development:best_practices_estimation
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development:best_practices_estimation [2016/03/30 07:58] schuemie [OHDSI Best Practices for Estimating Population-Level Effects] |
development:best_practices_estimation [2020/03/09 05:18] (current) schuemie |
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| ====== OHDSI Best Practices for Estimating Population-Level Effects ====== | ====== OHDSI Best Practices for Estimating Population-Level Effects ====== | ||
| - | :!: //This document is under development. Changes can be proposed and discussed via the OHDSI Forum and in the [[projects:workgroups:est-methods|Population-Level Estimation Workgroup]] meetings.// | + | :!: //This document is under development. Changes can be proposed and discussed via the [[http://forums.ohdsi.org/t/population-level-estimation-workgroup-discussing-best-practices|OHDSI Forum]] and in the [[projects:workgroups:est-methods|Population-Level Estimation Workgroup]] meetings.// |
| ===== General principles ===== | ===== General principles ===== | ||
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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. | ||
| + | |||
| * **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. | ||
| + | |||
| * **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 | ||
| + | * 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: | ||
| + | * Unit testing | ||
| + | * Simulation | ||
| + | * Double coding | ||
| + | * Code review | ||
| - | * Make all analysis code available as open source | + | * Include **negative controls** (exposure-outcome pairs where we believe there is no effect) |
| - | * Include negative controls (exposure-outcome pairs where we believe there is no effect) | + | |
| - | * 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 | ||
| + | |||
| + | * **Compute covariate balance** after matching for all covariates, and terminate study if a covariate has standardized difference > 0.1 | ||
| ===== Best practices (self-controlled case series) ===== | ===== Best practices (self-controlled case series) ===== | ||
| + | * Include a **risk window just prior to start of exposure** to detect time-varying confounding (e.g. contra-indications, protopathic bias) | ||
| + | |||
| + | ===== Best practices ((nested) case-control) ===== | ||
| - | * Include a risk window just prior to start of exposure to detect time-varying confounding (e.g. contra-indications, protopathic bias) | + | * **Don't** do a case-control study |
development/best_practices_estimation.1459324722.txt.gz · Last modified: 2016/03/30 07:58 by schuemie
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