Precision Medicine

Precision Medicine

Precision medicine uses relevant differences among people or diseases to guide prevention, diagnosis or treatment. Those differences can include molecular findings, clinical characteristics, environmental exposures and behaviour; a genomic test is only one possible input. This page follows the decision from identifying a useful characteristic through testing and treatment to patient outcomes, costs and equitable access.

From a measured characteristic to a better decision

The central question is whether using a characteristic changes a choice in a way that improves outcomes compared with current care. A biomarker may predict prognosis without predicting which treatment works better; these roles must be distinguished. A clinically useful test needs an actionable result, a suitable alternative for people who test negative, and evidence that the resulting pathway benefits patients.

Role of information	What it indicates	Decision implication
Prognostic marker	Expected outcome independent of a particular treatment comparison.	Helps estimate baseline risk, but does not alone prove treatment-effect modification.
Predictive marker	Difference in relative or absolute benefit from a specified treatment compared with an alternative.	Can support assigning a treatment to a group if evidence is credible.
Diagnostic marker	Presence or classification of a condition.	May change who enters a care pathway.
Treatment-monitoring marker	Change during or after therapy.	May inform continuation or adjustment if the response rule is validated.

Precision does not mean that every person receives a unique medicine. Often the decision is to assign people to a well-defined subgroup and choose among established options. The threshold for a positive result, timing of testing and whether the specimen is adequate all become part of the intervention.

Evaluate the whole test-and-treat pathway

The test has analytical performance: whether it reliably measures the intended feature. It also has clinical performance, such as sensitivity and specificity against a suitable reference, and clinical utility: whether acting on its result improves outcomes. A companion diagnostic may provide information essential to the safe and effective use of a corresponding therapy under the relevant regulatory definition.

Compare strategies, not a test in isolation. One strategy may test eligible people and treat only those with a positive result; another may treat without testing or use a different test and therapy. Include test failure, retesting, false positives, false negatives, treatment outcomes, harms, subsequent care and time to a decision. Local availability and laboratory turnaround can affect whether a technically good test improves care in practice.

Worked example: test results and costs

Consider an invented cohort of 1,000 people, of whom 200 truly have a treatment-relevant marker. If a test has 90% sensitivity and 95% specificity, it detects 180 of the 200 marker-positive people and misses 20. Among the 800 marker-negative people, it correctly returns 760 negative results and 40 false positives; total positive tests are $180+40=220$.

The positive predictive value in this cohort is $180/220\approx81.8%$, and the negative predictive value is $760/(760+20)\approx97.4%$. These values depend on marker prevalence even when sensitivity and specificity are unchanged. They concern marker classification, not the probability that a treated patient will respond.

Actual marker status	Test positive	Test negative	Total
Marker present	180 true positives	20 false negatives	200
Marker absent	40 false positives	760 true negatives	800
Total	220	780	1,000

At an illustrative £100 per person tested, the testing bill is $1{,}000\times £100=£100{,}000$. If all 220 test-positive people receive one £5,000 course, drug acquisition is $220\times £5{,}000=£1{,}100{,}000$, for £1,200,000 in testing plus drug acquisition. That total omits administration, false-result consequences, subsequent care and outcomes; it cannot establish that the strategy is cost effective or preferable to treating everyone.

Spreadsheet item	Illustrative formula	Result
True positives	=200*0.90	180.
False negatives	=200*(1-0.90)	20.
False positives	=800*(1-0.95)	40.
True negatives	=800*0.95	760.
Positive predictive value	=180/(180+40)	About 81.8% in this cohort.
Test and drug acquisition	=1000*100+220*5000	£1,200,000 under the stated assumptions.

The example assumes a known marker status for teaching, one valid test per person and a binary decision. Real estimates need uncertainty intervals, indeterminate results, possible repeat tests and evidence for benefit and harm in each test-defined group. The 40 false positives may receive treatment without the marker, while 20 false negatives may miss an effective treatment; their consequences cannot be judged from test accuracy alone.

Estimating value over time

An economic evaluation models the incremental costs and health outcomes of complete strategies over a horizon long enough to capture important effects. Relevant inputs include prevalence in the target population, test accuracy, treatment-effect differences across marker groups, test and treatment costs, adverse effects and subsequent outcomes. The analysis must avoid assigning the marker-positive treatment effect to every test-positive person when false positives are present.

If evidence of differential treatment benefit is weak, subgroup selection can be expensive without improving health. Investigators should examine whether an apparent subgroup effect arose from a pre-specified interaction analysis or exploratory data splitting, and whether it replicates. Probabilistic and scenario analyses can reveal which uncertainties could reverse the decision, including test threshold, uptake, treatment duration and price.

Access, data governance and common mistakes

Precision strategies require specimens, laboratory capacity, result interpretation, counselling where relevant and access to the indicated care. Unequal representation in development studies can reduce performance in groups not well studied; lack of insurance coverage or nearby testing can further widen disparities. Sensitive genomic and health data require governance, appropriate consent and privacy safeguards under the applicable rules.

• Separate marker roles: Prognostic association is not evidence that a treatment works differently by marker group.
• Use the right denominator: Positive predictive value depends on prevalence in the population actually tested.
• Include incorrect results: False negatives and positives alter both outcomes and costs of the strategy.
• Compare full pathways: A test's laboratory accuracy alone is not evidence of patient benefit.
• Check generalisability: Marker prevalence, test quality and care access can change across settings.
• Protect choice and equity: Patient preferences, data rights and practical access affect whether a strategy delivers its promised benefit.

Sources and further reading

The US National Institutes of Health overview describes the broader role of genes, environment and lifestyle. The US FDA companion diagnostics page defines the regulatory role of a test linked to a treatment. The WHO European Observatory discussion of personalised medicine addresses implementation and governance. The cohort, test characteristics and prices above are original teaching assumptions, not a real test's performance or clinical recommendation.