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Updated catalogue and readme

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Corentin Choisy 6 months ago
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README.md
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@ -30,10 +30,6 @@ This repository contains all code files related to our ROSALI/Resiuals Cohort st
### Analyzed Datasets ### Analyzed Datasets
**noDIF_noConf / XXX_N.csv** - Analysis for scenario XXX_N by PCM __without__ accounting for DIF nor confusion
**DIF_noConf / XXX_N.xls** - Analysis for scenario XXX_N by PCM __with__ DIF accounted for but not confusion
**ROSALI-DIF_noConf / XXX_N_original.xls** - Analysis for scenario XXX_N by PCM __with__ DIF accounted for after detection by ROSALI but not accounting for confusion
**RESIDUALS_noConf / XXX_N_original.xls** - Analysis for scenario XXX_N by PCM __with__ DIF accounted for after detection by Andrich & Hagquist's residuals method but not accounting for confusion
**noDIF / XXX_N.csv** - Analysis for scenario XXX_N by PCM __without__ accounting for DIF and confusion accounted for **noDIF / XXX_N.csv** - Analysis for scenario XXX_N by PCM __without__ accounting for DIF and confusion accounted for
**DIF / XXX_N.xls** - Analysis for scenario XXX_N by PCM __with__ DIF and confusion accounted for **DIF / XXX_N.xls** - Analysis for scenario XXX_N by PCM __with__ DIF and confusion accounted for

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catalogue.md
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## Scenarios without DIF ## Scenarios without DIF
| | **J** | | **M** | | | | **J** | | **Epsilon** | |
| -------------- | ----- | ----- | ----- | ----- | | -------------- | ----- | ----- | ----- | ----- |
| | **4** | **7** | **2** | **4** | | | **4** | **7** | **0.2** | **0.5** |
| **Scenario 1** | X | | X | | | **Scenario 101** | X | | X | |
| **Scenario 2** | X | | | X | | **Scenario 102** | X | | | X |
| **Scenario 3** | | X | X | | | **Scenario 103** | | X | X | |
| **Scenario 4** | | X | | X | | **Scenario 104** | | X | | X |
Each scenario has 5 subscenarios: Each scenario has 5 subscenarios:
@ -20,25 +20,25 @@ Each scenario has 5 subscenarios:
## Scenarios with DIF ## Scenarios with DIF
| | **J** | | **M** | | **Nb items DIF** | | | **DIF size** | | | | **J** | | **Epsilon** | | **Nb items DIF** | | | **DIF size** | |
| --------------- | ----- | ----- | ----- | ----- | ---------------- | ----- | ----- | -------------- | ------- | | --------------- | ----- | ----- | ----- | ----- | ---------------- | ----- | ----- | -------------- | ------- |
| | **4** | **7** | **2** | **4** | **1** | **2** | **3** | **0.3** | **0.5** | | | **4** | **7** | **0.2** | **0.5** | **1** | **2** | **3** | **0.3** | **0.5** |
| **Scenario 5** | X | | X | | X | | | X | | | **Scenario 105** | X | | X | | X | | | X | |
| **Scenario 6** | X | | | X | X | | | X | | | **Scenario 106** | X | | | X | X | | | X | |
| **Scenario 7** | X | | X | | X | | | | X | | **Scenario 107** | X | | X | | X | | | | X |
| **Scenario 8** | X | | | X | X | | | | X | | **Scenario 108** | X | | | X | X | | | | X |
| **Scenario 9** | X | | X | | | X | | X | | | **Scenario 109** | X | | X | | | X | | X | |
| **Scenario 10** | X | | | X | | X | | X | | | **Scenario 110** | X | | | X | | X | | X | |
| **Scenario 11** | X | | X | | | X | | | X | | **Scenario 111** | X | | X | | | X | | | X |
| **Scenario 12** | X | | | X | | X | | | X | | **Scenario 112** | X | | | X | | X | | | X |
| **Scenario 13** | | X | X | | | X | | X | | | **Scenario 113** | | X | X | | | X | | X | |
| **Scenario 14** | | X | | X | | X | | X | | | **Scenario 114** | | X | | X | | X | | X | |
| **Scenario 15** | | X | X | | | X | | | X | | **Scenario 115** | | X | X | | | X | | | X |
| **Scenario 16** | | X | | X | | X | | | X | | **Scenario 116** | | X | | X | | X | | | X |
| **Scenario 17** | | X | X | | | | X | X | | | **Scenario 117** | | X | X | | | | X | X | |
| **Scenario 18** | | X | | X | | | X | X | | | **Scenario 118** | | X | | X | | | X | X | |
| **Scenario 19** | | X | X | | | | X | | X | | **Scenario 119** | | X | X | | | | X | | X |
| **Scenario 20** | | X | | X | | | X | | X | | **Scenario 120** | | X | | X | | | X | | X |
Each scenario has 7 subscenarios: Each scenario has 7 subscenarios:
@ -56,3 +56,31 @@ Each scenario has 7 subscenarios:
- **Adverse effects example** - **Adverse effects example**
- G - H<sub>1</sub> true, effect size -0.4, DIF -0.3/0.5 on traitement group - G - H<sub>1</sub> true, effect size -0.4, DIF -0.3/0.5 on traitement group
- **Adverse effects example** - **Adverse effects example**
## Confusion simulation details
8 covariates are simulated for each scenario, each defined by their odds ratio on treatment and odds ratio on outcome:
**OR_TT^X**: Odds ratio describing the increase in probability of patients being treated when covariate X is positive.
**OR_Y^X**: Odds ratio describing the increase in average outcome when covariate X is positive.
Covariates are simulated as in *Sturmer et al, 2021*:
| **Variables** | **OR_TT^X** | **OR_Y^X** |
| -------------- | ----- | ----- |
| **X_1** | 2.0 | 1.0 |
| **X_2** | 1.5 | 1.0 |
| **X_3** | 1.0 | 2.0 |
| **X_4** | 1.0 | 1.5 |
| **X_5** | Epsilon | Epsilon |
| **X_6** | 1.5 x Epsilon | 1.5 x Epsilon |
| **X_7** | 1.0 / 10 | 1.0 / 10 |
| **X_8** | 1.0 / 0.1 | 1.0 / 10 |
**X_1** and **X_2** are thus *Instrumental variables*, **X_3** and **X_4** are *Risk factors for the outcome* and **X_5** and **X_6** are *Cofounders*.
**X_7** and **X_8** are unobserved tail-end cofounders indicating rare treatment decisions for extreme propensity score values in each group. Untreated patients with very high propensity of treatment will be very likely to have **X_7**=1 (patients that should have been treated but weren't due to frailty). Treated patients with very low propensity of treatment will be very likely to have **X_8**=1 (patients that should not have been treated but were due to severe condition).
For each replication, observed covariates explained treatment with an AUC drawn randomly between 0.65 and 0.85 (average: 0.75).

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