How to Choose Outcomes for an Evidence Table: Quantitative vs Qualitative Reviews

TL;DR

Outcome selection is not a data-collection task. It is a synthesis design task.

quantitative reviews need comparable outcomes
qualitative reviews need conceptually coherent outcomes
mixed-methods reviews need linked outcome domains across both

If outcomes cannot support your intended synthesis, your evidence table will be full but unusable.

Start with the decision, not the paper

Before selecting outcomes, define:

which decision the review must support
who will use the conclusion (HTA, payer, clinical team, internal strategy)
what uncertainty must be reduced

Then choose only outcomes that can move that decision.

For broader table design logic, see Analysis-Driven Design of Evidence Tables.

Quantitative outcome selection

For quantitative synthesis, each chosen outcome should pass four checks:

comparability: can values be meaningfully compared across studies?
definitional clarity: is the endpoint definition sufficiently aligned?
timepoint compatibility: are reporting windows compatible or transformable?
statistical usability: can it be converted to effect estimates?

Strong quantitative candidates

mortality or event outcomes with standard definitions
validated scale outcomes with known transformation approach
adverse-event outcomes with clear denominators
resource-use outcomes with consistent reporting rules

High-risk quantitative candidates

outcomes measured with many incompatible instruments
vague endpoint labels ("clinical improvement") without definition
mixed baseline/follow-up reporting without fixed windows

If an outcome repeatedly fails comparability checks, move it to narrative use instead of forcing weak pooling.

Qualitative outcome selection

In qualitative reviews, think in terms of phenomena of interest.

Good qualitative outcomes are:

decision-relevant (acceptability, feasibility, barriers, context)
conceptually coherent across studies
rich enough to support confidence assessment

Core qualitative domains often used

patient experience and burden
implementation barriers/facilitators
clinician workflow impact
system-level constraints

Define a small set of core domains in advance, then allow subthemes to emerge during synthesis.

Mixed-methods: link outcomes across approaches

Mixed-methods reviews are strongest when quantitative and qualitative outcomes are mapped to shared domains.

Example domain map:

effectiveness: quantitative effect size + qualitative "perceived benefit"
safety/tolerability: AE rates + qualitative acceptability narratives
implementation: quantitative adoption/protocol adherence + qualitative barriers

This lets you explain not only whether an intervention works, but also for whom and why.

A practical selection worksheet

For each proposed outcome, answer:

What decision does this support?
Quantitative, qualitative, or both?
Exact definition and measurement rule?
Required timepoint?
Expected synthesis method?
Risk if missing?

Keep only outcomes with clear downstream use.

Common mistakes

Including every reported endpoint

Creates extraction sprawl and weak synthesis.

No timepoint policy

Leads to non-comparable results and late-stage rework.

Mixing constructs in one column

Breaks analysis and confidence assessment.

Forcing qualitative themes into pseudo-quantitative scoring

Reduces meaning and can mislead decision makers.

Final thought

A good evidence table does not capture everything. It captures what is necessary for defensible synthesis.

If you choose outcomes with synthesis in mind, extraction becomes cleaner and conclusions become stronger.