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The Role of Statistical Science in Guiding Health Policy Dalene Stangl 1 Don Berry 2 Giovanni Parmigiani 1 1 Institute of Statistics and Decision Sciences.

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Presentation on theme: "The Role of Statistical Science in Guiding Health Policy Dalene Stangl 1 Don Berry 2 Giovanni Parmigiani 1 1 Institute of Statistics and Decision Sciences."— Presentation transcript:

1 The Role of Statistical Science in Guiding Health Policy Dalene Stangl 1 Don Berry 2 Giovanni Parmigiani 1 1 Institute of Statistics and Decision Sciences 2 MD Anderson Cancer Center

2 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. The Role of the Statistician in Policy Analysis and Research ¶Sir Claus Moser’s, 1975 ASA meeting, the “foremost responsibility (of the statistician) is to contribute to more enlightened and efficient ‘decision making’ … through the fullest possible exploitation of our skills in analyzing and interpreting the data.” ·Dorothy Price, Director, National Center for Health Statistics, (1976), The American Statistician, “The Role of Statistics in the Development of Health Care Policy” “…. As in other areas of social policy, health statisticians and health data are increasingly expected to provide keys to rational decision making. To accomplish this goal, the statistician and decision maker need to interact to an increasing degree.” ¸John Tukey, 1976, Am. J. of Epidemiology “…those statisticians for whom opportunity and a natural bent combine to offer experience and the development of expertise ought, in the public interest, become as much policy makers as their roles allow.”

3 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Quote Commonalities zAll refer to importance of decision-making zRecommend more involvement of statisticians zAll statements were made 20-25 years ago

4 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Focus of past 25 years z1970s and 1980s yDevelop coordinated, systematic data base x“At NCHS we are searching for innovations to enhance data production with minimal increased demand on resources and to provide data in a timely fashion…. We are being asked to produce more data, which is more relevant, with resources that are not growing commensurately. We are being asked to aid in the interpretation and analysis of the data as well.” z1990’s yStatistics and Policy (B.D. Spencer ed., 1997) xno mention of decision theory y“The statistical basis of public policy: a paradigm shift is overdue” (Lilford and Braunholtz, 1996) xBayesian methods superior to conventional methods. xPrimary advantages xUtility functions were given one sentence + reference

5 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. ‘Typical’ Bayesian Solution zHospital Profiling yOutcome ex. - mortality rate at time t (adjust case-mix) xClassical - Z-scores xBayesian - Posterior probabilities of ‘excess’ mortality yImplicit rather than explicit decision analysis yHow is decision-making embedded in the analysis? xchoice of outcome xtime point xrelative performance measure versus national guideline of performance xquality thresholds xposterior tail areas ySufficient?

6 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Why Insufficient? zNeeds explicit decision-theoretic framework zTwo proposals ¶relinquish automatic constant utilities embedded in p-values and posterior probabilities ·present statistical output in ways that increase the possibility and probability of applying a wide diversity of utility functions

7 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Prescriptive Perspective z“Making Health Policy Decisions: Is Human Instinct Rational? Is Rational Choice Human.” Paltiel and Stinnett, Chance, 1996 ÔApproach formal analysis from a prescriptive perspective, I.e. aim to provide decision-makers with information that can help them to make better choices but stop short of telling them what to do. Ô“By being forced to consider this issue explicitly, people may, whatever their final decision, benefit from scrutinizing and coming to grips with values to which they had previously given little thought.”

8 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Proposal 1: relinquish automatic constant utilities embedded in p-values and posterior probabilities  threshold,  ztwo decisions ¶d 0 accept - sufficient quality ·d 1 reject

9 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md.  d 1 is better than d 0 if  >   d 0 is better than d 1 if  >   U(d,  ) measures the worth/utility of d when the uncertain value is 

10 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Constant and Linear Utility

11 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Compromise Utility

12 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Loss of declaring sufficiency

13 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Uncertainty in  zCalculate expected loss   L(  )p(  )  zDeclare sufficient quality iff negative zBalances cost/benefits in a simple, comprehensive way

14 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Specifying Loss zNot the statistician’s loss function zStatistician can help decision-maker articulate value judgements in a way that allows coherent procedure

15 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Extensions zother loss functions zmultivariate outcomes - Tan and Smith (1998) zprior elicitation

16 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Proposal 2: Predictive Distributions for General Outcomes zHospital Profiling yexcessive mortality at time t as measured by 1.5 x median mortality across all hospitals ypredictive survival curves across time zAdvantages Jallow diversity of utility functions Jmetric upon which values are easily understood Jincorporate QUALYs zDisadvantages Lrequires event times Lharder to model event times than dichotomous outcomes

17 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. Other Thoughts yMeta-Analysis in Medicine and Health Policy (Stoto) yAttitudes of Policy World yGraduate Education yAttitudes of Statisticians

18 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. References Ù Berger, J.O. and Delampady, M. (1987). Testing precise hypotheses (with discussion). Statistical Science 2: 317-352. Ù Berger, J.O. and Sellke, T. (1987). Testing a point-null hypothesis: the irreconcilability of p-values and evidence ( with discussion). J. Amer. Statistica Assoc. 82:112-139. Ù Berger. R.L. and Hsu, J.C. (1996). Bioequivalence trials, intersection-union tests and equivalence confidence sets (with discussion). Statist. Sci. 11:283-319. Ù Bernardo, J.M. and Smith, A.F.M. (1994). Bayesian Theory. Wiley. Chichester. Ù Casella, G. and Berger, R.L. (1987). Reconciling Bayesian and frequentist evidence in the one-sided testing problem. J. Amer. Statist. Assoc. 82: 106-111. Ù Cochran, W.G. (1976). The role of statistics in national health policy decisions. American Journal of Epidemiology 104(4):370-379. Ù Lilford, R.J. and Thornton J.D. (1992). Decision logic in medical practice. Journal of the Royal Collegeof Physicians of London, 26(4):400-412. Ù Lilford, R.J. and Braudholtz, D. (1996). The statistical basis of public policy: a paradigm shift is overdue. British Medical Journal 313(7057):603-607. Ù Lindley, D.V. (1985) Making Decisions. Wiley, Chichester. Ù Lindley, D.V. (1997) The choice of sample size (with discussion). The Statistician 46: 129-166. Ù Lindley, D.V. (1998) Decision Analysis and Bioequivalence Trials. Statistical Science 13(2): 136-141. Ù Lindley, D.V. and Singpurwalla, N.D. (1991). On the evidence needed to reach agreed action between adversaries, with application to acceptance sampling. J. Amer. Statist. Assoc. 86: 933-937.

19 The Role of Statistical Science in Guiding Health Policy, JSM, Monday August 9, 1999, Baltimore, Md. References continued... Ù Normand, S., Glickman, M., Gatsonis, C. (1997). Statistical methods for profiling providers of medical care: issues and applications. J. Amer. Statist. Assoc. 92(439):803-814. Ù Paltiel, A.D. and Stinnett A.A. (1996). Making health policy decisions: Is human instinct rational? Is rational choice human? Chance 9(2):34-39. Ù Rice, D. (1977). The role of statistics in the development of health care policy. The American Statistician 31(3):101-106. Ù Tan S.B. and Smith, A.F.M. (1998). Exploratory thoughts on clinical trials with utilities. Statistics in Medicine 17:2771-2791. Ù Tukey, J.W. (1976). Discussion of: “Role of statistics in national health policy decisions.” American Journal of Epidemiology 104(4):380-385.


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