Analyzing Repeated Measures Marginal Models on Sample Surveys with Resampling Methods

Packaged statistical software for analyzing categorical, repeated measures marginal models on sample survey data with binary covariates does not appear to be available. Consequently, this report describes a customized SAS program which accomplishes such an analysis on survey data with jackknifed replicate weights for which the primary sampling unit information has been suppressed for respondent confidentiality. First, the program employs the Macro Language and the Output Delivery System (ODS) to estimate the means and covariances of indicator variables for the response variables, taking the design into account. Then, it uses PROC CATMOD and ODS, ignoring the survey design, to obtain the design matrix and hypothesis test specifications. Finally, it enters these results into another run of CATMOD, which performs automated direct input of the survey design specifications and accomplishes the appropriate analysis. This customized SAS program can be employed, with minor editing, to analyze general categorical, repeated measures marginal models on sample surveys with replicate weights. Finally, the results of our analysis accounting for the survey design are compared to the results of two alternate analyses of the same data. This comparison confirms that such alternate analyses, which do not properly account for the design, do not produce useful results.

Analyzing Repeated Measures Marginal Models on Sample Surveys with Resampling Methods

Packaged statistical software for analyzing categorical, repeated measures marginal models on sample survey data with binary covariates does not appear to be available. Consequently, this report describes a customized SAS program which accomplishes such an analysis on survey data with jackknifed replicate weights for which the primary sampling unit information has been suppressed for respondent confidentiality. First, the program employs the Macro Language and the Output Delivery System (ODS) to estimate the means and covariances of indicator variables for the response variables, taking the design into account. Then, it uses PROC CATMOD and ODS, ignoring the survey design, to obtain the design matrix and hypothesis test specifications. Finally, it enters these results into another run of CATMOD, which performs automated direct input of the survey design specifications and accomplishes the appropriate analysis. This customized SAS program can be employed, with minor editing, to analyze general categorical, repeated measures marginal models on sample surveys with replicate weights. Finally, the results of our analysis accounting for the survey design are compared to the results of two alternate analyses of the same data. This comparison confirms that such alternate analyses, which do not properly account for the design, do not produce useful results

Health impact of US military service in a large population-based military cohort: findings of the Millennium Cohort Study, 2001-2008

<p>Abstract</p> <p>Background</p> <p>Combat-intense, lengthy, and multiple deployments in Iraq and Afghanistan have characterized the new millennium. The US military's all-volunteer force has never been better trained and technologically equipped to engage enemy combatants in multiple theaters of operations. Nonetheless, concerns over potential lasting effects of deployment on long-term health continue to mount and are yet to be elucidated. This report outlines how findings from the first 7 years of the Millennium Cohort Study have helped to address health concerns related to military service including deployments.</p> <p>Methods</p> <p>The Millennium Cohort Study was designed in the late 1990s to address veteran and public concerns for the first time using prospectively collected health and behavioral data.</p> <p>Results</p> <p>Over 150 000 active-duty, reserve, and National Guard personnel from all service branches have enrolled, and more than 70% of the first 2 enrollment panels submitted at least 1 follow-up survey. Approximately half of the Cohort has deployed in support of operations in Iraq and Afghanistan.</p> <p>Conclusion</p> <p>The Millennium Cohort Study is providing prospective data that will guide public health policymakers for years to come by exploring associations between military exposures and important health outcomes. Strategic studies aim to identify, reduce, and prevent adverse health outcomes that may be associated with military service, including those related to deployment.</p

Risk factors for progression of peripheral arterial disease in large and small vessels.

ERMAInternational audienceBACKGROUND: Data on the natural history of peripheral arterial disease (PAD) are scarce and are focused primarily on clinical symptoms. Using noninvasive tests, we assessed the role of traditional and novel risk factors on PAD progression. We hypothesized that the risk factors for large-vessel PAD (LV-PAD) progression might differ from small-vessel PAD (SV-PAD). METHODS AND RESULTS: Between 1990 and 1994, patients seen during the prior 10 years in our vascular laboratories were invited for a new vascular examination. The first assessment provided baseline data, with follow-up data obtained at this study. The highest decile of decline was considered major progression, which was a -0.30 ankle brachial index decrease for LV-PAD and a -0.27 toe brachial index decrease for SV-PAD progression. In addition to traditional risk factors, the roles of high-sensitivity C-reactive protein, serum amyloid-A, lipoprotein(a), and homocysteine were assessed. Over the average follow-up interval of 4.6+/-2.5 years, the 403 patients showed a significant ankle brachial index and toe brachial index deterioration. In multivariable analysis, current smoking, ratio of total to HDL cholesterol, lipoprotein(a), and high-sensitivity C-reactive protein were related to LV-PAD progression, whereas only diabetes was associated with SV-PAD progression. CONCLUSIONS: Risk factors contribute differentially to the progression of LV-PAD and SV-PAD. Cigarette smoking, lipids, and inflammation contribute to LV-PAD progression, whereas diabetes was the only significant predictor of SV-PAD progression