Handling nonresponse in surveys: analytic corrections compared with converting nonresponders.

A large health survey was combined with a simulation study to contrast the reduction in bias achieved by double sampling versus two weighting methods based on propensity scores. The survey used a census of one New York county and double sampling in six others. Propensity scores were modeled as a logistic function of demographic variables and were used in conjunction with a random uniform variate to simulate response in the census. These data were used to estimate the prevalence of chronic disease in a population whose parameters were defined as values from the census. Significant (p < 0.0001) predictors in the logistic function included multiple (vs. single) occupancy (odds ratio (OR) = 1.3), bank card ownership (OR = 2.1), gender (OR = 1.5), home ownership (OR = 1.3), head of household's age (OR = 1.4), and income >$18,000 (OR = 0.8). The model likelihood ratio chi-square was significant (p < 0.0001), with the area under the receiver operating characteristic curve = 0.59. Double-sampling estimates were marginally closer to population values than those from either weighting method. However, the variance was also greater (p < 0.01). The reduction in bias for point estimation from double sampling may be more than offset by the increased variance associated with this method.