Clinical Trials

Restricted mean survival time is the expected duration of survival up to a chosen time of restriction τ . For comparison studies, the difference in restricted mean survival times between two groups provides a summary measure of the treatment effect that is free of assumptions regarding the relative shape of the two survival curves, such as proportional hazards. However, it can be difficult to judge the magnitude of the effect from a comparison of restricted means due to the truncation of observation at time τ .

In this article, we describe additional ways of expressing the treatment effect based on restricted means that can be helpful in this regard. These include the ratio of restricted means, the ratio of life-years (or time) lost, and the average integrated difference between the survival curves, equal to the difference in restricted means divided by τ . These alternative metrics are straightforward to calculate and provide a means for scaling the effect size as an aid to interpretation. Examples from two randomized, multicenter clinical trials in prostate cancer, NRG/RTOG 0521 and NRG/RTOG 0534, with primary endpoints of overall survival and biochemical/radiological progression-free survival, respectively, are presented to illustrate the ideas.

The four effect measures (restricted mean survival time difference, restricted mean survival time ratio, time lost ratio, and average survival rate difference) were 0.45 years, 1.05, 0.81, and 0.038 for RTOG 0521 and 1.36 years, 1.17, 0.56, and 0.12 for RTOG 0534 with τ  = 12 and 11 years, respectively. Thus, for example, the 0.45-year difference in the first trial translates into a 19% reduction in time lost and a 3.8% average absolute difference between the survival curves over the 12-year horizon, a modest effect size, whereas the 1.36-year difference in the second trial corresponds to a 44% reduction in time lost and a 12% absolute survival difference, a rather large effect.

In addition to the difference in restricted mean survival times, these alternative measures can be helpful in determining whether the magnitude of the treatment effect is clinically meaningful.

There are increasing pressures for anonymised datasets from clinical trials to be shared across the scientific community. However, there is no standardised set of recommendations on how to anonymise and prepare clinical trial datasets for sharing, while an ever-increasing number of anonymised datasets are becoming available for secondary research. Our aim was to explore the current views and experiences of researchers in the United Kingdom about de-identification, anonymisation, release methods and re-identification risk estimation for clinical trial datasets.

We used an online exploratory cross-sectional descriptive survey that consisted of both open-ended and closed questions.

We had 38 responses to invitation from June 2022 to October 2022. However, 35 participants (92%) used internal documentation and published guidance to de-identify/anonymise clinical trial datasets. De-identification, followed by anonymisation and then fulfilling data holders’ requirements before access was granted (controlled access), was the most common process for releasing the datasets as reported by 18 (47%) participants. However, 11 participants (29%) had previous knowledge of re-identification risk estimation, but they did not use any of the methodologies. Experiences in the process of de-identifying/anonymising the datasets and maintaining such datasets were mostly negative, and the main reported issues were lack of resources, guidance, and training.

The majority of responders reported using documented processes for de-identification and anonymisation. However, our survey results clearly indicate that there are still gaps in the areas of guidance, resources and training to fulfil sharing requests of de-identified/anonymised datasets, and that re-identification risk estimation is an underdeveloped area.

Self-reported questionnaires on health status after randomized trials can be time-consuming, costly, and potentially unreliable. Administrative data sets may provide cost-effective, less biased information, but it is uncertain how administrative and self-reported data compare to identify chronic conditions in a New Zealand cohort. This study aimed to determine whether record linkage could replace self-reported questionnaires to identify chronic conditions that were the outcomes of interest for trial follow-up.

Participants in 50-year follow-up of a randomized trial were asked to complete a questionnaire and to consent to accessing administrative data. The proportion of participants with diabetes, pre-diabetes, hyperlipidaemia, hypertension, mental health disorders, and asthma was calculated using each data source and agreement between data sources assessed.

Participants were aged 49 years (SD = 1, n = 424, 50% male). Agreement between questionnaire and administrative data was slight for pre-diabetes (kappa = 0.10), fair for hyperlipidaemia (kappa = 0.27), substantial for diabetes (kappa = 0.65), and moderate for other conditions (all kappa >0.42). Administrative data alone identified two to three times more cases than the questionnaire for all outcomes except hypertension and mental health disorders, where the questionnaire alone identified one to two times more cases than administrative data. Combining all sources increased case detection for all outcomes.

A combination of questionnaire, pharmaceutical, and laboratory data with expert panel review were required to identify participants with chronic conditions of interest in this follow-up of a clinical trial.

