Trial design, endpoints and cost

CHALLENGE proved exercise works in colon cancer but not how much is needed. A trial comparing doses, as we would for a drug, would tell health systems what to fund.

Running an early trial properly requires monitors, data managers, safety reporting and regulatory filings that universities cannot afford from commercial providers. A public not-for-profit would do this work at cost.

Blood tests can now find leftover cancer months before scans, but most patients who test positive have nothing to enrol in. One standing trial per country would fix that.

Trials that test many companies' drugs side by side against one shared control work best when nobody's company runs them. A standing non-profit sponsor would make this the norm rather than a rare exception.

Instead of one-off small studies, run a standing trial that continuously tests new treatments for side-effects, adding and dropping arms as evidence arrives.

Instead of starting a new trial for every drug pair, keep one always-open trial per cancer that new arms can join and leave, sharing the same control group.

Frequent tiny doses of cheap old chemotherapy pills have shown surprising benefit in some cancers. A single large trial network in India and Africa could find out where this works and where it does not.

When researchers use hospital records to ask 'would drug A have beaten drug B in a trial', they should follow a published recipe and register their plan first, so the answer can be trusted.

No company will pay to find out whether six months of its drug works as well as twelve. A dedicated public fund would pay for those trials, which save patients side effects and health systems money.

Universities often refuse to be the legal sponsor of a first-in-human trial because they cannot afford the insurance and liability. A shared public insurance pool would remove that block.

Sometimes a trial cannot randomise, so the new drug is compared with past patients' records. Clear published rules on when that is allowed, and how it must be done, would replace case-by-case guesswork.

The UK's RECOVERY trial tested many cheap COVID drugs quickly by randomising thousands of ordinary hospital patients. Cancer needs the same machine for old drugs.

Today a cancer drug is approved for shrinking tumours. A drug that stopped cancer spreading would fail that test, so almost nobody develops one. A new endpoint would fix that.

Thousands of patients have received standard treatment in the control arms of past trials. Pooling their anonymised data would let new trials borrow from them and randomise fewer patients to old treatments.

Between diagnosis and surgery there are usually a few weeks. Giving a new drug in that window and comparing the tumour before and after surgery shows whether it hits its target in real people, quickly and cheaply.

Some drugs are approved for any cancer with a particular mutation. Clear rules on how many cancer types must be tested, and how to combine results across them, would make these approvals more consistent and faster.

Rather than building a new trial from scratch for every drug, keep one permanent trial open per cancer where new treatments can be slotted in and dropped out, sharing the same patients, control group and infrastructure.

Instead of a new trial for each resistance mechanism, one continuous trial could sort patients into arms based on the reason their last treatment failed.

Large adjuvant trials already follow thousands of patients for years. Adding a second randomisation to a cheap old drug would answer repurposing questions almost for free.

When a resistance mutation first appears in the blood, the current drug is often still controlling most of the tumour. Adding a second drug rather than swapping may keep both under control.

Drugs are approved on trials of fit, younger patients and then given to everyone. A required follow-on trial in older, sicker and more diverse patients would show whether the benefit holds in real life.

For treatments of symptoms like nausea, fatigue or neuropathy, each patient can try the drug and a placebo in alternating periods and see what works for them; pooling many such personal trials gives a population answer too.

Statistical methods can combine information across similar rare cancers to reach an answer with fewer patients. Regulators need to say in advance when that is acceptable.

Measuring tumours on scans for trials is slow, expensive and inconsistent between readers. Software that measures lesions and flags changes, checked by a radiologist, could make trial endpoints cheaper and more reliable.

Once a class of antibody such as PD-1 blockers is proven, later copies could be approved on smaller trials showing equivalence, forcing price competition and freeing patients and money for genuinely new drugs.

Trials often measure a stand-in for survival, such as time until the cancer grows on scans. An independent body would test, for each cancer and treatment type, whether the stand-in actually predicts survival, and publish the answer.

Trials sometimes quietly swap the outcome they promised to measure for one that looks better. Software can compare the registered plan with the published paper and flag the switch.

Trials look at dozens of patient subgroups and some will look good by chance. A statistical method that pulls extreme subgroup results toward the overall result would make these claims more honest.

When the standard treatment has been given to thousands of similar patients in earlier trials, a new trial could randomise fewer people to it and lean on that history, as long as the old data still matches.

Gut bacteria appear to influence whether immunotherapy works, and diet and antibiotics shape gut bacteria. Yet almost no drug trial records what patients ate or which antibiotics they took. Recording it would cost almost nothing.

