Behavioral Context Patient Involvement

Transcription

1 1 Behavioral Context Patient Involvement Preliminary Findings 1,00 0,80 0,60 0,40 0,20 0,00-0,20-0,40-0,60-0,80-1,00 very easy to get somewhat easy to get somewhat difficult to get Accurate Health Information always coordinated often coordinated sometimes coordinated Care Transition always provided often provided sometimes provided Patient Education always considered sometimes considered rarely or never considered Attention to personal situation always given sometimes given rarely or never given Trust and Respect $500/year $1000/year $2000/year Out of pocket costs $3000/year Status Quo Diabetes Cancer

2 2 Behavioral Context Point of Care Preliminary Findings 1,20 1,00 0,80 0,60 0,40 0,20 0,00-0,20-0,40-0,60-0,80-1,00-1,20-1, days 7-10 days days Waiting Time very easy somewhat easy somewhat difficult Guidance within the Facility always, at any time sometimes, after asking rarely or never Patients Health Record once every month once every 6 months once every 12 months Proactive Care always together sometimes together rarely or never together Shared Decision-Making $500/year $1000/year $2000/year Out of pocket costs $3000/year Status Quo Diabetes Cancer

3 3 Behavioral Context Personnel Preliminary Findings 1,00 0,80 0,60 0,40 0,20 0,00-0,20-0,40-0,60-0,80-1,00-1,20 always friendly and helpful often friendly and helpful sometimes friendly and helpful Friendliness and Helpfulness of Staff very easy to access somewhat easy to access somewhat difficult to access Information on Performance 1 year of experience 5 years of experience 10 years of experience Experience of Care Provider always work together sometimes work together rarely or never work together Multidisciplinary Care always given often given sometimes given Attentiveness of Care Provider $500/year $1000/year $2000/year Out of pocket costs $3000/year Status Quo Diabetes Cancer

4 4 Behavioral Context Organization Preliminary Findings 1,00 0,80 0,60 0,40 0,20 0,00-0,20-0,40-0,60-0,80-1,00-1,20 20 minutes 40 minutes 60 minutes Travel Time to Care Provider very good good, fair, Medical Devices and Furnishings always based on guidlines often based on guidlines sometimes based on guidlines Treatment Guidelines All (100%) Most(75%) Half (50%) Clinical Information Exchange always assigned sometimes rarely or never assigned Case Management $500/year $1000/year $2000/year Out of pocket costs $3000/year Choice Status Quo Diabetes Diabetes Cancer Cancer

5 Content 1. Healthcare Decision Making & Public Participation: Priority Setting 2. Preference Elicitation Methods 3. Method & Study Design: Discrete Choice Experiment 4. Duke Health Care Delivery Systems & IQWiG Pilot Study: Hepatitis C 5. Discussion: Advantages & Disadvantages

6 Aim of the Pilot Study The aim of our pilot study was to investigate to what extent a DCE can be used as a method for the identification, weighting and prioritization in the case of multiple endpoints. We used the example of antiviral therapy for chronic hepatitis C (HCV), one of the most common chronic infections.

7 Identification: Attributes and Levels Hepatitis C Attribute Level 1 Level 2 Level 4 duration of treatment 12 weeks 24 weeks 48 weeks frequency of injecting interferon Once in 2 weeks time 1 times a week 3 times a week duration of flue like symptoms after injection one day after injection two days after injection tree days after injection probability of getting gastrointestinal symptoms 25 out of 100 people (25%) 35 out of 100 people (35%) 45 out of 100 people (45%) probability of getting psychiatric symptoms 35 out of 100 people (35%) 45 out of 100 people (45%) 55 out of 100 people (55%) probability of getting skin problems or Alopecia 35 out of 100 people (35%) 45 out of 100 people (45%) 55 out of 100 people (55%) probability of sustained virological response 6 month after treatment 45 out of 100 people (45%) 55 out of 100 people (55%) 65 out of 100 people (65%)

