development:best_practices_estimation
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development:best_practices_estimation [2016/04/20 09:17] schuemie |
development:best_practices_estimation [2016/07/15 11:44] schuemie |
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| * **Transparency**: others should be able to reproduce your study in every detail using the information you provide. | * **Transparency**: others should be able to reproduce your study in every detail using the information you provide. | ||
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| * **Prespecify** what you're going to estimate and how: this will avoid hidden multiple testing (fishing expeditions, p-value hacking). Run your analysis only once. | * **Prespecify** what you're going to estimate and how: this will avoid hidden multiple testing (fishing expeditions, p-value hacking). Run your analysis only once. | ||
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| * **Validation of your analysis**: you should have evidence that your analysis does what you say it does (showing that statistics that are produced have nominal operating characteristics (e.g. p-value calibration), showing that specific important assumptions are met (e.g. covariate balance), using unit tests to validate pieces of code, etc.) | * **Validation of your analysis**: you should have evidence that your analysis does what you say it does (showing that statistics that are produced have nominal operating characteristics (e.g. p-value calibration), showing that specific important assumptions are met (e.g. covariate balance), using unit tests to validate pieces of code, etc.) | ||
| ===== Best practices (generic) ===== | ===== Best practices (generic) ===== | ||
| - | + | * **Write a full protocol**, and make it public prior to running the study. This should include | |
| - | + | * Research question + hypotheses to be tested | |
| - | * Make all analysis code available as **open source** | + | * Which method(s), data, cohort definitions. |
| + | * What is the primary analyses and what are sensitivity analyses? | ||
| + | * Quality control | ||
| + | * Amendments and Updates | ||
| * **Validate** all code used to produce estimates. The purpose of validation is to ensure the code is doing what we require it to do. Possible options are: | * **Validate** all code used to produce estimates. The purpose of validation is to ensure the code is doing what we require it to do. Possible options are: | ||
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| * Produce **calibrated p-values** | * Produce **calibrated p-values** | ||
| + | * Make all analysis code available as **open source** so others can easily replicate your study | ||
| ===== Best practices (new-user cohort design) ===== | ===== Best practices (new-user cohort design) ===== | ||
| + | * Use **propensity scores** (PS) | ||
| + | * Build PS model using **regularized regression** and a **large set of candidate covariates** (as implemented in the CohortMethod package) | ||
| + | * Use either **variable-ratio matching** or **stratification** on the PS | ||
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| + | * **Compute covariate balance** after matching for all covariates, and terminate study if a covariate has standardized difference > 0.2 | ||
| ===== Best practices (self-controlled case series) ===== | ===== Best practices (self-controlled case series) ===== | ||
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| * Include a **risk window just prior to start of exposure** to detect time-varying confounding (e.g. contra-indications, protopathic bias) | * Include a **risk window just prior to start of exposure** to detect time-varying confounding (e.g. contra-indications, protopathic bias) | ||
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| + | ===== Best practices ((nested) case-control) ===== | ||
| + | |||
| + | * **Don't** do a case-control study | ||
development/best_practices_estimation.txt · Last modified: 2020/03/09 05:18 by schuemie
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