Eliciting Patients Preferences in Kidney

Transcription

1 Eliciting Patients Preferences in Kidney Transplantation A Discrete Choice Experiment Mesfin Genie 1 Antonio Nicolò 2 Giacomo Pasini 1,3 1 Ca Foscari University of Venice 2 University of Padua 3 NETSPAR, Network for Studies on Pensions, Ageing and Retirement

2 The Problem

3 Treatment Options

4 Demand-Supply Gap

5 Why preference elicitation in kidney transplantation? In transplantation of other organs (liver, lung, and heart) there are no outside options. BUT, in kidney transplantation, dialysis could be a reasonable outside option. In this regard, preferences may play a key role for optimal matching and to maximize welfare of patients.

6 Trade-o

7 Results Preview Substantial amount of preference heterogeneity.

8 The Discrete Choice Experiment (DCE) Survey-based stated-preference elicitation technique. In DCE: 1 A list of choice sets. 2 Choice between two or more alternatives. 3 Candidates choose the preferred alternative (Just One). Repeated Choice Sets! Number of Observations? First to apply DCE to estimate patients time and risk preferences for kidney transplantation.

9 Attributes of Kidney Transplantation DCE

10 Attributes and Levels in Kidney Transplantation DCE Kidney and Pancreas Transplantation Unit, Department of Surgery and Organ Transplantation, University of Padua (Kidney Surgeons) Table: Attributes and Levels of Kidney Transplantation Attributes Waiting Time Expected Graft Survival Infectious Risk Neoplastic Risk Levels 6, 12, 36, 60 months 10, 15, 20 years Standard Augmented Standard Augmented

11 Experimental Design Full factorial design-the number of possible combinations of attributes levels-is usually very large. The full factorial design matrix has 4 X 3 X 2 X 2=48 rows [combined into (48*47)/2=1128 pairs]. This is clearly too many to present to a single candidate-cognitively demanding! Which pairs should we choose? AlgDesign package in R (Wheeler, 2006; Aizaki and Nishimura, 2008) used for the design. The algorithm searches for a list of choice sets given the criteria. D-e cient design of 16 choice sets.

12 Kidney Transplant Choice-an Example Set Table: An Example of Kidney Transplant Choice: Whichtreatmentwould you prefer? (please put an X below the chosen treatment) Treatment A Treatment B Waiting Time 36 months 6 months Graft Survival 15 years 10 years Infectious Risk Standard Augmented Neoplastic Risk Augmented Augmented Your Choice

13 Survey Instrument Data Collection: 1 Face-to-face interview conducted by trained interviewer. Response rate? 99%? 2 Collection periods April 14, 2015 to February 8, Pool of Subjects: The entire population of patients enrolled in the waiting list of the kidney transplant unit at Padua. 211 candidates [6470 observations]. Key advantage: respondents know exactly the problem! Characteristics of Candidates: Time on Dialysis and Age Density Distribution of Age Density Distribution of Time on Dialysis Age (years) kernel = epanechnikov, bandwidth = Time on Dialysis (years) kernel = epanechnikov, bandwidth =

14 Model of Kidney Transplant Choice Inherent Heterogeneity of Donor Kidneys Heterogenous Preferences. Random Utility Model (RUM) captures this heterogeneity in preferences (McFadden, 1974). Under mild regularity conditions, any discrete choice model derived from RUM has choice probabilities that can be estimated by a mixed logit model (McFadden and Train, 2000). The utility: U mst = X Õ mst m + Á mst (1) The candidate chooses the alternative with the highest utility. P mst = Prob(U mst U msj > 0) t = j (2)

15 Model of Kidney Transplant Choice Assumption: Á mst IID Extreme Value. In mixed logit model, the distribution of random coe cients is estimated with two parameters: 1 Mean (mean of m) 2 Standard deviation ( ) Unobserved preference heterogeneity.

16 Model of Kidney Transplant Choice Assumption: Á mst IID Extreme Value. In mixed logit model, the distribution of random coe cients is estimated with two parameters: 1 Mean (mean of m) 2 Standard deviation ( ) Unobserved preference heterogeneity. The predictable component of the overall utility: V mst = 1,m Time mst + 2,m Survival mst + 3,m Infectious mst + 4,m Neoplastic mst (3) Including waiting time attribute an estimate of WTW: WTW m = 1 k,m 2 1,m Individual WTW (4)

17 Model Results Table: Mixed Logit Models with Normally Distributed Coe Estimates cients and WTW (1) (2) (3) (4) (5) (6) (Mean) (SD) (WTW) (Mean) (SD) (WTW) M1 M1 M1 M2 M2 M2 Waiting Time úúú úúú úúú ( ) ( ) ( ) Graft Survival úúú úúú úúú úúú úúú úúú (0.0332) (0.0282) (0.542) (0.0334) (0.0359) (0.518) Standard Infectious úúú úúú úúú úúú úúú úúú (0.136) (0.116) (2.518) (0.132) (0.115) (2.515) Standard Neoplastic úúú úúú úúú úúú úúú úúú (0.138) (0.128) (2.394) (0.148) (0.145) (2.710) Observations Num. Candidates Log likelihood Standard errors in parentheses ú p < 0.1, úú p < 0.05, úúú p < 0.01