Concerns about low accrual have long been a standard part of the discourse on cancer clinical trials, reaching even as far as the news media. Indeed, so many trials are closed before completing accrual that a cottage industry has recently developed creating statistical models to predict trial failure. We previously proposed four methodologic fixes for the current crisis in clinical trials: (1) dramatically reducing the number of eligibility criteria, (2) using data routinely collected in clinical practice for trial endpoints; then lowering barriers to accrual by (3) cluster randomization or (4) staged consent.

We report our practical experience of applying these fixes to randomized trials at Memorial Sloan Kettering Cancer Center.

We have completed seven single-center randomized trials, with several more underway and accruing rapidly, with a total accrual approaching 10,000. Many of the trials have compared surgical interventions, an area where trials have traditionally been hard to complete. Only one of these trials was externally funded. While low costs were possible due to the existing research infrastructure at our institution, such infrastructure is common at major cancer centers.

Further research on innovative clinical trial designs is warranted, particularly in higher-stakes settings, and in trials of medical and radiotherapy interventions.

Stepped-wedge cluster randomized trials tend to require fewer clusters than standard parallel-arm designs due to the switches between control and intervention conditions, but there are no recommendations for the minimum number of clusters. Trials randomizing an extremely small number of clusters are not uncommon, but the justification for small numbers of clusters is often unclear and appropriate analysis is often lacking. In addition, stepped-wedge cluster randomized trials are methodologically more complex due to their longitudinal correlation structure, and ignoring the distinct within- and between-period intracluster correlations can underestimate the sample size in small stepped-wedge cluster randomized trials. We conducted a review of published small stepped-wedge cluster randomized trials to understand how and why they are used, and to characterize approaches used in their design and analysis.

Electronic searches were used to identify primary reports of full-scale stepped-wedge cluster randomized trials published during the period 2016–2022; the subset that randomized two to six clusters was identified. Two reviewers independently extracted information from each report and any available protocol. Disagreements were resolved through discussion.

We identified 61 stepped-wedge cluster randomized trials that randomized two to six clusters: median sample size (Q1–Q3) 1426 (420–7553) participants. Twelve (19.7%) gave some indication that the evaluation was considered a “preliminary” evaluation and 16 (26.2%) recognized the small number of clusters as a limitation. Sixteen (26.2%) provided an explanation for the limited number of clusters: the need to minimize contamination (e.g. by merging adjacent units), limited availability of clusters, and logistical considerations were common explanations. Majority (51, 83.6%) presented sample size or power calculations, but only one assumed distinct within- and between-period intracluster correlations. Few (10, 16.4%) utilized restricted randomization methods; more than half (34, 55.7%) identified baseline imbalances. The most common statistical method for analysis was the generalized linear mixed model (44, 72.1%). Only four trials (6.6%) reported statistical analyses considering small numbers of clusters: one used generalized estimating equations with small-sample correction, two used generalized linear mixed model with small-sample correction, and one used Bayesian analysis. Another eight (13.1%) used fixed-effects regression, the performance of which requires further evaluation under stepped-wedge cluster randomized trials with small numbers of clusters. None used permutation tests or cluster-period level analysis.

Methods appropriate for the design and analysis of small stepped-wedge cluster randomized trials have not been widely adopted in practice. Greater awareness is required that the use of standard sample size calculation methods can provide spuriously low numbers of required clusters. Methods such as generalized estimating equations or generalized linear mixed models with small-sample corrections, Bayesian approaches, and permutation tests may be more appropriate for the analysis of small stepped-wedge cluster randomized trials. Future research is needed to establish best practices for stepped-wedge cluster randomized trials with a small number of clusters.

Performing large randomized trials in anesthesiology is often challenging and costly. The clinically integrated randomized trial is characterized by simplified logistics embedded into routine clinical practice, enabling ease and efficiency of recruitment, offering an opportunity for clinicians to conduct large, high-quality randomized trials under low cost. Our aims were to (1) demonstrate the feasibility of the clinically integrated trial design in a high-volume anesthesiology practice and (2) assess whether trial quality improvement interventions led to more balanced accrual among study arms and improved trial compliance over time.

