impact. This article outlines eight obstacles to work redesign: (1) influences on multiple outcomes, (2) trade-offs between different approaches, (3) difficulty in choosing appropriate units of analysis, (4) difficulty in predicting the nature of the job, (5) complications from individual differences, (6) job enlargement occurring without job enrichment, (7) creating new jobs as part of growth or downsizing, and (8) differences between longterm and short- term effects. This article examines the nature of these eight obstacles, reviews prior research on this topic, and outlines suggestions for managing these obstacles in practice. © 2005 Wiley Periodicals, Inc. [source] Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome dataJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2009Stuart R. Lipsitz Summary., In a large, prospective longitudinal study designed to monitor cardiac abnormalities in children born to women who are infected with the human immunodeficiency virus, instead of a single outcome variable, there are multiple binary outcomes (e.g. abnormal heart rate, abnormal blood pressure and abnormal heart wall thickness) considered as joint measures of heart function over time. In the presence of missing responses at some time points, longitudinal marginal models for these multiple outcomes can be estimated by using generalized estimating equations (GEEs), and consistent estimates can be obtained under the assumption of a missingness completely at random mechanism. When the missing data mechanism is missingness at random, i.e. the probability of missing a particular outcome at a time point depends on observed values of that outcome and the remaining outcomes at other time points, we propose joint estimation of the marginal models by using a single modified GEE based on an EM-type algorithm. The method proposed is motivated by the longitudinal study of cardiac abnormalities in children who were born to women infected with the human immunodeficiency virus, and analyses of these data are presented to illustrate the application of the method. Further, in an asymptotic study of bias, we show that, under a missingness at random mechanism in which missingness depends on all observed outcome variables, our joint estimation via the modified GEE produces almost unbiased estimates, provided that the correlation model has been correctly specified, whereas estimates from standard GEEs can lead to substantial bias. [source] Power and sample size when multiple endpoints are consideredPHARMACEUTICAL STATISTICS: THE JOURNAL OF APPLIED STATISTICS IN THE PHARMACEUTICAL INDUSTRY, Issue 3 2007Stephen Senn Abstract A common approach to analysing clinical trials with multiple outcomes is to control the probability for the trial as a whole of making at least one incorrect positive finding under any configuration of true and false null hypotheses. Popular approaches are to use Bonferroni corrections or structured approaches such as, for example, closed-test procedures. As is well known, such strategies, which control the family-wise error rate, typically reduce the type I error for some or all the tests of the various null hypotheses to below the nominal level. In consequence, there is generally a loss of power for individual tests. What is less well appreciated, perhaps, is that depending on approach and circumstances, the test-wise loss of power does not necessarily lead to a family wise loss of power. In fact, it may be possible to increase the overall power of a trial by carrying out tests on multiple outcomes without increasing the probability of making at least one type I error when all null hypotheses are true. We examine two types of problems to illustrate this. Unstructured testing problems arise typically (but not exclusively) when many outcomes are being measured. We consider the case of more than two hypotheses when a Bonferroni approach is being applied while for illustration we assume compound symmetry to hold for the correlation of all variables. Using the device of a latent variable it is easy to show that power is not reduced as the number of variables tested increases, provided that the common correlation coefficient is not too high (say less than 0.75). Afterwards, we will consider structured testing problems. Here, multiplicity problems arising from the comparison of more than two treatments, as opposed to more than one measurement, are typical. We conduct a numerical study and conclude again that power is not reduced as the number of tested variables increases. Copyright © 2007 John Wiley & Sons, Ltd. [source] A Partially Linear Tree-based Regression Model for Multivariate OutcomesBIOMETRICS, Issue 1 2010Kai Yu Summary In the genetic study of complex traits, especially behavior related ones, such as smoking and alcoholism, usually several phenotypic measurements are obtained for the description of the complex trait, but no single measurement can quantify fully the complicated characteristics of the symptom because of our lack of understanding of the underlying etiology. If those phenotypes share a common genetic mechanism, rather than studying each individual phenotype separately, it is more advantageous to analyze them jointly as a multivariate trait to enhance the power to identify associated genes. We propose a multilocus association test for the study of multivariate traits. The test is derived from a partially linear tree-based regression model for multiple outcomes. This novel tree-based model provides a formal statistical testing framework for the evaluation of the association between a multivariate outcome and a set of candidate predictors, such as markers within a gene or pathway, while accommodating adjustment for other covariates. Through simulation studies we show that the proposed method has an acceptable type I error rate and improved power over the univariate outcome analysis, which studies each component of the complex trait separately with multiple-comparison adjustment. A candidate gene association study of multiple smoking-related phenotypes is used to demonstrate the application and advantages of this new method. The proposed method is general enough to be used for