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.

A review of statistical methods in the analysis of data arising from observer reliability studies (Part II) *

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75734/1/j.1467-9574.1975.tb00259.x.pd

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

Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment

Covariate-adjusted sensitivity analyses is proposed for missing time-to-event outcomes. The method invokes multiple imputation (MI) for the missing failure times under a variety of specifications regarding the post-withdrawal tendency for having the event of interest. With a clinical trial example, we compared methods of covariance analyses for time-to-event data, i.e., the multivariable Cox proportional hazards model and non-parametric ANCOVA, and then illustrated how to incorporate these methods into the proposed sensitivity analysis for covariate adjustment. The MI methods considered are Kaplan-Meier Multiple Imputation (KMMI), covariate-adjusted and unadjusted proportional hazards multiple imputation (PHMI). The assumptions, statistical issues, and features for these methods are discussed

An Assessment of NASA Glenn's Aeroacoustic Experimental and Predictive Capabilities for Installed Cooling Fans

Driven by the need for low production costs, electronics cooling fans have evolved differently than the bladed components of gas turbine engines which incorporate multiple technologies to enhance performance and durability while reducing noise emissions. Drawing upon NASA Glenn's experience in the measurement and prediction of gas turbine engine aeroacoustic performance, tests have been conducted to determine if these tools and techniques can be extended for application to the aerodynamics and acoustics of electronics cooling fans. An automated fan plenum installed in NASA Glenn's Acoustical Testing Laboratory was used to map the overall aerodynamic and acoustic performance of a spaceflight qualified 80 mm diameter axial cooling fan. In order to more accurately identify noise sources, diagnose performance limiting aerodynamic deficiencies, and validate noise prediction codes, additional aerodynamic measurements were recorded for two operating points: free delivery and a mild stall condition. Non-uniformities in the fan s inlet and exhaust regions captured by Particle Image Velocimetry measurements, and rotor blade wakes characterized by hot wire anemometry measurements provide some assessment of the fan aerodynamic performance. The data can be used to identify fan installation/design changes which could enlarge the stable operating region for the fan and improve its aerodynamic performance and reduce noise emissions

A robust method for comparing two treatments in a confirmatory clinical trial via multivariate time-to-event methods that jointly incorporate information from longitudinal and time-to-event data

We consider regulatory clinical trials that required a pre-specified method for the comparison of two treatments for chronic diseases (e.g. Chronic Obstructive Pulmonary Disease) in which patients suffer deterioration in a longitudinal process until death occurs. We define a composite endpoint structure that encompasses both the longitudinal data for deterioration and the time-to-event data for death, and use multivariate time-to-event methods to assess treatment differences on both data structures simultaneously, without a need for parametric assumptions or modeling. Our method is straightforward to implement, and simulations show the method has robust power in situations in which incomplete data could lead to lower than expected power for either the longitudinal or survival data. We illustrate the method on data from a study of chronic lung disease

Dose-Weighted Adjusted Mantel-Haenszel Tests for Numeric Scaled Strata in a Randomized Trial

A recent three-arm parallel groups randomized clinical prevention trial had a protocol deviation causing participants to have fewer active doses of an in-office treatment than planned. The original statistical analysis plan stipulated a minimal assumption randomization-based extended Mantel-Haenszel (EMH) trend test of the high frequency, low frequency, and zero frequency treatment groups and a binary outcome. Thus a dose-weighted adjusted EMH (DWAEMH) test was developed with an extra set of weights corresponding to the number of active doses actually available, in the spirit of a pattern mixture model. The method can easily be implemented using standard statistical software. A set of Monte Carlo simulations using a logistic model was undertaken with (and without) actual dose-response effects through 1000 replicates for empirical power estimates (and 2100 for empirical size). Results showed size was maintained and power was improved for DWAEMH versus EMH and logistic regression Wald tests in the presence of a dose effect and treatment by dose interaction