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Evaluation methods – where can predictive risk models help?

Evaluation methods – where can predictive risk models help?. Adam Steventon Nuffield Trust 8 July 2013. The problem with observational studies. Intervention patients. Eligible patients. Source: Steventon et al (2012). Solutions, 1) before-after study. Solutions, 2) regression adjustment.

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Evaluation methods – where can predictive risk models help?

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  1. Evaluation methods – where can predictive risk models help? Adam Steventon Nuffield Trust 8 July 2013

  2. The problem with observational studies Intervention patients Eligible patients Source: Steventon etal (2012)

  3. Solutions, 1) before-after study

  4. Solutions, 2) regression adjustment Y = f(age, number of chronic conditions, prior emergency admissions, intervention status)

  5. Solutions, 3) Matched controls Intervention patients Matched controls Eligible patients Source: Steventon etal (2012)

  6. How to select matched controls Propensity score (Rosenbaum and Rubin 1983) -Predictive risk of receiving the intervention Prognostic score (Hansen 2008) - Predictive risk of experiencing the outcome (e.g. emergency hospitalisation), in the absence of the intervention Genetic matching (Sekhon and Grieve 2012) - computer-intensive search algorithm

  7. Advantages / disadvantages Disadvantage – only allows for observed variables But Matching as ‘data pre-processing’ – reduces dependence of estimated intervention effects on regression model specification Intuitive? Good for routine monitoring – once controls found, data can be updated

  8. Overcoming regression to the mean using a control group Start of intervention

  9. Overcoming regression to the mean using a control group Start of intervention

  10. Overcoming regression to the mean using a control group Start of intervention

  11. Overcoming regression to the mean using a control group Start of intervention

  12. Solutions, 4) regression discontinuity Winningthe next election Fraction of votes awarded to Democrats in the previous election Source: Lee and Lemieux(2009)

  13. What is being done at the moment?Telehealth studies in Pubmed, 2006-2012 Source: Steventon,Krief and Grieve (work in progress)

  14. References Lee DS, Lemieux T. Regression discontinuity designs in economics. 2009. Available from: http://www.nber.org/papers/w14723.pdf?new_window=1 Sekhon JS, Grieve RD. A matching method for improving covariate balance in cost-effectiveness analyses. Health economics 2012;21:695–714. Rosenbaum P, Rubin D. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41–55. Hansen BB. The prognostic analogue of the propensity score. Biometrika 2008;95:481–8. Steventon A, Bardsley M, Billings J, Georghiou T, Lewis GH. The role of matched controls in building an evidence base for hospital-avoidance schemes: a retrospective evaluation. Health services research 2012;47:1679–98.

  15. adam.steventon@nuffieldtrust.org.uk

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