Spotting Patterns, Seeing Problems: Smarter Designs for Alzheimer’s Research

In a recent article, The Economist reported on an idea gaining traction in dementia research: that certain viral infections may help trigger Alzheimer’s disease. Studies suggest that individuals vaccinated against shingles appear less likely to develop dementia later on. If viruses like varicella-zoster or herpes simplex do contribute to neurodegeneration, it could open new possibilities for prevention.

The pattern is striking. But interpreting it demands caution. Observational findings sometimes imply a finding but this may simply reflect complex entanglements between health behaviors, care access, and underlying risk factors and not the biological pathway subject to the research. Without careful design, even the most intriguing associations risk leading researchers down misleading paths.

For those serious about uncovering true causal relationships, it is no longer enough to analyze existing data and hope for the best.  Those reading blogs by Adigens Health know this well: questions like these demand a more disciplined approach.

Such approach starts with the trial we wish we could run, not simply the data we happen to have.

Before plunging into retrospective analyses, it helps to pause and ask what could go wrong and how best to anticipate it. A structured reflection on the risks, what we have been calling a trial pre-mortem, can expose the hidden traps that otherwise threaten to undo promising research.

From Data to Doubt

The idea is simple. Before reaching for models and p-values, researchers should first outline the essentials of the study they would ideally conduct: who the participants would be, what interventions and comparisons would occur, and how outcomes would be measured over time. This discipline, which is at the heart of the target trial framework, provides a reference point against which real-world data (RWD) can be judged. Only by clarifying the ideal trial can researchers properly assess if retrospective analyses can approximate it and describe situations where the gaps might be too wide to bridge.

In the case of the virus–Alzheimer’s hypothesis, several risks stand out.

🔹First, confounding by health status could easily cloud the results. Individuals who choose to get vaccinated often differ systematically from those who do not. They may be more proactive about their health, more likely to exercise, better at managing chronic conditions, or more engaged with healthcare providers.  All these factors independently lower the risk of dementia. Without carefully accounting for these differences, any observed benefit of vaccination could simply reflect broader patterns of healthier living.

🔹Second, ambiguities in timing could distort interpretations. Early signs of cognitive decline, even if subtle, may influence healthcare behavior. A patient beginning to experience memory issues might, for instance, forgo a shingles vaccination they would otherwise have sought. If later diagnosed with dementia, it could misleadingly appear that lack of vaccination preceded and contributed to the disease, when in fact the earliest stages of dementia altered the vaccination decision itself.

🔹Third, biases rooted in RWD structure can introduce hidden distortions. Health records may capture a shingles vaccination but overlook earlier symptoms of mild cognitive impairment that went undocumented. Patients may also leave a healthcare system midway through the observation period, breaking the continuity needed to track cognitive outcomes reliably. Gaps like these can warp study findings either exaggerating a protective effect or obscuring a real one.  Even if anticipated, appropriate adjustments may need to be made.

Each of these pitfalls, if unrecognized, can nudge retrospective analyses away from the truth. Pausing to anticipate them, and not waiting to fix problems after they appear, can make the difference between a study that misleads and one that genuinely informs.

Making Retrospective Studies Worth Believing

The trial pre-mortem is not a bureaucratic hurdle. We have been arguing that it is an essential safeguard and one worth doing particularly when public interest is high and premature conclusions could misdirect scientific priorities.

Causal inference approaches, implemented practically with approaches like the target trial framework, offer the clearest path forward. They acknowledge that while we often must rely on imperfect RWD, we should aim to emulate the structure and rigor of the trials we cannot run.

The virus/Alzheimer’s hypothesis may yet reshape how we think about dementia prevention. But if it does, it will not be because patterns were spotted in passing; it will be because careful methods revealed real signals amid the noise.

Observations alone are not enough. Thoughtful design, grounded in causal principles, is the bridge between what we see and what we can trust.