Incorporating cost in Bayesian Variable Selection, with application to cost-effective measurement of quality of health care.

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1 Incorporating cost in Bayesian Variable Selection, with application to cost-effective measurement of quality of health care University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 2 Synopsis Dimitris Fouskakis, Department of Mathematics, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Athens, Greece; Joint work with: Ioannis Ntzoufras & David Draper Department of Statistics Department of Applied Mathematics and Statistics Athens University of Economics and Business University of California Athens, Greece; Santa Cruz, USA; 1. Motivation - Indirect Measurement of Quality of Health Care. 2. Model Specification. 3. Cost - Benefit Analysis. 4. Cost - Restriction - Benefit Analysis. 5. Discussion. Presentation is available at: fouskakis/conferences/bms/bms.pdf. University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 3 1 Motivation - Indirect Measurement of Quality of Health Care How to measure hospital quality of care? Indirect method: input-output approach hospital outcomes (e.g., mortality within 30 days of admission) compared after adjusting for differences in inputs (sickness at admission). Patient sickness at admission is traditionally assessed by using logistic regression of mortality within 30 days of admission on a fairly large number of sickness indicators (on the order of 100) to construct a sickness scale. Benefit - Only Analysis : Classical variable selection techniques can be employed to find an optimal subset of indicators. In a major U.S. study constructed by RAND Corporation, such approach was used to reduced the initial list of p = 83 sickness indicators gathered on n =2, 532 pneumonia patients down to a core of 14 predictors (Keeler, et al., 1990). University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 4 The 14-Variable Rand Pneumonia Scale The RAND admission sickness scale for pneumonia (p = 14 variables), with the marginal data collection costs per patient for each variable (in minutes of abstraction time). Variable Cost Variable Cost (Minutes) (Minutes) Blood Urea Nitrogen 1.50 Age 0.50 Systolic Blood Pressure 0.50 Chest X-ray Congestive 2.50 Score (2-point scale) Heart Failure Score (3-point scale) Total APACHE II Score APACHE II Coma Score 2.50 (36-point scale) (3-point scale) Serum Albumin 1.50 Shortness of Breath 1.00 (3-point scale) Day 1 Respiratory Distress 1.00 Septic Complications 3.00 Prior Respiratory Failure 2.00 Recently Hospitalized 2.00 Ambulatory Score 2.50 Initial Temperature 0.50 (3-point scale)

2 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 5 2 Model Specification Logistic regression model with Y i = 1 if patient i dies after 30 days of admission. X ij : j sickness predictor variable for the i patient. m γ =(γ 1,...,γ p ) T. γ j : Binary indicators of the inclusion of the variable X j in the model. Model space M = {0, 1} p ; p = total number of variables considered. Hence the model formulation can be summarized as indep (Y i γ) Bernoulli(p i (γ)), ( ) pi (γ) η i (γ) = log = β j γ j X ij, 1 p i (γ) j=0 η(γ) = X diag(γ) β = Xγ βγ. University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 6 Two different approaches The RAND Benefit - Only approach is sub-optimal: it does not consider differences in cost of data collection among available predictors. We propose a Cost - Benefit Analysis, in which variables are chosen only when they predict well enough given how much they cost to collect. In problems such as this, in which there are two desirable criteria that compete, and over which a joint optimization must be achieved, there are two main ways to proceed: Both criteria can be placed on a common scale, and optimization can occur on that scale (strategy (a)). One criterion can be optimized, subject to a bound on the other (strategy (b)). University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 7 Three methods for solving this problem (1) (strategy (a)) Draper and Fouskakis (2000) and Fouskakis and Draper (2002, 2008) proposed an approach to this problem based on Bayesian Decision Theory. They used stochastic optimization methods to find (near-) optimal subsets of predictor variables that maximize an expected utility function which trades off data collection cost against predictive accuracy. (2) (strategy (a)) In this work, as an alternative to (1), we propose a prior distribution that accounts for the cost of each variable and results in a set of posterior model probabilities which correspond to a Generalized Cost-Adjusted version of the Bayesian Information Criterion (Fouskakis, Ntzoufras and Draper, 2007a). (3) (strategy (b)) We also implement a Cost - Restriction - Benefit Analysis, where the search is conducted only among models whose cost does not exceed a budgetary restriction (Fouskakis, Ntzoufras and Draper, 2007b), by the usage of a Population - Based Trans - Dimensional RJMCMC Method. Here we present results from methods (2) (Cost - Benefit Analysis) and (3) (Cost - Restriction - Benefit Analysis). University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 8 3 Cost-Benefit Analysis The aim is to identify well fitted models after taking into account the cost of each variable. Therefore we need to estimate the posterior model probability f(γ) f(y βγ, γ)f(βγ γ)dβγ f(γ y) = f(γ ) f(y βγ, γ )f(βγ γ γ )dβγ {0,1} p after introducing a prior on model space f(γ) depending on the cost. Prior on Model Parameters ( ( ) ) 1 f(βγ γ) =Normal 0, 4n X T γxγ Low Information Prior, since it gives weight to the prior equal to one data-point (see Ntzoufras, Delaportas and Forster, 2003).