the assessment of the joint effect of a set of multiple risk factors on a multivariate outcome in other biomedical research settings. [source] Discriminant Analysis for Longitudinal Data with Multiple Continuous Responses and Possibly Missing DataBIOMETRICS, Issue 1 2009Guillermo Marshall Summary Multiple outcomes are often used to properly characterize an effect of interest. This article discusses model-based statistical methods for the classification of units into one of two or more groups where, for each unit, repeated measurements over time are obtained on each outcome. We relate the observed outcomes using multivariate nonlinear mixed-effects models to describe evolutions in different groups. Due to its flexibility, the random-effects approach for the joint modeling of multiple outcomes can be used to estimate population parameters for a discriminant model that classifies units into distinct predefined groups or populations. Parameter estimation is done via the expectation-maximization algorithm with a linear approximation step. We conduct a simulation study that sheds light on the effect that the linear approximation has on classification results. We present an example using data from a study in 161 pregnant women in Santiago, Chile, where the main interest is to predict normal versus abnormal pregnancy outcomes. [source] Quantitative Risk Assessment for Multivariate Continuous Outcomes with Application to Neurotoxicology: The Bivariate CaseBIOMETRICS, Issue 3 2005Zi-Fan Yu Summary The neurotoxic effects of chemical agents are often investigated in controlled studies on rodents, with multiple binary and continuous endpoints routinely collected. One goal is to conduct quantitative risk assessment to determine safe dose levels. Such studies face two major challenges for continuous outcomes. First, characterizing risk and defining a benchmark dose are difficult. Usually associated with an adverse binary event, risk is clearly definable in quantal settings as presence or absence of an event; finding a similar probability scale for continuous outcomes is less clear. Often, an adverse event is defined for continuous outcomes as any value below a specified cutoff level in a distribution assumed normal or log normal. Second, while continuous outcomes are traditionally analyzed separately for such studies, recent literature advocates also using multiple outcomes to assess risk. We propose a method for modeling and quantitative risk assessment for bivariate continuous outcomes that address both difficulties by extending existing percentile regression methods. The model is likelihood based; it allows separate dose,response models for each outcome while accounting for the bivariate correlation and overall characterization of risk. The approach to estimation of a benchmark dose is analogous to that for quantal data without the need to specify arbitrary cutoff values. We illustrate our methods with data from a neurotoxicity study of triethyl tin exposure in rats. [source] Latent Transition Regression for Mixed OutcomesBIOMETRICS, Issue 3 2003Diana L. Miglioretti Summary. Health status is a complex outcome, often characterized by multiple measures. When assessing changes in health status over time, multiple measures are typically collected longitudinally. Analytic challenges posed by these multivariate longitudinal data are further complicated when the outcomes are combinations of continuous, categorical, and count data. To address these challenges, we propose a fully Bayesian latent transition regression approach for jointly analyzing a mixture of longitudinal outcomes from any distribution. Health status is assumed to be a categorical latent variable, and the multiple outcomes are treated as surrogate measures of the latent health state, observed with error. Using this approach, both baseline latent health state prevalences and the probabilities of transitioning between the health states over time are modeled as functions of covariates. The observed outcomes are related to the latent health states through regression models that include subject-specific effects to account for residual correlation among repeated measures over time, and covariate effects to account for differential measurement of the latent health states. We illustrate our approach with data from a longitudinal study of back pain. [source] Classroom and Developmental Differences in a Path Model of Teacher Expectancy EffectsCHILD DEVELOPMENT, Issue 5 2001Margaret R. Kuklinski A path model of teacher expectancy effects was evaluated in a sample of 376 first- through fifth-grade urban elementary school children. The roles of two moderators (classroom perceived differential treatment environment and developmental differences) and one mediator (children's self-expectations) of teacher expectancy effects on children's year-end achievement were examined. Significant differences in effects and effect sizes are presented. Both classroom environment (high versus low in differential treatment, as seen through children's eyes) and developmental differences moderated the strength of teacher expectancy effects. Generally, stronger effects were found in classrooms in which expectancy-related cues were more salient to children, but developmental differences moderated which effect was most pronounced. A significant age-related decline in direct effects on ending achievement was interpreted as evidence that teacher expectations may tend to magnify achievement differences in the early grades, but serve to sustain them in later grades. Support for indirect effects (teacher expectations , children's self-expectations , ending achievement) was limited to upper elementary grade classrooms perceived as high in differential treatment. In contrast to prior research that emphasized small effect sizes, the present analyses document several instances of moderate effects, primarily in classrooms in which expectancy-related messages were most salient to children. These results underscore the importance of explicit attention to the inclusion of moderators, mediators, and multiple outcomes in efforts to understand teacher expectancy effects. [source] | |||||||||||||