Entry Criterion Re-execution Bug In Waterfall Reports

Hey guys! Today, we're diving deep into a tricky bug encountered in the Bayer-Group's PhenEx project. This bug messes up the counts in waterfall reports, making the data look all sorts of wonky. Let's break it down, figure out what's going on, and see how to avoid this headache.

Understanding the Bug: The Case of the Miscalculated Counts

So, here's the deal: The entry criterion—that initial filter that determines who gets included in the cohort—is being re-run using something called subset_tables_index instead of the original raw data. Imagine you're trying to count how many people initially meet a certain condition. Instead of looking at the whole population, the system is only looking at a subset that's already been filtered. This leads to the initial entry criterion count being the same as the final cohort size, which is totally not what we want! We need to know the actual number of people who met the entry criteria from the get-go. This incorrect re-execution skews the waterfall report, because the subsequent columns don't reflect the true attrition.

Think of it like this: you're hosting a party and want to know how many people RSVP'd "yes." But instead of checking the original guest list, you're only counting the people who actually showed up. You'd miss all the people who RSVP'd but couldn't make it, right? That's essentially what's happening here. The subset_tables_index is like only looking at the people who showed up, instead of the original RSVP list. The root cause? Characteristic phenotypes, which are like additional traits or conditions we're tracking, have references to the entry phenotype via anchor_phenotypes. Because of this dependency, the entry criterion gets recomputed using that smaller, already-filtered subset_tables_index. The reporting mechanism then pulls these incorrect numbers, resulting in a waterfall report that doesn't make sense. This problem highlights the importance of accurate data lineage and how dependencies between phenotypes can introduce subtle but significant errors in cohort analysis.

To really nail this down, let's visualize it. Suppose our initial dataset has 1000 people. The entry criterion should identify, say, 500 of those people. But due to this bug, the system re-evaluates that entry criterion on a subset, say only the 300 that are in the final cohort. Then, it wrongly reports that 300 people met the initial entry criterion. The waterfall report is then skewed to show an inaccurate depiction of the cohort selection process. The true impact extends beyond just incorrect reporting. If decisions are made based on these reports, they could lead to flawed analyses and ultimately, incorrect conclusions about the study population. Therefore, identifying and rectifying this kind of bug is crucial for ensuring the integrity of research outcomes.

How to Reproduce the Bug: A Step-by-Step Guide

Want to see this bug in action? Follow these steps:

1. Set the Stage: First, you need to create baseline characteristics that use the entry criterion set as an anchorphenotype. This is the key, guys! This establishes the dependency that triggers the re-execution.
2. Execute the Cohort: Run the cohort generation process, but make sure you set lazy_execution = False. This forces the system to compute everything right away, instead of deferring calculations.
3. Create and Execute the Waterfall Reporter: Build a waterfall reporter, which is the tool that visualizes how the cohort size changes at each step. Then, run it.
4. Witness the Madness: Observe the waterfall reporter. You'll likely see that the 'N' counts (the final cohort sizes) are correct, but the 'remaining' column will be messed up. It'll show the final cohort size for all rows, instead of the actual number of people who met each criterion at each step.

By following these steps, you should be able to reliably reproduce the bug and see the incorrect counts in the waterfall report. It's like a magic trick, but instead of pulling a rabbit out of a hat, you're pulling incorrect data out of a cohort!

Expected Behavior: What We Want to See

Okay, so what should happen? Well, the waterfall report should show the correct counts at each step. Specifically, the 'remaining' column should accurately reflect the number of individuals who still meet all the criteria up to that point. It should show how the cohort size decreases as more criteria are applied. Imagine a real waterfall, gradually diminishing as it goes over rocks. The report must mimic that gradual decline and never lie. The goal is transparency. Each data point in the waterfall is intended to provide insights into how different inclusion or exclusion criteria impact the overall cohort size. The accuracy is critical because researchers rely on these reports to understand the composition of the cohort, identify potential biases, and assess the impact of individual phenotypes.

For example, if we start with 1000 people and the entry criterion selects 500, the first row of the waterfall should show 500 remaining. If the next criterion filters it down to 300, the second row should show 300 remaining, and so on. No funny business, just the straight facts. With accurate counts, users can easily understand how the cohort is being shaped by different selection criteria. Also, they can quickly see if a specific criterion has an unexpectedly large impact on the cohort size, which might indicate a problem with the criterion itself or with the underlying data.

Diving Deeper: Why This Matters So Much

This bug isn't just a minor annoyance; it can have serious consequences for data analysis. Imagine making important decisions based on incorrect data! That's why it's so important to understand what's going on and how to fix it. Here's why this matters so much:

• Incorrect Cohort Characterization: The waterfall report is a key tool for understanding the characteristics of the cohort. If the counts are wrong, you'll get a misleading picture of who's actually in the study.
• Flawed Analysis: If you're using the waterfall report to inform further analysis, incorrect counts can lead to flawed conclusions. For example, you might overestimate or underestimate the prevalence of certain conditions in the cohort.
• Compromised Reproducibility: If someone else tries to reproduce your analysis using the same data and criteria, they'll get different results if the waterfall report is broken. This undermines the reproducibility of the research.
• Wasted Time and Resources: Incorrect data can lead to wasted time and resources as researchers try to reconcile the discrepancies and figure out what went wrong. It's like chasing your tail, guys.

Potential Solutions and Workarounds

Okay, so we've identified the problem and understand why it matters. What can we do about it? Here are a few potential solutions and workarounds:

• Fix the Dependency: The root cause is the dependency between characteristic phenotypes and the entry phenotype. The best solution is to modify the code to prevent the entry criterion from being re-executed using the subset_tables_index. This might involve restructuring the way dependencies are handled or introducing a flag to prevent re-execution in certain cases. Ensuring the entry phenotype is evaluated only once is critical for data integrity.
• Re-evaluate the Anchor Phenotype: Assess whether the characteristics truly require being anchored on the entry criterion phenotype. If they are logically independent, then removing the anchor might be a valid option. Be cautious though, because you need to carefully consider the impact on the overall analysis. Removing the anchor could alter the meaning of the characteristic phenotypes.
• Manual Correction: As a temporary workaround, you could manually correct the counts in the waterfall report. This is not ideal, as it's time-consuming and prone to errors, but it might be necessary in some cases. However, document all manual corrections meticulously to ensure transparency and reproducibility.
• Data Validation: Implement data validation checks to detect discrepancies in the waterfall report. This can help you identify the bug early on and prevent it from affecting downstream analyses. Regularly comparing initial entry criterion counts with the final cohort size may highlight potential re-execution issues.

Conclusion: Staying Vigilant with Cohort Analysis

This bug highlights the importance of being vigilant when working with cohort analysis tools. Even seemingly small errors in the code can have significant consequences for data quality and analysis results. The best way to avoid these kinds of problems is to have a strong understanding of how the tools work, to test them thoroughly, and to implement data validation checks to catch errors early on. By understanding the intricacies of PhenEx and being aware of potential pitfalls like this re-execution bug, researchers can ensure the reliability and validity of their cohort studies. Keep your eyes peeled and your data clean, guys!