Impact of Non-Weighting in the Analysis of Data Obtained from Complex Samples

Denise De Fátima Barros Cavalcante,Gláucia Maria Bovi Ambrosano,Marcelo De Castro Meneghim,Yuri Wanderley Cavalcanti,Antonio Carlos Pereira,Elza Cristina Farias De Araújo

doi:10.1590/pboci.2021.101

Abstract

Objective: To compare the estimates obtained, considering or not the weighting data. Material and Methods: Secondary data from the Oral Health Survey of the State of São Paulo (SBSP2015) was used for calculation of mean estimates, standard errors of the mean and confidence intervals (CI) for the DMFT index and components (decayed, lost and filled), in the age group of 35-44 years. Multiple logistic regression models were estimated, considering or not the weighting from the sampling plan (p<0.05). Results: It was observed that the estimates of the DMFT index and the carious component did not vary much when the design was considered or not (1.1% and 2.0%, respectively). However, the data referring to the lost and filled component showed greater differences between the values of the means. The averages fluctuated up and down by up to 6.7% for weighted versus unweighted analyses. The standard error was underestimated in the unweighted analysis and the confidence interval showed variations. Differences between the regression models obtained by the weighted and unweighted analysis of the data were detected. Conclusion: Although weighted and unweighted models presented differences of less than 10% in estimates of the mean, confidence intervals, as well as statistical inferences, were different. Thus, weighting should be applied in the population base data analysis collected by sampling with complex designs.

Highlights

Population-based epidemiological studies require a design that allows the characteristics of the studied population to be analyzed, using a representative sample plan of the population, where each element of the population has a known and non-zero probability of being selected
It was observed that the estimates of the DMFT index and the carious component did not vary much when the design was considered or not (1.1% and 2.0%, respectively)
It is noted that the standard error was underestimated when weighting was not considered

Summary

Introduction

Population-based epidemiological studies require a design that allows the characteristics of the studied population to be analyzed, using a representative sample plan of the population, where each element of the population has a known and non-zero probability of being selected. One of the ways to guarantee the representativeness of the data in the population is the randomization by probabilistic sampling [1,2]. Studies by probabilistic sampling allow the researcher to observe the representation of the phenomenon studied in the population and calculate confidence intervals and statistical significance. The last one, which guides our research, is used in large-scale population surveys, where each sampling unit is a group of elements [3]. A major challenge for population-based surveys is the analysis of data collected by complex sampling (a combination of several probabilistic sampling methods for selecting a sample). Using a combined method of sampling allows the researcher to visit compact areas instead of dispersed areas individually, reducing time and expenses [4]

Methods

Results

Discussion

Conclusion