Kate Tilling

Areas of application

  • Childhood growth
  • Lifecourse analyses.
  • Trajectories of change in disease outcomes (e.g. PSA in prostate cancer, EDS in Multiple Sclerosis).
  • Complex inter-relationships which change over time (e.g. fat mass and physical activity, risk factors for CHD)

Methodological interests

  • Methods to examine and correct for bias due to missing data.
  • Structural equation models, particularly for longitudinal data.
  • Multilevel models for growth trajectories.
  • Fractional polynomial and linear spline models.
  • Time-varying confounding - marginal structural models and g-estimation.

Example PhD topics

  • Accounting for time-varying confounding in the relationship between exposures and outcomes which change over time (e.g. physical activity and health)
  • Assessing the sensitivity to unmeasured confounding of cohort analyses
  • Quantifying associations between outcomes and exposures which vary over the lifecourse (e.g. cumulative effects of poor health on later outcomes)
  • Comparison of methods (e.g. fractional polynomials, cubic splines, penalised splines, traditional growth models, the SITAR growth model) for approximating patterns of growth/change over time. A variety of different outcomes could be considered here, depending on the applied interests of the student.
  • Disentangling the effects of growth and absolute weight on later outcomes (e.g. potentially using methods including: partial least squares methods, tracking of exposures over time, latent class methods).
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