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Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

Louise Marston1, Janet L Peacock, Keming Yu

  • 1Department of Primary Care and Population Health, Computing and Mathematics, Brunel University, London, UK. l.marston@pcps.ucl.ac.uk

Paediatric and Perinatal Epidemiology
|June 16, 2009
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Statistical methods for analyzing data from premature infants, often featuring multiple births, must account for data clustering. Multilevel modeling is recommended for continuous outcomes, while logistic regression or multilevel modeling is advised for dichotomous outcomes, especially with small datasets.

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Area of Science:

  • Biostatistics
  • Perinatal Epidemiology
  • Statistical Modeling

Background:

  • Premature infant studies frequently involve multiple births, creating hierarchical data structures.
  • Ignoring data clustering in such studies can lead to inaccurate statistical inferences.
  • Appropriate statistical methods are crucial for reliable analysis of clustered data in perinatal research.

Purpose of the Study:

  • To compare the performance of various statistical methods for analyzing hierarchical data from prematurely born infants.
  • To evaluate generalized estimating equations, multilevel models, multiple linear regression, and logistic regression.
  • To identify the most suitable methods for continuous and dichotomous outcomes in the presence of clustered data.

Main Methods:

  • Analysis of four distinct datasets varying in size and proportion of multiple births.
  • Application of generalized estimating equations, multilevel models (ML GLS, ML MLE), multiple linear regression, and logistic regression.
  • Comparison of statistical results, including estimates and confidence intervals, across different methods and datasets.

Main Results:

  • For continuous outcomes, two-level models showed similar results in larger datasets, but ML GLS and ML MLE yielded divergent estimates in smaller datasets.
  • For dichotomous outcomes, most methods, except ML GH, produced comparable odds ratios and confidence intervals.
  • Multilevel modeling is suggested for continuous outcomes, with caution advised for ML GLS and ML MLE on small datasets.

Conclusions:

  • Multilevel modeling is recommended for analyzing continuous outcomes in studies of prematurely born infants.
  • For dichotomous outcomes with significant clustering, logistic regression with adjusted standard errors or multilevel modeling is advised.
  • Generalized least squares multilevel modeling and maximum likelihood multilevel modeling require careful application with small datasets due to potential for divergent estimates.