Exploring the reproducibility crisis in reproductive medicine and how strong inference can lead to more reliable fertility treatments
Imagine you're a fertility researcher on the verge of a breakthrough. Your experiments suggest a new treatment that could help countless couples conceive. You publish your exciting results, only to discover that other labs—including your own—can't reproduce them when they try again. The promising treatment vanishes like a mirage, and you're left wondering what went wrong.
This scenario plays out more often than we'd like to admit in reproductive medicine, and it's part of a broader phenomenon called the "replication crisis" 1 . Across many scientific fields, researchers are finding that a surprising number of published studies don't hold up when others attempt to reproduce them. In reproductive medicine, where hopes and lives hang in the balance, the stakes for reliable science couldn't be higher.
The concept of "strong inference" was first introduced by physicist John Platt in 1964, who noticed that some scientific fields progressed much faster than others 2 . He attributed this difference to a systematic approach of generating multiple competing hypotheses and then designing crucial experiments to eliminate all but one.
Generate competing hypotheses for fertility problems
Create tests that can distinguish between hypotheses
Rigorously discard explanations that don't fit data
This approach stands in stark contrast to simply gathering data that supports a single predetermined idea. When reproductive medicine embraces strong inference, we get more reliable, robust findings that can truly help patients.
The replication crisis, also known as the reproducibility crisis, refers to the growing number of published scientific results that other researchers cannot reproduce 1 . While this affects many fields, the problem is particularly concerning in reproductive medicine.
What's causing this crisis in reproductive medicine? Several factors converge:
To understand how reproducibility issues play out in real-world reproductive medicine, let's examine the case of preimplantation genetic screening (PGS).
PGS involves screening embryos for chromosomal abnormalities before implantation during in vitro fertilization (IVF). The technique saw rapid adoption in fertility clinics, but evidence supporting its effectiveness varied considerably 2 .
A typical PGS experiment involves several precise steps:
A few cells are carefully removed from the part of the embryo that will become the placenta (trophectoderm)
The biopsied cells are analyzed using techniques like next-generation sequencing to detect chromosomal abnormalities 2
Only embryos with normal chromosomal profiles are selected for transfer
Early studies of PGS showed promising results, with some reporting significant improvements in pregnancy rates. However, as more labs attempted to replicate these findings, inconsistencies emerged.
| Study Type | Reported Improvement in Pregnancy Rates | Consistency Across Labs |
|---|---|---|
| Early PGS Studies | Significant improvement | Low |
| Later Replication Studies | Mixed results | Moderate |
| Multi-center Trials | Moderate improvement | High |
This variability stemmed from multiple sources:
The controversy led to more rigorous, multi-center studies and eventually to improved, more standardized PGS protocols. This process exemplifies how strong inference and attention to reproducibility can ultimately strengthen a field, even through initial uncertainty.
Modern reproductive research relies on sophisticated laboratory equipment and reagents. Here are some key tools enabling advances in fertility science:
Precise measurement and transfer of small liquid volumes
Handling eggs, sperm, and embryos; preparing culture mediaMeasure heat from chemical reactions
Studying energy dynamics of gametesContainment and mixing of solutions
Preparing culture media, chemical reactionsContinuous monitoring of embryo development
Assessing embryo quality without disruptionDetailed assessment of sperm motility and morphology
Male fertility diagnosis and researchHigh-resolution genetic analysis
Preimplantation genetic testingThese tools have revolutionized fertility research in multiple ways. Time-lapse imaging, for instance, allows embryologists to monitor embryo development in real-time without disturbing the delicate culture environment 7 . Advanced pipettes enable precise handling of minute volumes when working with eggs, sperm, and embryos. Computer-assisted sperm analysis systems provide objective, detailed assessment of sperm quality, replacing subjective visual estimates 7 .
The good news is that the scientific community is actively working on solutions to the reproducibility crisis. Many of these approaches align with the principles of strong inference:
As Stuart Buck of the Paragon Health Institute notes, while there's "no hard-and-fast target" for ideal reproducibility rates, we should expect "more like 80-90% of science to be replicable" 5 .
The journey toward more reproducible reproductive medicine isn't about achieving perfection. As Brian Nosek, executive director of the Center for Open Science, wisely observes: "Science is not bad; it's just flawed. But ultimately the goal of the science community is to improve it" 5 .
By embracing strong inference, implementing open science practices, and maintaining realistic expectations about the inherent variability in biological systems, reproductive medicine can continue to advance. The result will be more reliable treatments that truly help the millions of people struggling with infertility worldwide.
The reproduction problem in science might not have a quick fix, but through systematic thinking and commitment to rigorous methods, we can ensure that fertility research remains a trustworthy foundation for building families.