Using Resampling Techniques to Measure the Effectiveness of Providers in Workers’ Compensation Insurance David Speights Senior Research Statistician HNC Insurance Solutions Irvine, California

Presentation Outline Introduction to the problem Introduction to Bootstrap Resampling Two resampling approaches for comparing two groups Examples Conclusions

Introduction to the Problem Compare two groups from observational data –Outcome (Y) {e.g. Claim Cost} –Characteristics (X) have distributions F 1 and F 2 Difficulties –F 1  F 2 –X is associated with Y (i.e. X is a confounder) –example: claim severity associated with claim cost

Introduction to the Problem Ideal solution –Randomize subjects into the two groups –Ideal solution not usually possible Alternate solution {Topic of the paper} –Identify characteristics where F 1  F 2 –Adjust the distribution of Y to account for the differing distributions of X

Introduction to Bootstrap Resampling Purpose –Obtain the distribution of a parameter estimate (i.e. sampling distribution) –Not rely on assumptions about the underlying distribution –Often used when parameter estimate has difficult to obtain distribution relies heavily on unrealistic assumptions

Introduction to Bootstrap Resampling Given Data – {X 1, X 2, …, X n } where X i is a p x 1 vector –X has unspecified distribution F Parameter of interest  –  = T(F) is a parameter of interest We want the distribution of

Introduction to Bootstrap Resampling Distribution of –usually obtained through theoretical properties if repeated sampling is performed on a population with a known distribution of X –bootstrap techniques resample from the data to simulate repeated sampling from the population with unknown distribution of X

Introduction to Bootstrap Resampling Example -- Population Mean Example -- Population Mean Resample with replacement from data –Data is (X 1, …, X n ). –Each data point equally likely to be selected –Resampled data is (X (b) 1, …, X (b) n ). – is the b th bootstrap estimate of 

Introduction to Bootstrap Resampling Example -- Population Mean B bootstrap samples are drawn Distribution of is estimated with the empirical distribution function of Mean and variance of this distribution used to estimate mean and variance of

Two Resampling Methods for Comparing Two Groups Method 1: Normalized comparisons –Y is a response of interest –X is a category variable, confounder –Z=1 for group 1, Z=2 for group 2 –F(Y|Z=1) normalized for distribution of X in group 2 –F(Y|Z=2) non- normalized

Two Resampling Methods for Comparing Two Groups Method 1: Normalized comparisons –Resample from (Y i,X i ) seperately for groups 1 and 2 –Construct estimates of F(Y|X=x j ) and P(X=x j ) for two groups –Construct estimates of the normalized distribution functions on the previous slide –Parameter estimates can be obtained from this

Two Resampling Methods for Comparing Two Groups Method 2: Bootstrapping linear regression –Y is a response of interest –X is vector of variables, confounders –Z=1 for group 1, Z=2 for group 2 –Use the regression model

Two Resampling Methods for Comparing Two Groups Method 2: Bootstrapping linear regression –Estimate ( ,  ) with the least squares estimates on original data –Resample with replacement from the residuals –Construct the b th bootstrap value of Y as –b th bootstrap sample is

Two Resampling Methods for Comparing Two Groups Method 2: Bootstrapping linear regression –Construct estimates of ( ,  ) with the least squares estimates on bootstrap sample –Using the B bootstrap estimates of ( ,  ), construct the distribution of the parameters of interest

Examples Using Data from a Nationwide Data Base of Workers Compensation Claims Normalized comparisons of percentiles –Y= Total claim cost –Group 1: Providers in network A –Group 2: Providers not in network A –X is a 10 level variable representing claim severity derived through ICD9 code on a claim –B = 500 bootstrap sample drawn –median, 75 th, and 95 th percentiles compared –Normalization relative to group 1

Examples Using Data from a Nationwide Data Base of Workers Compensation Claims Normalized comparisons of percentiles

Examples Using Data from a Nationwide Data Base of Workers Compensation Claims Bootstrapping linear regression –Y = log(Total Indemnity Costs) –X consists of several variables NCCI body part designation, nature of injury designation, accident cause, industry class code, and injury type 10 level claim severity measure derived with ICD9 code Age and gender –Group 1: Specific provider of interest (Provider Z) –Group 2: All other providers –B=500 bootstrap samples

Examples Using Data from a Nationwide Data Base of Workers Compensation Claims

Conclusions Bootstrap methodology is a flexible robust method for deriving sampling distributions Can be used to compare two groups while considering possible confounder variables Useful method for observational studies Only a few examples shown in this paper/presentation, much more potential