title: Statistical projection of clinical subsample estimates to a survey population.
abstract: BACKGROUND
  The goal of public health research often involves estimating clinical outcomes for a broad target population. The gold standard for the basis of such inference is a nationally representative survey that includes a clinical component. The cost of this gold standard can be prohibitive, and alternative approaches are needed. We propose a statistical methodology for projecting estimates from a clinical subsample to a larger survey population that has utility in periodontal research.
  
  
  METHODS
  The statistical methodology consists of fitting prediction models to clinical outcome variables that are available only for a subset of the survey population. Variables measured on the larger survey population are included in the model as predictors. The resulting prediction equations are applied to the entire survey population to produce estimates of prevalence for the clinical outcomes of interest. Methods for computing variance estimates for the model-based predictions also are proposed.
  
  
  RESULTS
  This projection methodology was developed originally for use with a nationally representative sample of the veteran population in the United States as part of the National Vietnam Veterans Readjustment Study. Details of the projection methodology and illustration of its use are provided. Issues associated with the application of this methodology to periodontal research are discussed.
  
  
  CONCLUSIONS
  The proposed projection methodology supports valid inference about clinical outcomes to a broad population using data from a more narrowly defined clinical subsample. This approach is useful in addressing important public health questions until such time as a nationally representative clinical study can be undertaken.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA. lisa_lavange@unc.edu
authors.forename: Lisa M
authors.initials: LM
authors.lastname: LaVange
authors.forename: Gary G
authors.initials: GG
authors.lastname: Koch
mesh_terms: D000465:Algorithms; D003130:Combat Disorders / Q000453:epidemiology; D004813:Epidemiology / Q000706:statistics & numerical data*; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008403:Mass Screening / Q000379:methods; D015233:Models, Statistical; D010510:Periodontal Diseases / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods; D015995:Prevalence; D011336:Probability; D011634:Public Health; D012647:Self-Assessment; D013313:Stress Disorders, Post-Traumatic / Q000453:epidemiology; D014481:United States / Q000453:epidemiology; D014728:Veterans / Q000706:statistics & numerical data