8 Example Choice Set IQWiG Pilotstudy Therapie A Therapie B duration of treatment 24 weeks 48 weeks frequence of injecting interferon Once in 2 weeks time 1 times a week duration of flue like symptoms after injection probability of getting gastrointestinal symptoms probability of getting phychiatric symptoms probability of getting skin problems or Alopecia probability of sustained virological response 6 month after treatment tree days after injection 25 out of 100 people (25%) 55 out of 100 people (55%) 55 out of 100 people (55%) 55 out of 100 people (55%) one day after injection 45 out of 100 people (45%) 45 out of 100 people (45%) 45 out of 100 people (45%) 45 out of 100 people (45%) Please choose A or B

9 Identification: Patients Attribut (1) duration of treatment (2) frequency of injecting interferon (3) duration of flue like symptoms after injection (4) probability of getting gastrointestinal symptoms (5) probability of getting psychiatric symptoms (6) probability of getting skin problems or Alopecia (7) probability of sustained virological response 6 month after treatment coeff Odds Ratio se coeff Sig 95% CI low 95% CI up 95% CI breite oneway rel. Gew. in % 0,2503 1, ,02342 < ,2044 0,2962 0, ,2966 1, ,02337 < ,2508 0,3424 0, ,1052 1, ,02323 < ,0597 0,1507 0, ,1233 1, ,02332 < ,0776 0,169 0, ,1857 1, ,02342 < ,1398 0,2317 0, ,1055 1, ,02627 < ,0599 0,1511 0, ,8041 2, ,02611 < ,7529 0,8553 0, Likelihood - ratio test of rho=0: chibar2(01) = Prob >= chibar2 = 0.000, Random- effects logistic regression, Number of obs = 5252, Log likelihood = , Prob > chi2 =

10 Identification: Patients

11 Approx. Cardinal Scale: Patients

12 Identification: Experts Attribut (1) duration of treatment (2) frequency of injecting interferon (3) duration of flue like symptoms after injection (4) probability of getting gastrointestinal symptoms (5) probability of getting psychiatric symptoms (6) probability of getting skin problems or Alopecia (7) probability of sustained virological response 6 month after treatment Coeff Odds ratio se coeff sig 95% CI low 95% CI up 95% CI breite oneway rel. Gew. in % 0,7918 2, , < ,6560 0,9277 0, ,4053 1, , ,0000 0,2948 0,5158 0, ,0786 1, , ,1610-0,0314 0,1885 0, ,1620 1, , < ,0472 0,2767 0, ,2702 1, , < ,1538 0,3867 0, ,0622 1, , ,2880-0,0525 0,1769 0, ,7362 5, , < ,5673 1,9050 0, Likelihood- ratio test of rho=0: chibar2(01) = 0.00, Prob >= chibar2 = 1.000, Random- effects logistic regression, Number of obs = 1512, LR chi2(7) = , Log likelihood = , Prob > chi2 = 0.000

13 Identification: Experts

14 Approx. Cardinal Scale: Experts

15 Expert judgment versus patient preferences

16 Endpoint based utility assessment of therapy A: PegInterferon (high dose) + Ribavirin Endpoint En EMin EMax Znm Gnm Tnm 1) duration of treatment , ) frequency of injecting interferon 3) duration of flue like symptoms after injection 4) probability of getting gastrointestinal symptoms 5) probability of getting psychiatric symptoms 6) probability of getting skin problems or Alopecia 7) probability of sustained virological response 6 month after treatment , , ,75 22,25 27, , ,33 30,75 29,75 32,50 36,36 0,1857-6,75 32,67 26,67 32, ,105-10, , ,41 Endpoint- based utility assessment therapy A 50,83

17 Endpoint based utility assessment of therapy B: PegInterferon (low dose) + Ribavirin Endpoint En EMin EMax Znm Gnm Tnm 1) duration of treatment , ) frequency of injecting interferon 3) duration of flue like symptoms after injection 4) probability of getting gastrointestinal symptoms 5) probability of getting psychiatric symptoms 6) probability of getting skin problems or Alopecia 7) probability of sustained virological response 6 month after treatment , , ,75 22,25 27,75 27,27 0,1233-3,36 29,75 29,75 32,50 0 0, ,17 26,67 32,67 58,33 0,105-6, , Endpoint- based utility assessment therapy B - 9,48

