Do Trauma Centers Save Lives?

1. Do Trauma Centers Save Lives? A Statistical Solution Daniel O. Scharfstein

2. Collaborators • Brian Egleston • Ciprian Crainiceanu • Zhiqiang Tan • Tom Louis

3. Issues • Outcome Dependent Sampling • Missing Data • Confounding • Direct Adjustment • Propensity Score Weighting • Propensity Model Selection • Weight Trimming • Clustering

4. Big Picture Counterfactual Population: Y(1),X Counterfactual Population: Y(0),X Counterfactual Sample Counterfactual Sample Population: (Y,X,T) Sample Sub-Sample

5. Population: (Y,X,T) Sample Sub-Sample

11. Sample Weights • Reciprocal of the conditional probability of being included in the sub-sample given • ISS • AIS • Age • Dead/Alive at Sample Ascertainment • Dead/Alive at 3 Months post injury • Weights depend on outcome - they can’t be ignored.

13. Missing Data

14. Multiple Imputation • For proper MI, we fill in the missing data by randomly drawing from the posterior predictive distribution of the missing data given the observed data. • To reflect the uncertainty in these imputed values, we create multiple imputed datasets. • An estimate (and variance) of the effect of trauma center is computed for each completed data. • The results are combined to obtain an overall estimate. • The overall variance is the sum of the within imputation variance and the between imputation variance.

15. Multiple Imputation • To draw from the posterior predictive distribution, a model for the joint distribution of the variables and a prior distribution on the model parameters must be specified. • Joe Schafer’s software • UM’s ISR software - IVEWARE • Specifies a sequence of full conditionals, which is not, generally, compatible with a joint distribution. • WINBUGS - Crainiceanu and Egleston • Specifies a sequence of conditional models, which is compatible with a joint distribution

16. Selection Bias

17. Selection Bias

18. Selection Bias

19. Notation • T denotes treatment received (0/1) • X denotes measured covariates • Y(1) denotes the outcome a subject would have under trauma care. • Y(0) denotes the outcome a subject would have under non-trauma care. • Only one of these is observed, namely Y=Y(T), the outcome of the subject under the care actually received. • Observed Data: (Y,T,X)

20. Causal Estimand

21. Selection Bias • We worked with scientific experts to define all possible “pre-treatment” variables which are associated with treatment and mortality. • We had extensive discussions about unmeasured confounders. • Within levels of the measured variables, we assumed that treatment was randomized. • T is independent of {Y(0),Y(1)} given X

22. Example (Hernan et al., 2000)

23. Direct Adjustment

24. Direct Adjustment

25. Direct Adjustment

26. Direct Adjustment

27. Direct Adjustment

28. Direct Adjustment

29. Counterfactual Population: Y(1),X Counterfactual Population: Y(0),X Counterfactual Sample Counterfactual Sample Population: (Y,X,T) Sample Sub-Sample

30. Propensity Score Weighting

32. Y(1) Counterfactual Population

33. Y(0) Counterfactual Population

34. Why does this work?

35. Propensity Model Selection • Select a propensity score model such that the distribution of X is comparable in the two counterfactual populations (Tan, 2004).

36. Weight Trimming • The propensity score weighted estimator can be sensitive to individuals with large PS weights. • When the weights are highly skewed, the variance of the estimator can be large. • We trim the weights to minimize MSE.

38. Clustering • Assumed a working independence correlation structure. • Fixed up standard errors using the sandwich variance technique.

39. Results Counterfactual Populations Sample

40. Results Counterfactual Populations Sample

41. Results Counterfactual Populations Sample

42. Results

43. 15 10 5 0 TCs In 30 days 90 days 365 days NTCs Hospital Case Fatality RatiosAdjusted for Differences in Casemix Adjusted Relative Risk: .60.75.95

44. Results MAXAIS <=3

45. Results MAXAIS = 4

46. Results MAXAIS = 5,6

47. Relative Risks by Age and Severity

48. Potential Lives Saved Nationwide H-CUP Hospital Discharge Data 360,293adults who meet NSCOT inclusion criteria 45% Treated in NTCs 162,132 22,374 Deaths If Treated in NTCs 16,862 Deaths If Treated in TCs 5,512 Each Year

49. Conservative Estimate • Study non-trauma centers were limited to those treating at least 25 major trauma patients each year; most non-trauma centers are smaller • 17 of the study non-trauma centers had a designated trauma team and 8 had a trauma director

50. Conclusions . . . to date • The results demonstrate the benefits of trauma center care and argue strongly for continued efforts at regionalization • At the same time, they highlight the difficulty in improving outcomes for the geriatric trauma patient