Trials often stop following patients once the main result is in, so we never learn whether the drug extended life. Linking participants to national death and cancer registries costs almost nothing and would answer that question.

Three companies testing three drugs against the same standard treatment each recruit their own control group. Pooling those controls in one shared study would need fewer patients and answer faster.

Radiotherapy trials need physicists to check every plan and central review of every target drawn, which nobody pays for. Fund that infrastructure permanently so trials are faster and results are trustworthy.

Trials report side effects as a table of percentages that hides how long they lasted, how bad they felt and whether people stopped treatment. Tolerability should be measured with defined endpoints and a decision rule, like efficacy.

Trial staff still retype data from the hospital record into the trial database, and monitors then check every entry by hand. Piping data directly and checking by risk would cut cost and errors.

Before spending millions on a randomised trial, analyse existing patient records as if the trial had already happened. If the answer is obvious or the question is unanswerable, skip or redesign the trial.

Trials stop following patients after a few years, so late side effects and late relapses are missed. Link trial participants to national records so follow-up continues automatically for decades.

Rare cancers often share a broken cellular machine even when they arise in different organs. Grouping patients by that shared fault makes trials possible.

Simulate trials of drug combinations in populations of virtual patients to decide which real trials to run, and keep score of how often the simulations were right.

Before a company runs its big trial, all the major regulators should agree together on the design in one meeting, so the same trial can be approved everywhere.

A blood test at six weeks can show whether a treatment is doing anything. Trials should use it to drop failing combinations fast and move patients on.

Blood tests that look for many cancers at once take a decade to prove they save lives. Regulators could accept fewer late-stage cancers as the first answer, if trials keep counting deaths afterwards.

As results come in, the trial sends more new patients to the arms that are working and fewer to those that are not, so more people benefit and bad arms die faster.

Companies sometimes choose the biomarker threshold that makes their trial look best after seeing the data. Requiring the threshold to be fixed and published before the big trial starts prevents this.

A drug that shrinks a tumour by half but leaves the resistant sub-population untouched will fail. Trials should measure whether every sub-population is cleared, not just overall size.

Most cancer patients are over 65 and many have other illnesses, yet trials routinely exclude them. Regulators should require sponsors to justify each exclusion, so the evidence matches the patients.

Trials and hospital records describe the same things in different languages. Publish the translation so trial patients can be followed for life in routine data and trial results compared with routine care.

Metastasis causes most cancer deaths, yet no drug is developed to stop cells spreading. Create a formal approval route for drugs that prevent metastasis, tested in people at high risk of it.

Several drugs are approved for the same cancer, but nobody tests which order works best because no company benefits from the answer. Public multi-arm trials could settle these questions efficiently.

Rare cancers are collectively common but each is too rare for normal trials. Link every rare cancer patient worldwide into one network with registries and trial designs built for small numbers.

Prevention trials are slow and run separately. A shared platform trial across precancers like Barrett's, oral leukoplakia, lung nodules and pancreatic cysts would test many drugs faster.

Rare cancers together are a fifth of all cancers, but each is too small for its own trial. One permanent trial with many arms would give all of them a route.

Whether finding cancer earlier saves lives depends on the cancer. Public models, one per cancer, would let trials and payers predict the mortality benefit from a stage shift honestly.

Every trial writes its protocol and analysis plan from scratch. A shared library of well-written templates and ready-to-run analysis code would let teams start from the best version rather than a blank page.

A drug that adds years of life would earn extra years of market protection; one that adds a few weeks would earn none. Extensions would be lost if the promised benefit is not confirmed.

Before a trial is funded, patients who have had the disease sign off on how many visits, scans and blood draws it demands, and on whether the endpoints measure things that matter to them.

When two accepted treatments are equally reasonable, the computer system would offer to randomise the choice and track the result, turning ordinary care into a continuous trial.

A trial can be designed to detect a tiny improvement that is statistically real but too small to matter. Protocols should state up front what size of benefit would be worth having, and be built to detect that.

For a frail 80-year-old, staying independent may matter more than living a few months longer. Trials designed for older patients should measure what they care about.

Patients join a long-term cohort once and agree in advance that they may be offered new treatments as they appear, while others in the cohort serve as the comparison group. No new trial has to start from zero.

When control-arm patients switch to the new drug after their cancer grows, the survival comparison gets muddied. Trials should plan in advance how they will correct for this, and report both raw and corrected numbers.