3 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 9 A Cost-penalized Prior on Model Space University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 10 Approximations of the Posterior Model Odds ( γj f(γ j ) exp 2 c 0 c j c 0 ) log n for j =1,...,p. When comparing models γ (k) and γ (l) penalty imposed to the log-likelihood ratio is given by 2 log f(γ(k) ) f(γ (l) ) = ( γ (k) j c j : cost per observation for X j variable. ) γ (l) cj ) j log n c (dγ d (k) γ log n. (l) 0 c 0 : baseline cost (default choice: c 0 = min{c j } j =1,...,p). Indifference concerning the cost c j = c 0 for j =1,...,p uniform prior on model space (f(γ) 1) Posterior model odds = Bayes factor. Using Laplace approximation in our model formulation we end up 2 log f(γ y) = 2 log f(y βγ, γ)+φ(γ) } prior model prob. { }} { 2 log f(γ) +O(n 1 ). {{ } Penalty Term with φ(γ) = β γ : posterior mode of f(β γ y, γ), dγ = p γj is the dimension of the model γ, 1 4n β T γx T γxγ βγ Ψ 1 γ + dγ log(4n) + log X T γxγ } {{ } can be thought a measure of discrepancy between the data and the prior information of the model parameters Ψγ is minus the inverse of the Hessian matrix of h(βγ ) = log f(y βγ, γ) + log f(βγ γ) evaluated at the posterior mode βγ.. University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 11 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 12 Penalty Interpretation: A generalized cost-adjusted BIC Implementation and Results 2 log f(γ y) = 2 log f(y ˆβγ)+ = 2 log f(y ˆβγ)+ C γ c 0 Cγ = p γ jc j, the cost of model γ. ˆβγ = MLE of the parameters βγ of model γ. If c j = c 0 for all j BIC = 2 log f(y ˆβγ)+dγ log n. γ j c j c 0 log n + O(1) log n + O(1). Run RJMCMC (Green, 1995) for 100K iterations in the full model space. Eliminate non-important variables (with marginal probabilities < 0.30) forming a new reduced model space. Run RJMCMC for 100K iterations in the reduced model space to estimate posterior model odds and best models. Two setups: 1. Benefit only analysis (uniform prior on model space). 2. Cost - Benefit Analysis (cost penalized prior on model space).

4 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 13 Preliminary Results: Marginal Probabilities f(γ j =1 y) University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 14 Reduced Model Space: Posterior Model Probabilities/Odds Variable Benefit Cost-Benefit Index Name Cost Analysis Analysis 1 Systolic Blood Pressure (SBP) Score Age Blood Urea Nitrogen Apache II Coma Score Shortness of Breath Day Septic Complications Initial Temperature Heart Rate Day Chest Pain Day Cardiomegaly Score Hematologic History Score Apache Respiratory Rate Score Admission SBP Respiratory Rate Day Confusion Day Apache ph Score Morbid + Comorbid Score Musculoskeletal Score Common variables in both analyses: X 1 + X 2 + X 3 + X 5 + X 12 + X 70 Benefit-Only Analysis Common Variables Additional Model Posterior k Within Each Analysis Variables Cost Probabilities PO 1k 1 X 4 + X 15 + X 37 + X 73 +X 8 +X 27 +X X 8 +X X X 27 +X Cost-Benefit Analysis Common Variables Additional Model Posterior k Within Each Analysis Variables Cost Probabilities PO 1k 1 X 46 + X 51 +X 49 +X X 14 +X 49 +X X 13 +X 49 +X X 13 +X 14 +X 49 +X X 14 +X X X 37 +X X 13 +X 14 +X X above 3%. posterior odds of the best model within each analysis versus the current model k. University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 15 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 16 Reduced Model Space: Comparisons Comparison of measures of fit, cost and dimensionality between the best models in the reduced model space of the benefit-only and cost-benefit analysis; percentage difference is in relation to benefit-only. Analysis Difference Benefit-Only Cost-Benefit (%) Minimum Deviance Median Deviance Cost Dimension Cost Restriction - Benefit Analysis Implement a Cost - Restriction - Benefit Analysis, in which the practical relevance of the selected variable subsets is ensured by enforcing an overall limit on the total data collection cost of each subset: the search is conducted only among models whose cost does not exceed this budgetary restriction C. Therefore, we should a-priori exclude models γ with total cost larger than C, resulting to a significantly reduced model space, M = {γ {0, 1} p : c i γ i C}. AIM: Estimate posterior model probabilities in the cost restricted model space. i=1

