{
  "abstract": "Background Researchers often use composite variables (e.g., BMI and change scores) to assess body weight outcomes. By combining multiple variables (e.g., height and weight or follow-up weight and baseline weight) into a single variable it becomes challenging to untangle the causal roles of each component variable. Composite variable bias – an issue previously identified for exposure variables that may yield misleading causal inferences – is illustrated as a similar concern for composite outcomes. We explain how this occurs for composite weight outcomes: BMI, ‘weight change’, their combination ‘BMI change’, and variations involving relative change.Methods Data from the National Child Development Study (NCDS) cohort surveys ( n = 9223) were analysed to estimate the causal effect of ethnicity, sex, economic status, malaise score, and baseline height/weight at age 23 on weight-related outcomes at age 33. The analyses were informed by a directed acyclic graph (DAG) to demonstrate the extent of composite variable bias for various weight outcomes.Results Estimated causal effects differed across different weight outcomes. The analyses of follow-up BMI, ‘weight change’, ‘BMI change’, or relative change in body size yielded results that could lead to potentially different inferences for an intervention.Conclusion This is the first study to illustrate that causal estimates on composite weight outcomes vary, potentially leading to misleading inferences and misguided decisions by health practitioners and policymakers. Methodological flaws therefore often compromise the reliability of studies that seek causal insights due to their use of either composite body weight measures or change scores in body size. It is recommended that only follow-up weight be analysed while conditioning on baseline weight for meaningful estimates. How conditioning on baseline weight is implemented depends on whether baseline weight precedes or follows the exposure of interest. For the former, conditioning on baseline weight may be achieved by inclusion in the regression model or via a propensity score. For the latter, the alternative strategy of causal mediation analysis is necessary to model the joint effects of exposure and baseline weight – the choice of strategy is best informed by a DAG.",
  "authors": [
    {
      "affiliations": [
        "The Alan Turing Institute, The Alan Turing Institute, London, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK",
        "School of Geography, University of Leeds, Leeds, UK"
      ],
      "name": "Ridda Ali"
    },
    {
      "affiliations": [
        "School of Psychology, University of Leeds, Leeds, UK"
      ],
      "name": "Andrew Prestwich"
    },
    {
      "affiliations": [
        "The Alan Turing Institute, The Alan Turing Institute, London, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK",
        "School of Geography, University of Leeds, Leeds, UK"
      ],
      "name": "Jiaqi Ge"
    },
    {
      "affiliations": [
        "Obesity Institute, Leeds Beckett University, Leeds, UK"
      ],
      "name": "Claire Griffiths"
    },
    {
      "affiliations": [
        "The Alan Turing Institute, The Alan Turing Institute, London, UK",
        "Alliance Manchester Business School, The University of Manchester, Manchester, UK"
      ],
      "name": "Richard Allmendinger"
    },
    {
      "affiliations": [
        "Alliance Manchester Business School, The University of Manchester, Manchester, UK",
        "Liverpool Business School, Liverpool John Moores University, Liverpool, UK"
      ],
      "name": "Azar Shahgholian"
    },
    {
      "affiliations": [
        "The Alan Turing Institute, The Alan Turing Institute, London, UK",
        "Alliance Manchester Business School, The University of Manchester, Manchester, UK"
      ],
      "name": "Yu-wang Chen"
    },
    {
      "affiliations": [
        "Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran"
      ],
      "name": "Mohammad Ali Mansournia"
    },
    {
      "affiliations": [
        "The Alan Turing Institute, The Alan Turing Institute, London, UK",
        "Obesity Institute, Leeds Beckett University, Leeds, UK"
      ],
      "name": "Mark S Gilthorpe"
    }
  ],
  "title": "P85 Composite variable bias: causal analysis of weight outcomes",
  "uid": "321b912c-b7fb-5ca6-9ea9-77c9dd1270c4"
}