Give combinations before surgery and look at how much tumour is left when it is removed. That answer comes in months, so many pairs can be tested quickly.

Some cancers reach the brain in up to a quarter of patients. Prevention trials could aim specifically at stopping that, instead of treating it once it has happened.

Nobody knows what a cancer trial should cost because budgets are secret. Publishing anonymised cost per patient by trial type would expose waste and let funders set targets.

Trials often have independent radiologists check whether tumours grew. How often they disagree with the treating doctors is a measure of how trustworthy the result is, and it is rarely published.

Trials record why each screened patient did not join, but that data is never shared. Publishing it would show which rules block the most people and which are pointless.

Regulators are unsure what to accept as proof that an anti-wasting drug helps. Agreeing on a simple measure such as stair climbing would unblock the whole field.

Before a big trial starts, ask hundreds of patients how much extra survival they would trade for a given side-effect, so the trial is designed to test something patients would actually want.

National cancer registries already collect the outcome data. Adding a randomisation button lets doctors compare two standard treatments across thousands of patients at a fraction of the usual cost.

Rare cancer patients are already tracked in registries. Offering randomisation inside the registry makes trials far cheaper and lets almost anyone take part.

Trials usually study one treatment at a time, so nobody knows the best order. Deciding the next step in advance, by lottery, answers the sequencing question at little extra cost.

Immunotherapy is often given for two years or until it stops working, but responses can last long after stopping. Trials that randomly assign responders to stop or continue would show whether the extra year is needed.

Use the cancer registry itself as the trial machine: randomise patients at diagnosis, then let the registry collect the outcomes for a fraction of the usual cost.

Few companies develop cancer prevention drugs because trials take decades. If regulators accepted validated precancer endpoints, as they do cholesterol for heart disease, industry would return.

A treatment that adds two months of life but takes up most of those days in hospitals and clinics may not be worth it to an older patient. Trials should report how many days treatment consumes.

Many large trials are launched on the strength of a small study with no comparison group, and most of those large trials fail. Insisting on a smaller randomised comparison first would filter out weak drugs earlier.

Once two drugs of a kind exist, a third should have to prove itself against the best of them, not against an outdated comparison, so patients and payers learn which is actually better.

The traditional way of finding a dose looks only at severe side effects in the first month. Modern statistical designs can use all patients' data and count the grumbling, long-lasting problems that make people quit months later.

Instead of stopping after the mid-sized trial, waiting a year, then starting the big one, run them as one study with a clear pre-agreed rule for continuing. This saves a year or more per drug.

Wasting has several causes. Measuring the specific hormone in each patient's blood would put the right patients into the right trial instead of mixing everyone together.

Real treatment is a series of decisions: start with this, switch to that if it fails. Sequential multiple-assignment randomised trials test whole strategies by randomising patients again at each decision point.

When a trial has no comparison group, the comparison is sometimes built from old patient records. Set rules for how that is done so the answer is not rigged.

One in five people with advanced cancer has brain spread, yet most trials refuse them. Requiring brain cohorts would give those patients evidence instead of guesswork.

Many big trials continue for years after the data already show the drug is unlikely to work. Agreeing in advance to stop earlier when the odds look bad would spare patients and free money for better ideas.

Trials that only enrol patients with a positive biomarker can never prove the biomarker matters. Including a smaller biomarker-negative group would show whether the test is really needed.

For treatments that will not cure, what matters is how long the treatment keeps working without becoming unbearable, and how the person feels. Trials should measure both of those as their main results.

New cancer drugs should have to prove not only that they extend life but how they affect how people feel and function, with that result printed in the label like efficacy.

Frail older patients are often given full doses and then have them cut after bad side effects, or are given nothing. Trials should test starting at a lower dose from the beginning.

Scans at three months often cannot tell whether immunotherapy is working. A blood test at six weeks may give a clearer, earlier answer.

Giving immunotherapy for a few weeks before surgery produces a tumour sample that shows exactly what the drug did. That is the fastest way to learn who responds.

If a blood test shows no tumour DNA after months of treatment, a trial could test pausing the drug and restarting only when the DNA reappears, giving patients time off without waiting for scans to show growth.

Pragmatic trials want to use the progression dates recorded in ordinary clinic notes instead of expensive protocol scans. Checking how well those routine records match formal trial measurements would show when that shortcut is safe.

Every patient on the new drug is compared with every patient on the old one: who lived longer, and if equal, who had fewer serious side effects, and if still equal, who felt better. The share of 'wins' becomes the result.