5 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 17 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 18 PROBLEM: Due to the cost limit, model space areas of local maximum exist. Thus, we need to change the definition of the neighborhood structure of the proposed models and construct more advanced proposed jumps possibly between models of the same cost in order to avoid getting trapped into local maxima. SOLUTION: Intelligent trans-dimension MCMC methods that allow to move across areas of local maximum even if these are distinct. Proposed Algorithm We have developed a Population Based Trans-Dimensional Reversible-Jump Markov Chain Monte Carlo algorithm (Population RJMCMC), combining ideas from the Population-Based MCMC (Jasra, Stephens and Holmes, 2007) and Simulated Tempering (Geyer and Thompson, 1995) algorithms. Population RJMCMC Use 3 chains: The actual one, plus two auxiliary ones. In the auxiliary chains the posterior distributions are raised in a power t k (temperature), k =1, 2. 1st auxiliary chain: t 1 > 1 increasing differences between the posterior probabilities (makes the distribution steeper allowing by this way the MCMC to move closer to locally best models). 2nd auxiliary chain: 0<t 2 < 1 reducing differences between the posterior probabilities (makes the distribution flatter allowing by this way the MCMC to move easily across different models). Temperatures t k change stochastically. By this way the extensive number of chains is avoided. University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 19 The incorporation of stochastic temperatures can be done using pseudo priors g k (t k ). In this case the posterior distribution will be expanded to { f(β, γ, β (k), γ (k),t 1,t 2 y) f(y β, γ)f(β γ)f(γ) } 2 k=1 { f(y β (k), γ (k) )f(β (k) γ (k) )f(γ (k) )} tk g k (t k ), where γ (k) and β (k) are the model indicator and parameter vector of chain k. Model indicators and parameters can be updated using RJMCMC steps, while the temperature t k can be generated from the conditional posterior distribution f(t k β, γ, β (k), γ (k),t \k, y) { f(y β (k), γ (k) )f(β (k) γ (k) )f(γ (k) ) } t k g k (t k ). University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 20 Since g k (t k ) are pseudo-priors, we can set g k (t k ) h k(t k ) Z k (y,t k ) where h k (t k ) are convenient and easy to simulate from density functions resulting to For the selection of h k (t k ) we propose to use f(t k y) =h k (t k ). h 1(t 1)=Gamma(t 1 1; a 2,b 2) and h 2(t 2)=Beta(t 2; a 1,b 1). Prior Distributions The desired posterior marginal distribution for the temperatures t k is given by ( f(t k y) f(y tk, β (k), γ (k) )f(β (k) γ (k) )f(γ (k) ) ) t k g k (t k )dβ (k) γ (k) M β (k) Z k (y,t k )g k (t k ), where Z k (y,t k ) is the marginal likelihood over all possible models for chain k. Same prior on model parameters as in the Cost - Benefit Analysis and a uniform prior on cost restricted model space, i.e. f(γ) I(γ M: c(γ) = γ jc j C), where c j is the differential cost per observation for variable X j and C is the budgetary restriction.

