Abra JeffersVA Palo Alto

Dept. of Mgmt Science & EngineeringStanford University

Medical Decision Making

Decision AnalysisQuantitative, systematicIdentifies alternatives, outcomes, utility of

outcomesUses models

Represent structural relationshipsDeterministicProbabilistic

Compute expected outcomes of each alternative

Balance feasibility v. reality / simplicity v. complexity

Examine robustness2

Identify AlternativesMutually exclusive

A, B mutually exclusive ⇒ P(A∩B) = 0One and only one of the events must occur

Collectively exhaustiveA, B collectively exhaustive ⇒ P(A) + P(B) = 1Events represent entire outcome spaceAt least one event must occur

3

Identifying Outcomes

4

Cost-Effectiveness PlaneΔC

ΔE

5

Increased CostsIncreased EffectivenessIs ICER < WTP?

Increased CostsDecreased EffectivenessUse status quo

Decreased CostsIncreased EffectivenessUse new strategy

Decreased CostsDecreased EffectivenessProbably use status quo

StepsDetermine problemDetermine status quo interventionDetermine alternative interventionsAnalysisSensitivity Analysis

6

ExampleHypothetical cohort with prevalent disorderTreatment

Reduces probability of serious complicationsLong term side effects

There is an imperfect test for disorderAlternatives:

Treat no oneTest all and treat positivesTreat all

7

Complications2

No complications15

Disorder

No disorder15

Treat none

ComplicationsT1.95

No complications14.95

TP - treat

Complications2

No complications15

FN - don't treat

Disorder

TN - don't treat15

FP - treat14.95

No disorder

Test

Complications1.95

14.95

Disorder

No disorder14.95

Treat all

Decision Diagram

Decision node

Probabilistic node

Terminal node

8

Example cont.Outcomes

No disorder, no treatmentNo disorder, treatmentDisorder, treatment, complicationsDisorder, treatment, no complicationsDisorder, no treatment, complicationsDisorder, no treatment, no complications

For each outcome assume known costs and LE (clinical trial)

Intermediate eventsTestsSide effects

9

Create Decision Node

10

Create Decision Node

11

Create Decision Node

12

Create Decision Node

13

14

15

16

17

18

19

20

Strategy 1: Treat None

21

Strategy 1: Treat None

22

Strategy 1: Treat None

23

Strategy 1: Treat None

24

Strategy 1: Treat None

25

Select Subtree

26

Collapse Subtree

27

28

Strategy 2 - Test

29

Strategy 2 - Test

30

Strategy 2 - Test

31

32

33

34

35

36

37

38

Now What?Now we have our tree structureLets start filling in probabilities

39

40

41

42

# = 1- prev

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

RecapThus far…

Created decision treeUsed descriptive variable namesUsed clones to take advantage of parallel

structureTracking variables

Now What?OutcomesAssume we know from clinical trial

CostsLife years

62

Complications2

No complications15

Disorder

No disorder15

Treat none

ComplicationsT1.95

No complications14.95

TP - treat

Complications2

No complications15

FN - don't treat

Disorder

TN - don't treat15

FP - treat14.95

No disorder

Test

Complications1.95

14.95

Disorder

No disorder14.95

Treat all

63

Effectiveness OutcomesNormal – 15 life expectancyDisorder – 3 year detrimentComplications – 10 year detrimentTreatment – 0.05 year detriment

64

65

66

67

68

Add Complications Tracker

69

Add Complications Tracker

70

Add Complications Tracker

71

Add Disorder Tracker

72

73

Payoff 1: LE_normal - if(t_disorder = 1; LEdet_disorder; 0) + - if(t_treatment= 1; LEdet_treatment; 0) - if(t_complications = 1; LEdet_complications; 0)

74

75

76

77

Payoff 2: c_normal + if(t_disorder = 1; c_disorder; 0) + if(t_test = 1; c_test; 0) + + if(t_treatment = 1; c_treatment; 0) + if(t_complications = 1; c_complications; 0)

78

79

80

81

82

83

84

85

Baseline = Status Quo 86

87

88

89

90

91

Sensitivity Analysis

92

93

94

95

96

CaveatsAssumed full knowledge of

Future life expectancyFuture lifetime costs

Inappropriate if payoffs without full lifetime infoMarkov model

97

98

Thank you!