Session 7: The Sliding Dichotomy 7.1 Background 7.2 Principles 7.3 Hypothetical example 7.4 Implementation 7.5 Example: CRASH Trial
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1 Session 7: The Sliding Dichotomy 7.1 Background 7.2 Principles 7.3 Hypothetical example 7.4 Implementation 7.5 Example: CRASH Trial Session 7 1
2 7.1 The Sliding Dichotomy: Background The sliding dichotomy is another approach to the analysis of ordinal outcome scales It was first proposed (I believe) by two stroke physicians, Eivind Berge (Oslo) and David Barer (Newcastle) It differs fundamentally from the proportional odds model approach Its starting point is the heterogeneity of any group/cohort/sample of individuals Session 7 2
3 7.2 The Sliding Dichotomy: Principles A conventional dichotomous analysis of an ordinal scale sets the same threshold to define a favourable outcome for all individuals This one threshold fits all approach applied to a heterogeneous population can lead to major problems with floor and ceiling effects Moreover, the one threshold fits all approach does not accord with clinical thinking The sliding dichotomy tailors the threshold to each individual s baseline prognosis Session 7 3
4 7.3 The Sliding Dichotomy: An Example A hypothetical clinical trial was constructed by taking data from two completed Phase III head injury trials one to provide the active treatment group and the other to provide the placebo group. Session 7 4
5 Conventional Analysis of Hypothetical Trial (%) 'Active' 'Placebo' 0 Dead/VS Severe Moderate Good Session 7 5
6 Conventional Analysis of Hypothetical Trial (%) 'Active' 'Placebo' 0 Dead/VS Severe Moderate Good Session 7 6
7 Prognostic Model The patients were grouped into three bands using a prognostic model based on age, the GCS Motor Score and the CT scan classification Session 7 7
8 Poor Prognosis Band - Hypothetical Trial (%) 'Active' 'Placebo' 10 0 Dead/VS Severe Moderate Good Session 7 8
9 Poor Prognosis Band - Hypothetical Trial (%) 'Active' 'Placebo' 10 0 Dead/VS Severe Moderate Good Session 7 9
10 Intermediate Prognosis Band - Hypothetical Trial (%) 'Active' 'Placebo' 0 Dead/VS Severe Moderate Good Session 7 10
11 Intermediate Prognosis Band - Hypothetical Trial (%) 'Active' 'Placebo' 0 Dead/VS Severe Moderate Good Session 7 11
12 Good Prognosis Band - Hypothetical Trial (%) 'Active' 'Placebo' 10 0 Dead/VS Severe Moderate Good Session 7 12
13 Good Prognosis Band - Hypothetical Trial (%) 'Active' 'Placebo' 10 0 Dead/VS Severe Moderate Good Session 7 13
14 Analysis Conventional Dichotomy Difference in % Favourable: Active - Placebo 7.9% p-value Sliding Dichotomy 13.3% <0.001 Session 7 14
15 7.4 Implementing the Sliding Dichotomy (1 of 2) Need to start with a prognostic model Good discrimination is essential Good calibration is less important Need a way to define the bands Could choose equal numbers of patients per band Could choose a range of predicted probabilities Need to choose the number of prognostic bands Could link to the number of levels on the outcome scale Could use a large number but enforce monotonicity of cut-points (see next bullet point) Session 7 15
16 7.4 Implementing the Sliding Dichotomy (2 of 2) Need to determine the point of dichotomy within each band Could be pre-determined Could be based on pooled outcome distribution within the band Need to pool results over the prognostic bands Could simply count total numbers of favourable and unfavourable outcomes Could pool odds ratios from the separate bands (Mantel-Haenszel) Session 7 16
17 7.5 Example: The CRASH Trial (1 of 5) CRASH (Corticosteroid Randomisation After Significant Head injury) Lancet 2004; 364: Primary endpoint was 14 day mortality Trial was stopped early with clear evidence of harm with active treatment (21.1% mortality versus 17.9% on placebo; p=0.0001) This example is based on a secondary outcome measure, namely the Glasgow Outcome Scale at 6 months after injury (with vegetative state pooled with severe disability) Session 7 17
18 7.5 Example: The CRASH Trial (2 of 5) A prognostic model was built using binary logistic regression to predict unfavourable GOS at 6 months (dead/vegetative/severe) The covariates included were: age, GCS, pupillary reaction and presence of major extracranial injury Patients were divided into three prognostic bands of equal size: best, intermediate and worst prognosis The definition of favourable outcome was prespecified for each prognostic band Session 7 18
19 7.5 Example: The CRASH Trial (3 of 5) Dead SD MD GR Best prognosis Corticosteroid Placebo Intermediate prognosis Corticosteroid Placebo Worst prognosis Corticosteroid Placebo Session 7 19
20 7.5 Example: The CRASH Trial (4 of 5) Worst than expected Better than expected OR (95% CI) Best prognosis Corticosteroid (1.03 to 1.43) Placebo Intermediate prognosis Corticosteroid (0.91 to 1.23) Placebo Worst prognosis Corticosteroid (1.11 to 1.47) Placebo Overall result: summing numbers OR 1.17 (1.08 to 1.27), p= pooling ORs [conventional split OR 1.17 (1.07 to 1.27), p= OR 1.08 (0.99 to 1.17), p=0.0759] Session 7 20
21 7.5 Example: The CRASH Trial (5 of 5) Dead SD MD GR Best prognosis Corticosteroid Placebo Intermediate prognosis Corticosteroid Placebo Worst prognosis Corticosteroid Placebo Session 7 21
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