Adam Steventon: How can predictive risk models help?

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Adam Steventon: How can predictive risk models help?

  1. 1. © Nuffield Trust Evaluation methods – where can predictive risk models help? Adam Steventon Nuffield Trust 8 July 2013
  2. 2. © Nuffield Trust The problem with observational studies Eligible patients All patients Intervention patients n 54,990 556 % aged 85+ 21.6 46.2 Prior emergency admissions 0.5 1.4 Number chronic conditions 1.0 1.1 Predictive risk score 22.3 33.6 Intervention patients Source: Steventon et al (2012)
  3. 3. © Nuffield Trust Solutions, 1) before-after study
  4. 4. © Nuffield Trust Solutions, 2) regression adjustment Y = f(age, number of chronic conditions, prior emergency admissions, intervention status)
  5. 5. © Nuffield Trust Eligible patients Intervention patients Matched controls All patients Matched controls Intervention patients n 54,990 556 556 % aged 85+ 21.6 46.2 46.2 Prior emergency admissions 0.5 1.4 1.4 Number chronic conditions 1.0 1.3 1.1 Predictive risk score 22.3 33.5 33.6 Solutions, 3) Matched controls Source: Steventon et al (2012)
  6. 6. © Nuffield Trust 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)
  7. 7. © Nuffield Trust 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?
  8. 8. © Nuffield Trust Overcoming regression to the mean using a control group Start of intervention
  9. 9. © Nuffield Trust Overcoming regression to the mean using a control group Start of intervention
  10. 10. © Nuffield Trust Overcoming regression to the mean using a control group Start of intervention
  11. 11. © Nuffield Trust Overcoming regression to the mean using a control group Start of intervention
  12. 12. © Nuffield Trust Solutions, 4) regression discontinuity Winningthenextelection Fraction of votes awarded to Democrats in the previous election Source: Lee and Lemieux (2009)
  13. 13. © Nuffield Trust What is being done at the moment? Telehealth studies in Pubmed, 2006-2012 Descriptive Before after Dose response Controlled All Number of studies 3 24 1 16 44 Median number of patients in telemonitored group (range) 45 (40 to 851) 35 (7 to 17,025) 246 102 (19 to 1,767)* 45 (7 to 17,025)* Endpoints Mortality 2 - 1 3 6 Hospital use (or costs) 3 6 - 12 21 Clinical (e.g. HbA1c) - 17 - 4 21 Patient reported outcomes (e.g. quality of life) 1 10 - 3 14 Source: Steventon, Krief and Grieve (work in progress)
  14. 14. © Nuffield Trust 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.
  15. 15. © Nuffield Trust8 July 2013 www.nuffieldtrust.org.uk Sign-up for our newsletter: www.nuffieldtrust.org.uk/newsletter Follow us on Twitter: http://twitter.com/NuffieldTrust © Nuffield Trust adam.steventon@nuffieldtrust.org.uk

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