Should you adjust for baseline characteristics within randomized controlled trials?

At first glance, one may think the answer is no. Randomization should insure that baseline characteristics are balanced across trial arms. In practice, however, sometimes baseline characteristics due differ somewhat by trial arm simply by chance, especially in smaller randomized controlled trials (RCTs). A JAMA Guide to Statistics and Methods by Holmberg et al. 2022…

Should you adjust for covariates when analyzing data from randomized controlled trials?

FDA draft guidance published this month says you should. In most cases, adjusting for covariates is not necessary. Randomization generally insurers that covariates are balanced across clinical trial arms. Randomization, however, may not always result in perfectly balanced trial arms. In these cases, the FDA notes that covariate adjustment is perfectly acceptable. There are some…

When (and how) to use population-adjusted indirect comparisons?

Population-adjusted indirect comparisons (PAICs) include both matching-adjusted indirect comparisons (MAICs) and Simulated treatment comparisons (STCs). The key data requirement for these methods is that they have individual patient data (IPD) from at least one clinical trial. This means the methods are most useful for studied funded by the clinical trial sponsor or when IPD clinical…

The gold standard of scientific evidence

That is the title of my latest article in Pharmaceutical Market Europe. An excerpt is below. Randomised controlled trials (RCTs) are regarded as the gold standard of scientific evidence, and for good reason. By randomizsing a treatment across study arms, RCTs eliminate patient-treamtent selection bias, resulting in reliable causal inference. In contrast, in the real…

Off-label prescribing

How frequently are pharmaceuticals used off label?  Perhaps more than you think.  Although these figures are a bit dated, Tabarrok (2000) details the extent of off-label prescribing in the U.S. as follows: According to a study by the U.S. General Accounting Office, 56 percent of cancer patients have been given non-FDA-approved prescriptions, and 33 percent…

Too many trials, not enough patients

As research in new cancer treatments has grown, scientists may have run into a serious roadblock: there many not be enough patients to fill the needed clinical trials.  As the New York Times reports: There are too many experimental cancer drugs in too many clinical trials, and not enough patients to test them on. The logjam…

Predicting Real-World Effectiveness of Cancer Therapies Using OS and PFS Clinical Trials Endpoints

Clinical trials for cancer treatments aim to demonstrate whether one treatment is better than another. What is of most interest to patients, providers and payers, however, is which treatment works best in the real-world, not in a randomized controlled trial. Further, clinical trials often use progression free survival to measure treatment outcomes rather than overall…