18 Endpoint based utility assessment of therapy C: Interferon + Ribavirin Endpoint En EMin EMax Znm Gnm Tnm 1) duration of treatment , ) frequency of injecting interferon 3) duration of flue like symptoms after injection 4) probability of getting gastrointestinal symptoms 5) probability of getting psychiatric symptoms 6) probability of getting skin problems or Alopecia 7) probability of sustained virological response 6 month after treatment Endpoint- based utility assessment therapy C , , , ,25 22,25 27,75 0 0, ,5 29,75 32, , ,57 26,67 26,67 32,67 0 0, , ,23

19 Content 1. Healthcare Decision Making & Public Participation: Priority Setting 2. Preference Elicitation Methods 3. Method & Study Design: Discrete Choice Experiment 4. Duke Health Care Delivery Systems & IQWiG Pilot Study: Hepatitis C 5. Discussion: Advantages & Disadvantages

20 Problem and Solution Goal: Evidence based metrics consistent with on utility-theoretic principles But: People (insured or patients) cannot provide reliable numeric evaluations (cardinal versus ordinal) of their preferences Solution: People can provide reliable ordinal rankings for health states, therapeutic properties, outcome profiles Stated preferences data reveal implicit relative importance weights (part worths; marginal utility). Choice-format stated preference studies can be used to quantify relative importance of benefits, properties and risks Multiple metrics are derived from the same conceptual framework Context of decision can be incorporated (real product, innovation, policy decision) Real world problem can influence experiment and choice of metric (tradeoffs regarding therapeutic properties, cost-benefit tradeoffs, risk-effectiveness tradeoffs)

21 Disadvantages of Stated Preferences Approach Potential for hypothetical bias Cognitively challenging for subjects Requires expertise in Qualitative and quantitative survey methods Experimental design Advanced statistical analysis

22 Problems of Acceptability Hypothetical bias Market-research legacy Weak consensus on methods (?) Question format Experimental Design Importance of reducing cognitive burden Statistical analysis Reporting results Audiences Clinical journals and regulators Payers

23 Advantages of preference elicitation Benefits and harms are retrieved in trade-off-relationships (realistically) and weighted against each other à How many side-effects is a patient willing to accept in order to get a given outcome. Provides coefficients for both, the intervention itself as well as its individual attributes (benefit and harm endpoints) Aggregation of a cardinal index/ score for different endpoints is possible Consistent/ uniform evidence base for patient benefit can be depicted Data on patient preferences provide a new form of evidence - and can be used by decision makers as additional source of information when designing and evaluating healthcare interventions.

24 Conclusion DCEs provide researchers with a method to examine the importance that patients (and potentially other stakeholders like experts) place on treatment attributes. DCEs can analyze the trade-offs that patients are willing to make across them. Although restricted by the sample size and the data collection method, we demonstrated the value of the DCE approach in measuring the therapeutic preferences within a utility assessment framework.

25 THANK YOU! Prof. Dr. rer. oec. Axel C. Mühlbacher Hochschule Neubrandenburg Brodaerstr Neubrandenburg muehlbacher@hs-nb.de Studies, Presentations & Advisory Boards (national & international): Abbott, Amgen, AstraZeneca. Baxter, Bayer, Berlin Chemie, Boehringer Ingelheim, BPI, Bristol-Myers Squibb, Credopard GmbH, Daiichi Sankyo, Eli Lilly, Genentech, Gilead Sciences, GlaxoSmithKline, Grünenthal, Insight Health, Janssen-Cilag, Johnson & Johnson, Merck, Merck KGaA, Novartis, Pfizer, Roche, Sanofi-Aventis, Stallergenes, VFA, ViiV Healthcare GmbH, Wyeth.