Research Finance

Research Finance

The money side of research operations: building a study budget that funds what the study will actually consume, tracking spend against delivery rather than against the calendar, and deciding which research to fund when the portfolio asks for more than the budget holds.

When to use this skill

• Costing a study before a funding request, where the budget will be scrutinised line by line and a missing line item becomes unfunded work
• Answering "what does this cost per participant" — the number every funder, finance partner, and sponsor asks first
• Tracking a programme mid-flight and needing to know whether the spend is buying delivery or just buying time
• Forecasting an overrun early enough to descope rather than late enough to need supplementary funding
• Prioritising a research portfolio that asks for more than the budget available
• Defending a research budget against a finance partner who sees a cost centre and needs to see decision value

Inputs the skill expects

• Unit costs: per participant, per site, per site-month, and fixed programme costs
• Enrolment target, screen failure rate, site count, and duration
• Contingency and overhead rates your organisation applies
• For tracking: budget at completion, weighted milestones with completion, and planned versus actual spend by period
• For portfolio work: each study's cost, the value of the decision it informs, and the probability it changes that decision
• Decision deadlines — a study that lands after the decision is worth nothing

Clarify First

Before generating, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Screen failure or recruitment yield rate — budgets built on enrolled participants alone systematically under-fund screening, which is real work on real people
• Whether overhead and contingency are inside or outside the quoted number — the same study is quoted at wildly different totals depending on this, and the mismatch surfaces after the award
• The decision each study informs and its value — without it a portfolio can only be ranked by cost, which funds the cheap studies rather than the valuable ones
• Who holds the budget and what triggers a change request — determines how much contingency you need and how granular the tracking must be

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Workflows

Workflow 1 — Build a study budget from unit costs

1. Separate costs into four blocks: per participant, per site, per site-month, and fixed. Most under-budgeting comes from costs sitting in the wrong block — site coordination billed as fixed rather than per site-month is the classic.
2. Mark which per-participant costs are incurred on screened rather than enrolled participants. Screening is charged on everyone screened.
3. Set contingency and overhead. [RECOMMENDED] 10% contingency minimum; 8% is the floor below which every protocol amendment becomes a change request.
4. Run the builder. Read the cost per enrolled participant and the cost per insight — those are the two numbers the funding conversation turns on.
5. Clear every fail. They are all cases of work the study will do and the budget does not fund.

python3 research-ops/research-finance/scripts/study_budget_builder.py \
  --input research-ops/research-finance/assets/sample_budget.json \
  --format text

Workflow 2 — Track burn against milestone delivery

1. Weight the milestones by share of total work, not by how visible they are. Enrolment usually carries 40-50% of the weight; setup milestones feel important and are cheap.
2. Record planned and actual spend by period.
3. Run the tracker. It computes earned value, cost and schedule performance, and the estimate at completion.
4. Act on the spend-versus-delivery gap, not on the spend-versus-calendar view. A programme at 36% spent and 24% delivered is heading for a 50% overrun, and the monthly finance report showing "on budget" will not tell you that.

python3 research-ops/research-finance/scripts/burn_vs_milestone_tracker.py \
  --input research-ops/research-finance/assets/sample_burn.json \
  --format text

Workflow 3 — Prioritise the portfolio by decision value

1. For each candidate study, state the decision it informs, the cost of getting that decision wrong, and the probability the study changes the choice.
2. Record decision reversibility and the decision deadline.
3. Run the prioritiser. It computes expected decision value, discounts for reversibility, zeroes out studies that arrive too late or inform decisions already made, and allocates the budget greedily by value per unit cost.
4. Take the fail findings to the portfolio review directly. "This study informs a decision that has already been made" is a conversation worth having out loud, and the ranking alone will not force it.

python3 research-ops/research-finance/scripts/portfolio_prioritizer.py \
  --input research-ops/research-finance/assets/sample_portfolio.json \
  --budget 400000 --format text

Decision frameworks

Where research budgets under-fund themselves

Interpreting the cost performance index

[PROVEN] The single most useful number in research finance is the gap between percent spent and percent delivered. A gap above 20 points is the reliable early signal of a supplementary funding request, and it appears months before the calendar-based view shows anything wrong.

Value of information

A study is worth funding to the extent that it changes a decision, and a decision is worth informing to the extent that getting it wrong is expensive.

expected decision value = decision value × P(research changes the decision)
                          × reversibility multiplier

Two studies are automatically worth zero regardless of their inputs: one informing a decision already made, and one answering after the decision deadline. Both are common, and both survive portfolio review because nobody asks the question directly.

Cost per insight benchmarks

Cost per insight is a blunt instrument and a useful one — it forces a comparison across methods that otherwise get evaluated in isolation.

[RECOMMENDED] Count engineering time in experiment costs. It is the most frequently omitted research cost in product organisations, and omitting it makes experiments look free relative to studies that carry an explicit invoice.

Anti-Patterns

Budgeting the Enrolled, Screening the Many

Mistake: Building the participant budget on the enrolment target when the protocol will screen substantially more people to reach it. Why it happens: The enrolment number is the one in the protocol and the one everyone quotes. The screen failure rate lives in a different section, if it is written down at all. Instead: Gross every screening-stage cost up by 1/(1 − screen failure rate). At a 25% failure rate that is a third more screening assessments than the enrolment target implies — and screening is real clinical work on real people that someone has to pay for.

Tracking Spend Against the Calendar

Mistake: A monthly report showing spend versus planned spend, with no delivery measure alongside it. Why it happens: Spend and calendar are both easy to measure and both come from finance systems automatically. Delivery requires someone to assess milestone completion honestly. Instead: Weight the milestones, assess completion each period, and report the spend-delivery gap as the headline. A study spending exactly to plan while enrolling at half rate looks perfectly healthy on a calendar view and is heading for a large overrun.

Front-Loaded Milestone Weights

Mistake: Assigning heavy weights to setup milestones — protocol approved, ethics obtained, first site activated — so the programme shows 40% delivered before a single participant is enrolled. Why it happens: Setup milestones are discrete, visible, and satisfying to complete. Enrolment is a long grind with no natural checkpoints. Instead: Weight by share of actual work and cost. Enrolment typically deserves 40-50% of the total weight. Front-loaded weights hide exactly the problem earned-value tracking exists to expose, and they hide it during the window when descoping is still possible.

Ranking the Portfolio by Cost

Mistake: Funding the cheap studies first because more of them fit in the budget. Why it happens: Cost is known precisely and decision value is an estimate, so the ranking gravitates to the number that feels solid. Instead: Rank by expected decision value per unit cost. A rough estimate of decision value beats no estimate — it at least surfaces the studies costing more than the decision is worth. Funding by cost systematically starves the expensive studies attached to the largest decisions, which is precisely backwards.

Funding the Decision Already Made

Mistake: A study that will report after the choice has been committed, kept in the portfolio to validate it. Why it happens: The work was scoped when the decision was still open, and cancelling it feels like admitting the decision was made prematurely. Instead: Ask directly, at every portfolio review, whether each study's decision is still open and what result would change it. If nothing would, cut the study and redirect the money. This is documentation, and it should be funded as documentation if it is funded at all.

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