How to Manage Confounding Variables in Research

*** Propensity Score Matching: Explained *** ~ Propensity score matching (PSM) is a statistical technique to reduce selection bias in observational studies. It's beneficial when you want to estimate the effect of a treatment, policy, or intervention but can't randomly assign subjects to treatment and control groups. ~ How It Works 1. Estimate Propensity Scores: Calculate each subject's treatment probability based on observed covariates (characteristics). This is often done using logistic regression. 2. Match Subjects: Pair subjects in the treatment group with subjects in the control group with similar propensity scores. This creates a "matched" sample where the groups are comparable on the observed covariates. 3. Analyze Outcomes: To estimate the treatment effect, compare outcomes between the matched treatment and control groups. ~ Why Use PSM? * Reduce Bias: By matching subjects on observed covariates, PSM helps control for confounding variables that could bias the treatment effect estimate. * Mimic Randomization: In randomized experiments, randomization ensures that treatment and control groups are balanced on average. PSM attempts to mimic this balance in observational studies. ~ Example Scenario Imagine you want to study the effect of smoking on lung cancer, but can't randomly assign people to smoke. You collect data on smokers and non-smokers and use PSM to match smokers with non-smokers who have similar characteristics (age, gender, etc.). This way, you can more accurately estimate the effect of smoking on lung cancer by reducing the bias from confounding variables. ~ Benefits and Limitations of PSM Benefits: * Reduces Bias: Controls for confounding variables and reduces selection bias. * Transparency: Makes the assumptions and methodology explicit. * Flexibility: It can be used for various treatments and outcomes. Limitations: * Unobserved Confounders: PSM cannot account for unobserved confounders, which can still bias the results. * Matching Quality: The matching quality depends on the chosen covariates and matching method. * Sample Size: Matching can reduce the sample size, affecting the study's statistical power. ~ Conclusion Propensity Score Matching is a powerful tool for estimating causal effects in observational studies. By carefully estimating propensity scores, matching subjects, and analyzing outcomes, researchers can reduce bias and obtain more reliable estimates of treatment effects. However, it's essential to acknowledge its limitations and ensure rigorous implementation and diagnostics. --- B. Noted

When your treatment effect p-value decreases and your effect size increases with the addition of more controls, it's a good thing, right? Well, sometimes... Here are key considerations to bear in mind: - Omitted Variable Bias Correction: Adding controls can rectify biases stemming from omitting confounding variables correlated with both the treatment and outcome. This correction may amplify the estimate, indicating a "stronger" effect. Example: In a study on education's impact on income, neglecting innate ability (linked to both education and income) could underestimate education's true effect. Introducing a proxy for ability might boost the education coefficient. - Precision Increases (Variance Decreases): Incorporating covariates that account for outcome variance can diminish residual variance. This leads to tighter confidence intervals and reduced standard errors, bolstering the statistical strength of estimates. Exercise caution in the case of: - Post-Treatment Bias: Controlling for post-treatment variables may introduce bias despite apparent result enhancement. - Overfitting or Mechanical Increases in Significance: In scenarios with limited samples or numerous controls, results may appear inflated due to overfitting. Validate robustness through methods like cross-validation or pre-analysis plans. - Suppression Effects: Occasionally, a control variable may suppress noise or counteract a masking effect. While acceptable, understanding the causal framework is crucial for interpretation. In summary: ✅ Positive indicators after control addition: - Reduction of omitted variable bias. - Justified pre-treatment controls aligned with your identification strategy. ⚠️ Exercise caution and evaluate: - Appropriateness of controlled variables. - Validity of your causal model. - Potential introduction of bias.

The pitfalls of class prediction in omics 🧵 1/ You think you’ve built the perfect omics predictor. The accuracy is high. The p-value is low. But is it real—or just a story your data whispered back? 2/ High-dimensional data is a double-edged sword. Thousands of genes, hundreds of samples. With enough features, even random noise can look predictive. That’s the curse of dimensionality. 3/ Statistically, you can always draw a hyperplane to separate two classes. Even if the labels are random. That’s overfitting. And omics is a playground for it. 4/ So we add regularization: LASSO, Ridge, Elastic Net. They penalize complexity, reward simplicity. But it’s not enough. Because the real danger is how we validate. 5/ Cross-validation (CV) is standard. But do you select your features before the CV folds? That’s data leakage. And it gives inflated performance. Always. Every time. 6/ Nested CV is your friend: Inner loop: tune hyperparameters Outer loop: estimate error It’s slower. But it’s honest. 7/ Still confident? Let’s talk confounders. Batch effects. Age. Ethnicity. Study site. If they’re correlated with outcome, they fake predictive power. 8/ Confounding doesn’t go away with random splits. It hides in the noise. And only shows itself when your model fails in an external dataset. Validate on independent cohorts. 9/ Want to compare your model to an existing one? Do it on a neutral dataset. Using your training set to favor your model is bias by design. 10/ So you beat the baseline by 2%. Is it statistically significant? Not unless you test it across multiple datasets. Ideally 5–6. Meta-analysis helps. 11/ Unsupervised pitfalls: If you cluster samples using features chosen with the labels in mind, You’ll rediscover your labels—not biology. Clustering must be unsupervised in every way. 12/ Many retractions in omics come from these mistakes: Data leakage Confounding Overfitting Unvalidated results Because story-telling is easier than science. 13/ To get it right, you need more than code. You need humility. Statistical discipline. Curated metadata. And rock-solid validation. 14/ Key takeaways: Overfitting loves high dimensions Never pre-select features across folds Use nested CV Validate externally Watch for confounders Simplicity > complexity 15/ Omics is powerful. But power needs control. Guard your models from yourself. The truth is out there—but only if you earn it. I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics https://lnkd.in/erw83Svn

In the quest to unearth causal relationships from observational data, we often confront the conundrum of selection bias. But what if we could adjust for this bias directly in our analysis? One of the solution is to incorporate propensity scores into regression models—a statistical tactic that's gaining traction across various fields. Let's unpack a few scenarios: Case1 Health Interventions: Patients self-select into treatments, influenced by severity of illness or demographics, potentially skewing effectiveness studies. Case2 Educational Innovations: More engaged students are likely to participate in new academic programs, obscuring the program's true efficacy. Case3 Business Campaigns: High-spend customers might be the first to try new products, making it hard to gauge the product's success across the broader market. Now let's look at how we do regression adjustment with propensity scores. Here’s the step-by-step: Step1 Compute Propensity Scores: For each individual, calculate the propensity score—the probability of receiving the treatment, based on observable characteristics. Step2 Use in Regression: Input this score as an independent variable in your regression analysis, alongside the actual treatment variable. Step3 Adjust and Analyze: The regression now adjusts for selection bias, allowing us to estimate the treatment's effect more accurately, as if the treatment were randomly assigned. By including the propensity score in your regression model, you control for the factors that could lead to an individual receiving the treatment—factors that could also independently affect the outcome. It’s a powerful way to control for confounding variables without losing data points, a common limitation in matching methods. Have you utilized propensity scores within your regression models? Share your experiences or thoughts on this method below!