Clinical trials and their translation in hepatology: Past, present, and future
Bibliographic record
Abstract
Since starting my life as a hepatologist in 1968, I have witnessed marked improvements in the design, conduct, and analysis of clinical trials—thanks to such pioneers as David Sackett and Gordon Gyatt, just two of the North American scientists devoted to studying clinical epidemiology and evidence-based medicine. The Cochrane Collaboration, first established in the United Kingdom, now with centers worldwide, has focused on systematic reviews of published clinical trials.1 This was a timely development, because randomized, controlled trials (RCTs) designed to evaluate new therapeutic agents for liver disease have multiplied, particularly over the last 15 years (Fig. 1). RCTs are needed to evaluate the efficacy of new drugs, procedures, dietary modifications, and so on. This process remains incomplete unless translated to healthcare providers. Published RCTs in hepatology. Total versus trials for ascites and for CHC (PubMed Medline). As a busy intern on a ward caring for 30 patients with liver disease, my armamentarium consisted of the following: vasopressin for “presumed” bleeding varices, lasix and aldactone for fluid retention, corticosteroids and azathioprine for autoimmune hepatitis, neomycin for hepatic encephalopathy, and a very small selection of antibiotics for sepsis. The only radiologic tests available were a flat plate of the abdomen, angiography, and splenic venography! There were no endoscopic procedures, aside from rigid sigmoidoscopy! The discovery of, and then testing for, hepatitis B2 and C3 identified many clinically silent, yet chronically infected, individuals. Some had received another diagnosis for their “hepatitis.” The scientists whose identification of hepatitis B and C revolutionalized hepatology were honored with a Nobel prize and the Lasker award, respectively. Their discoveries changed the focus for many scientists and put the pharmaceutical industry into “top gear.” Clinical trials in hepatitis B and C became big business (see Fig. 2) to the virtual exclusion of “investigator-initiated” trials in viral hepatitis. But, dilemmas remain, including the following: Who has chronic viral hepatitis?; Is it cost-effective to treat all infected?; and how do we pay for our successes? Knowing you have a potentially fatal condition, curable only at prohibitive cost, is, to me, unacceptable! Therapeutics for almost all liver diseases are entering the era of “designer” drugs, which interfere with specific targets, for example, the first antivirals targeted at the hepatitis C virus (HCV) itself or at cellular components essential to complete viral replication4 (Table 1). RCTs for CHB and CHC (1992-2008 in PubMed Medline). Many academics have adopted new research interests within hepatology, such as complex trial analyses (including meta-analyses), cost-effectiveness studies, quality analysis, and the development of management guidelines—all essential to translate the indications for new therapies to clinicians. The “takeover” by the pharmaceutical industry has translated new knowledge of antivirals to front-line physicians. However, there is no budget for the translation of investigator-initiated studies5 (mostly in liver failure)—hence the continued accumulation of such patients in the emergency room. Combining the results of RCTs provides the “power” to estimate the overall effect (i.e., good or bad). Because not all trials are conducted to the same standard, the inclusion of poorly designed or conducted studies may lead to misinterpretation of the results.6, 7 To translate specific findings to our patients, careful scrutiny of all factors relevant to patient outcome must be reported, as must adherence to recruitment criteria and/or results of the screening log (i.e., number approached of the total and proportion of those approached who consented—two items commonly found missing, but much needed to asses the generalizability of a study). Further analysis (e.g., race, percentage of those with symptomatic versus asymptomatic disease at baseline, severity of background liver disease, age, sex, comorbidities, outcome of previous treatments, drug interactions, and so on) is needed to relate the outcome to our patient population. When this information is omitted, in part because of publishers' length limitations, the trial data are inaccurately presented. Reexamination of trial data is possible now that all clinical trials must be registered online (www.clinical trials.gov), and all data generated are kept for 25 years after the study's completion. Great advances in our understanding of the treatment of liver disease have taken place during my academic career, in part because the science of designing and executing clinical trials has received great attention. A major “hidden” confounder of trials remains so long as there is no formal “reporting” system for publication of “negative” trials. Responsibility for this gross oversight—with potential to compromise patient safety—lies with both journals and investigators. The risk of subsequent patients receiving unhelpful, perhaps even toxic, therapies could easily be prevented by a requirement that all trial results be summarized—linked to the mandatory registration website. Healthcare professionals, whose job is to promote good health and reduce illness, need access to the “complete story.” All the data are needed, including safety and conflicts of interest, and clinically relevant measures of efficacy, both while on and following withdrawal from therapy. Results must be clinically meaningful, reproducible, and understandable. Only then can organizations such as the Cochrane Collaboration reliably estimate the benefit of new therapies. Approval of new drugs is a huge commitment for government agencies. Because many are for the same disease, “cost-effectiveness analysis” has become a “business of its own” used not only by treating physicians, but also health-insurance agencies and stockbrokers. In Britain, where government promises “universal” access to treatment, the National Institute for Health and Clinical Excellence was established partly to assess cost-effectiveness of new treatments and technologies and unify access across all health districts.8 The downside of limiting access to agents not shown to be cost-effective is their unavailability to specific individuals anxious for a reprieve from fatal illness (e.g., sorafenib), if only for a few months.9 Thereby, a conflict arises between what is cost-effective for a population and that which is not cost-effective, but still has seemingly tangible benefits for an individual. Though some of these expensive medications may make only minor differences in life expectancy, with time, these small, incremental advances may eventually lead to dramatic improvements in outcome (e.g., treatments for breast cancer). This review focuses first on some mistakes and/or misinterpretations of clinical trials since the 1970s that may have interfered with the production of reliable data (for the most part, from my own experiences). An RTC is the most rigorous assessment of a new agent's therapeutic effect. Randomization is now done in blocks of 4, 6, 10, and so on, depending on expected recruit numbers. When possible, both patient and investigator should be blinded to the randomization. In terms of clinical-trial expertise, I was very naïve in 1968, when part of my job was to monitor patients in follow-up in the 5-year trial of azathioprine for primary biliary cirrhosis (PBC). Neither single nor double blinding had been considered in the trial design, and no formal patient-evaluation process had been outlined! Fortunately, there were only 45 recruits—we had not calculated the sample size needed to show a difference in outcome (i.e., death) at 5 years! All documentation was with pen and paper. Every result had to be accurately transferred from many different sheets of paper, thereby limiting the reliability of the reporting—desktop computers did not exist back then. Twenty years later, I recognized that for our RCT to be truly double blind, the unpleasant taste of ursodeoxycholic acid (UDCA) should be “matched” with an unpleasant tasting, similarly shaped, and colored placebo, to prevent patients unblinding themselves when they “compared notes” at support-group meetings. Failure to “blind” a study effectively becomes particularly relevant and, possibly, very difficult, if the side effects of the treatment under study are marked and if the primary measure of outcome is “soft,” such as patients with PBC who want very much to have their fatigue or pruritus reduced. Only with close examination of the “strange” results of our crossover trial-design to evaluate the effect of ondansetron on fatigue in PBC did we “figure out” that the results were invalidated both by patient anticipation of benefit and the near-universal side effects of odansetron! In a more recently published trial in patients with autoimmune hepatitis (AIH), one outcome marker was the combined biochemical response and the change in sense of “well-being,” the latter of which may have been compromised by different dosing regimens for the two arms of the study.10 Patients with PBC tend to be more “informed” than most. 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Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".