6 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 21 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 22 Implementation and Results COST LIMIT: C = 10 minutes of abstraction time. Run Population RJMCMC for 100K iterations in the full model space, twice, starting each time from a different model. Eliminate non-important variables (with marginal probabilities < 0.30 in both runs) forming a new reduced model space. Run population RJMCMC in the reduced space, twice. Compare results and performance of population RJMCMC with simple RJMCMC. Preliminary Results: Marginal Probabilities f(γ j =1 y) Variables with marginal posterior probabilities f(γ j =1 y) above 0.30 in at least one run. Marginal Posterior Probabilities Variable First Run Second Run Index Name Cost Analysis Analysis 1 Systolic Blood Pressure (SBP) Score Age Blood Urea Nitrogen Apache II Coma Score Shortness of Breath Day Serum Albumin Initial Temperature Apache Respiratory Rate Score Admission SBP Respiratory Rate Day Confusion Day Body System Count Apache ph Score University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 23 Reduced Model Space: Posterior Model Probabilities/Odds University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 24 Reduced Model Space: Monte Carlo Errors Common variables in both analyses: X 2 + X 4 Population RJMCMC - 500K iterations 1st Run 2nd Run Common Additional Posterior Posterior k m Variables Variables Prob. PO 1k Prob. PO 1k 1 m 1 X 1 + X 12 + X 37 +X 3 +X 5 +X m 2 +X 5 +X 46 +X 62 +X m 3 +X 3 +X 62 +X m 4 +X 3 +X 5 +X 6 +X Simple RJMCMC - 500K iterations 1st Run 2nd Run Common Additional Posterior Posterior k m Variables Variables Prob. PO 1k Prob. PO 1k 1 m 1 X 62 +X 1 +X 3 +X 5 +X 12 +X m 3 +X 1 +X 3 +X 12 +X 37 +X m 2 +X 1 +X 5 +X 12 +X 37 +X 46 +X m 5 +X 3 +X 5 +X 46 +X 49 +X < 0.03 > m 6 +X 1 +X 3 +X 5 +X 49 +X < 0.03 > 19.9 posterior odds of the best model within each analysis versus the current model k. All models appearing in the table have total cost 10 min (cost limit). Monte Carlo Errors (%) RJMCMC Type Run Iterations m 1 m 2 m 3 m 4 POP K POP K POP K POP K POP K POP K SIMPLE 1 500K SIMPLE 2 500K Relative Comparisons SIMPLE vs. POP. 500K (First Run) 200K K SIMPLE vs. POP. 500K (Second Run) 200K K

7 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 25 University of Florida 10th Annual Winter Workshop: Bayesian Model Selection and Objective Methods 26 References 5 Discussion Cost - Benefit Analysis: The resulting models achieve dramatic gains in cost and noticeable improvement in model simplicity at the price of a small loss in predictive accuracy, when compared to the results of a more traditional benefit-only analysis. Cost - Restriction - Benefit Analysis: Population RJMCMC algorithm explores the model space efficiently and converges faster than simple RJMCMC (having lower Monte Carlo errors). Draper D, Fouskakis D (2000). A case study of stochastic optimization in health policy: problem formulation and preliminary results. Journal of Global Optimization, 18, Fouskakis D, Draper D (2002). Stochastic optimization: a review. International Statistical Review, 70, Fouskakis D, Draper D (2008). Comparing stochastic optimization methods for variable selection in binary outcome prediction, with application to health policy. Journal of the American Statistical Association, 103, forthcoming. Fouskakis D, Ntzoufras I, Draper D (2007a). Bayesian variable selection using cost-adjusted BIC, with application to cost-effective measurement of quality of health care. (submitted). Fouskakis D, Ntzoufras I, Draper D (2007b). Population Based Reversible Jump MCMC for Bayesian Variable Selection and Evaluation Under Cost Limit Restrictions. (submitted). Geyer CJ, Thomson EA (1995). Annealing Markov Chain Monte Carlo with applications to ancestral inference. Journal of the American Statistical Association, 90, Green P (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, Jasra A, Stephens DA, Holmes CC (2007). Population-based reversible jump MCMC. Biometrika. forthcoming. Keeler E, Kahn K, Draper D, Sherwood M, Rubenstein L, Reinisch E, Kosecoff J, Brook R (1990). Changes in sickness at admission following the introduction of the Prospective Payment System. Journal of the American Medical Association, 264, Ntzoufras I, Dellaportas P, Forster JJ (2003). Bayesian variable and link determination for generalized linear models. Journal of Statistical Planning and Inference, 111,

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