18 Distribution of Individual WTW by Time on Dialysis Distribution of Individual WTW for Graft Survival by Time on Dialysis Density Shorter Time on Dialysis Longer Time on Dialysis Mean WTW willingness to wait for extra year of graft survival (in months) kernel = epanechnikov, bandwidth = Density Distribution of Individual WTW for Infectious Risk by Time on Dialysis Shorter Time on Dialysis Longer Time on Dialysis Density Distribution of Individual WTW for Neoplastic Risk by Time on Dialysis Shorter Time on Dialysis Longer Time on Dialysis willingness to wait for standard infectious risk (in months) kernel = epanechnikov, bandwidth = willingness to wait for standard neoplastic risk (in months) kernel = epanechnikov, bandwidth =

19 Stochastic Dominance: WTW by Time on Dialysis CDFs willingness to wait for an extra year of graft survival (in months) shorter time on dialysis longer time on dialysis CDFs CDFs willingness to wait for standard infectious risk (in months) willingness to wait for standard neoplastic risk (in months) shorter time on dialysis longer time on dialysis shorter time on dialysis longer time on dialysis

20 Distribution of Individual WTW by Age Distribution of Individual WTW for Graft Survival by Age Density Age Age Age Mean WTW by Age CDFs willingness to wait for extra year of graft survival (in months) kernel = epanechnikov, bandwidth = Density Distribution of Individual WTW for Infectious Risk by Age Age Age Age Density Distribution of Individual WTW for Neoplastic Risk by Age Age Age Age willingness to wait for standard infectious risk (in months) willingness to wait for standard neoplastic risk (in months) kernel = epanechnikov, bandwidth = kernel = epanechnikov, bandwidth =

21 Conclusion We elicit patients time and risk preferences in kidney transplantation using DCE. Three core lessons can be learnt from this study. 1 Evidence of substantial amount of preference heterogeneity. 2 Younger patients are willing to wait longer. 3 Patients who spent longer time on dialysis are willing to wait longer. Recognizing di erences in individual preferences in kidney allocation algorithms is expected to improve welfare of patients.

22 Implications for Medical Practice 1 Give the available kidney, regardless of its quality, to older candidates as soon as they enter on the waiting list. 2 O er the better quality kidneys to those that spent longer time on dialysis. The current algorithm is on first-come first-transplanted basis.

23 Results by Time on Dialysis Table: Mixed Logit Model Results-by Time on Dialysis (1) (2) (3) (4) (5) (6) (7) (Mean) (SD) (WTW) (Mean) (SD) (WTW) (D/ce-WTW) M1 M1 M1 M2 M2 M2 t-ratio Waiting Time úúú úúú ( ) ( ) Graft Survival úúú úúú úúú úúú úúú úúú úú (0.0438) (0.0360) (0.619) (0.0501) (0.0424) (0.964) Standard Infectious úúú úúú úúú úúú úúú úúú úú (0.191) (0.161) (2.985) (0.190) (0.163) (4.402) Standard Neoplastic úúú úúú úúú úúú úúú úúú úú (0.184) (0.175) (2.662) (0.209) (0.196) (4.549) Observations Log likelihood Standard errors in parentheses ú p < 0.1, úú p < 0.05, úúú p < 0.01 Back to Kernel Plots

24 Results by Age Table: Mixed Logit Model Results by Age (1) (2) (3) (4) (5) (6) (Mixed Logit) (WTW) (Mixed Logit) (WTW) (Mixed Logit) (WTW) Mean Waiting Time úúú úúú úúú ( ) ( ) ( ) Graft Survival úúú úúú úúú úúú úúú úúú (0.0527) (1.051) (0.0508) (0.841) (0.0763) (0.924) Standard Infectious úúú úúú úúú úúú úúú úúú (0.212) (5.235) (0.218) (4.403) (0.297) (3.588) Standard Neoplastic úúú úúú úúú úúú úúú úúú (0.215) (4.866) (0.230) (4.419) (0.295) (3.470) SD Graft Survival úúú úúú úúú (0.0440) (0.0435) (0.0646) Standard Infectious úúú úúú úúú (0.175) (0.179) (0.268) Standard Neoplastic úúú úúú úúú (0.196) (0.207) (0.288) Observations Loglikelihood Standard errors in parentheses ú p < 0.1, úú p < 0.05, úúúp < 0.01 Back to Kernel Plots by Age

25 Stochastic Dominance: Individual WTW by Age CDFs willingness to wait for an extra year of graft survival (in months) Ages Ages Ages CDFs CDFs willingness to wait for standard infectious risk (in months) willingness to wait for standard neoplastic risk (in months) Ages Ages Ages Ages Ages Ages Back to Kernel Plots by Age