The Australian longitudinal study on male health sampling design and survey weighting: implications for analysis and interpretation of clustered data

BMC Public Health

Research

Dianne Currier¹  Dallas R. English¹  Lyle Gurrin¹  John B. Carlin²  Jane Pirkis³  Matthew J. Spittal³  Marnie Downes⁴  Ian Gordon⁵ 

[1] Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 3010, Melbourne, Australia;Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 3010, Melbourne, Australia;Murdoch Childrens Research Institute, Royal Children’s Hospital, 3052, Parkville, Australia;Centre for Mental Health, Melbourne School of Population and Global Health, The University of Melbourne, 3010, Melbourne, VIC, Australia;Murdoch Childrens Research Institute, Royal Children’s Hospital, 3052, Parkville, Australia;Department of Paediatrics, Melbourne Medical School, The University of Melbourne, 3010, Melbourne, Australia;Statistical Consulting Centre, School of Mathematics and Statistics, The University of Melbourne, 3010, Melbourne, Australia;

关键词: Sample Weight;    Smoking Prevalence;    Male Health;    Australian Longitudinal Study;    Unweighted Analysis;   

DOI  :  10.1186/s12889-016-3699-0

来源: Springer

【 摘 要 】

BackgroundThe Australian Longitudinal Study on Male Health (Ten to Men) used a complex sampling scheme to identify potential participants for the baseline survey. This raises important questions about when and how to adjust for the sampling design when analyzing data from the baseline survey.MethodsWe describe the sampling scheme used in Ten to Men focusing on four important elements: stratification, multi-stage sampling, clustering and sample weights. We discuss how these elements fit together when using baseline data to estimate a population parameter (e.g., population mean or prevalence) or to estimate the association between an exposure and an outcome (e.g., an odds ratio). We illustrate this with examples using a continuous outcome (weight in kilograms) and a binary outcome (smoking status).ResultsEstimates of a population mean or disease prevalence using Ten to Men baseline data are influenced by the extent to which the sampling design is addressed in an analysis. Estimates of mean weight and smoking prevalence are larger in unweighted analyses than weighted analyses (e.g., mean = 83.9 kg vs. 81.4 kg; prevalence = 18.0 % vs. 16.7 %, for unweighted and weighted analyses respectively) and the standard error of the mean is 1.03 times larger in an analysis that acknowledges the hierarchical (clustered) structure of the data compared with one that does not. For smoking prevalence, the corresponding standard error is 1.07 times larger. Measures of association (mean group differences, odds ratios) are generally similar in unweighted or weighted analyses and whether or not adjustment is made for clustering.ConclusionsThe extent to which the Ten to Men sampling design is accounted for in any analysis of the baseline data will depend on the research question. When the goals of the analysis are to estimate the prevalence of a disease or risk factor in the population or the magnitude of a population-level exposure-outcome association, our advice is to adopt an analysis that respects the sampling design.

【 授权许可】

CC BY   
© The Author(s). 2016