title: [Estimate methods used with complex sampling designs: their application in the Cuban 2001 health survey].
abstract: OBJECTIVE
  To look at the individual features of three different methods used to estimate simple parameters--means, totals, and percentages, as well as their standard errors--and of logistic regression models, and to describe how such methods can be used for analyzing data obtained from complex samples.
  
  
  METHODS
  Data from Cuba's Second National Survey of Risk Factors and Non-Communicable Chronic Ailments [Segunda Encuesta Nacional de Factores de Riesgo y Afecciones Crónicas No Transmisibles], which was conducted in 2001, were studied. A complex, stratified multi-stage cluster sampling design was used. Cuba's 14 provinces and the municipality of Isla de la Juventud served as the strata, while the clusters consisted of sampled geographic areas (SGA), blocks, and sectors. Samples were weighted in inverse proportion to their probability of being selected, and estimates were performed by sex and age group (15-34, 35-54, 55-74, and 75 or more years). Taylor approximations were used to estimate variances. Three statistical methods were compared: conventional analysis, which assumes all data were obtained through simple random sampling; weighted analysis, which only takes into account the weight of the samples when performing estimates; and adjusted analysis, which looks at all aspects of the sampling design (namely, the disparity in the probability of being included in the sample and the effect of clustering on the data).
  
  
  RESULTS
  The point estimates obtained with the three different types of analytic methods were similar. Standard error (SE) estimates for the prevalence of overweight and of arterial hypertension that were obtained by conventional analysis were underestimated by 19.3% and by more than 11.5%, respectively, when such estimates were compared to those obtained with the other two analytic methods. On the other hand, weighted analysis generated SE values that were much smaller than those obtained with the other two types of analyses. The same pattern was noted when odds ratios were calculated using the different methods.
  
  
  CONCLUSIONS
  Analytic methods that take into account the way the data are structured as well as the study design give a more realistic picture of the problem under study and provide more exact estimates of the study parameters and their SE than conventional analytic methods. Because data from epidemiologic and public health research are often obtained through complex sampling designs, the methods described in this paper and the statistical packages that utilize them should be used more widely.
authors.affiliation: Arthritis Community Research & Evaluation Unit, Division of Outcomes & Population Health, University Health Network, Toronto, Canada. mcanizar@uhnres.utoronto.ca
authors.forename: Mayilée
authors.initials: M
authors.lastname: Cañizares Pérez
authors.forename: Isabel
authors.initials: I
authors.lastname: Barroso Utra
authors.forename: Alina
authors.initials: A
authors.lastname: Alfonso León
authors.forename: René
authors.initials: R
authors.lastname: García Roche
authors.forename: Karen
authors.initials: K
authors.lastname: Alfonso Sagué
authors.forename: Martha
authors.initials: M
authors.lastname: Chang de la Rosa
authors.forename: Mariano
authors.initials: M
authors.lastname: Bonet Gorbea
authors.forename: Esther M
authors.initials: EM
authors.lastname: León
mesh_terms: D003462:Cuba; D006306:Health Surveys; D012107:Research Design; D018401:Sample Size