All Items 2 Collection 1 The Octagon 2 Contributor 6 Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College) 2 Bell, Melanie L. (Psycho-Oncology Co-operative Research Group, University of Sydney) 1 Fairclough, Diane Lynn (Department of Preventive Medicine and Biometry, University of Colorado at Denver) 1 Kenward, Michael G., 1956- (Department of Medical Statistics, London School of Hygiene and Tropical Medicine) 1 Phipps, Polly (Office of Survey Methods Research, Bureau of Labor Statistics) 1 Toth, Daniell (Office of Survey Methods Research, Bureau of Labor Statistics) 1 show more 1 show fewer Topic 2 Research--Statistical methods 2 Statistics 2 Part Of 1 The Amherst College Octagon 2 Genre 1 Articles 2 Subject 2 Research--Statistical methods 2 Statistics 2 Adjusting models of ordered multinomial outcomes for nonignorable nonresponse in the occupational employment statistics survey Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College) An establishment’s average wage, computed from administrative wage data, has been found to be related to occupational wages. These occupational wages are a primary outcome variable for the Bureau of Labor Statistics Occupational Employment Statistics survey. Motivated by the fact that nonresponse in this survey is associated with average wage even after accounting for other establishment characteristics, we propose a method that uses the administrative data for imputing missing occupational wage values due to nonresponse. This imputation is complicated by the structure of the data. Since occupational wage data is collected in the form of counts of employees in predefined wage ranges for each occupation, weighting approaches to deal with nonresponse do not adequately adjust the estimates for certain domains of estimation. To preserve the current data structure, we propose a method to impute each missing establishment’s wage interval count data as an ordered multinomial random variable using a separate survival model for each occupation. Each model incorporates known auxiliary information for each establishment associated with the distribution of the occupational wage data, including geographic and industry characteristics. This flexible model allows the baseline hazard to vary by occupation while allowing predictors to adjust the probabilities of an employee’s salary falling within the specified ranges. An empirical study and simulation results suggest that the method imputes missing OES wages that are associated with the average wage of the establishment in a way that more closely resembles the observed association. Adjusting models of ordered multinomial outcomes for nonignorable nonresponse in the occupational employment statistics survey Differential dropout and bias in randomised controlled trials: when it matters and when it may not Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College) Dropout in randomised controlled trials is common and threatens the validity of results, as completers may differ from people who drop out. Differing dropout rates between treatment arms is sometimes called differential dropout or attrition. Although differential dropout can bias results, it does not always do so. Similarly, equal dropout may or may not lead to biased results. Depending on the type of missingness and the analysis used, one can get a biased estimate of the treatment effect with equal dropout rates and an unbiased estimate with unequal dropout rates. We reinforce this point with data from a randomised controlled trial in patients with renal cancer and a simulation study. Differential dropout and bias in randomised controlled trials: when it matters and when it may not