This is an interim analysis of recruitment to a cluster-randomized trial investigating three nerve block approaches for mastectomy with immediate implant-based reconstruction: paravertebral block (arm 1), paravertebral plus interpectoral plane blocks (arm 2), and serratus anterior plane plus interpectoral plane blocks (arm 3). We monitored accrual and consent rates, clinician compliance with the randomized treatment, and availability of outcome data. Assessment after the initial year of implementation showed a slight imbalance in study arms suggesting areas for improvement in trial compliance. Specific improvement interventions included increasing the frequency of communication with the consenting staff and providing direct feedback to clinician investigators about their individual recruitment patterns. We assessed overall accrual rates and tested for differences in accrual, consent, and compliance rates pre- and post-improvement interventions.

Overall recruitment was extremely high, accruing close to 90% of the eligible population. In the pre-intervention period, there was evidence of bias in the proportion of patients being accrued and receiving the monthly block, with higher rates in arm 3 (90%) compared to arms 1 (81%) and 2 (79%, p = 0.021). In contrast, in the post-intervention period, there was no statistically significant difference between groups (p = 0.8). Eligible for randomization rate increased from 89% in the pre-intervention period to 95% in the post-intervention period (difference 5.7%; 95% confidence interval = 2.2%–9.4%, p = 0.002). Consent rate increased from 95% to 98% (difference of 3.7%; 95% confidence interval = 1.1%–6.3%; p = 0.004). Compliance with the randomized nerve block approach was maintained at close to 100% and availability of primary outcome data was 100%.

The clinically integrated randomized trial design enables rapid trial accrual with a high participant compliance rate in a high-volume anesthesiology practice. Continuous monitoring of accrual, consent, and compliance rates is necessary to maintain and improve trial conduct and reduce potential biases. This trial methodology serves as a template for the implementation of other large, low-cost randomized trials in anesthesiology.

Excessive use of antibiotics has led to development of antibiotic resistance and other antibiotic-associated complications. Dermatologists prescribe more antibiotics per clinician than any other major specialty, with much of this use for acne. Alternative acne treatments are available but are used much less often than antibiotics, at least partially because dermatologists feel that they are less effective. Spironolactone, a hormonal therapy with antiandrogen effects that can address the hormonal pathogenesis of acne, may represent a therapeutic alternative to oral antibiotics for women with acne. However, the comparative effects of spironolactone and oral antibiotics in the treatment of acne have not been definitively studied. The Spironolactone versus Doxycycline for Acne: A Comparative Effectiveness, Noninferiority Evaluation (SD-ACNE) trial aims to answer whether spironolactone, in addition to standard topical therapy, is noninferior to doxycycline (an oral antibiotic) for women with acne. Several interesting challenges arose in the development of this study, including determining acceptability of the comparative regimens to participating dermatologists, identifying data to support a noninferiority margin, and establishing a process for unblinding participants after they completed the study while maintaining the blind for study investigators.

We present the scientific and clinical rationale for the decisions made in the design of the trial, including input from key stakeholders through a Delphi consensus process.

The Spironolactone versus Doxycycline for Acne: A Comparative Effectiveness, Noninferiority Evaluation trial (NCT04582383) is being conducted at a range of community and academic sites in the United States. To maximize external validity and inform clinical practice, the study is designed with broad eligibility criteria and no prohibition of use of topical medications. Participants in the trial will be randomized to receive either spironolactone 100 mg/day or doxycycline hyclate 100 mg/day for 16 weeks. The primary outcome is the absolute decrease in inflammatory lesion count, and we have established a noninferiority margin of four inflammatory lesions. Secondary outcomes include the percentage of participants achieving Investigator Global Assessment success, change in quality of life, and microbiome changes and diversity.

The Spironolactone versus Doxycycline for Acne: A Comparative Effectiveness, Noninferiority Evaluation trial will have substantial implications for the treatment of acne and antibiotic stewardship. In addition, this study will provide important information on the effect of these systemic agents on the development of changes to the microbiome and antibiotic resistance in a healthy population of patients.

This study aimed to determine the prevalence of ordinal, binary, and numerical composite endpoints among coronavirus disease 2019 trials and the potential bias attributable to their use.

We systematically reviewed the Cochrane COVID-19 Study Register to assess the prevalence, characteristics, and bias associated with using composite endpoints in coronavirus disease 2019 randomized clinical trials. We compared the effect measure (relative risk) of composite outcomes and that of its most critical component (i.e. death) by estimating the Bias Attributable to Composite Outcomes index [ln(relative risk for the composite outcome)/ln(relative risk for death)].

Composite endpoints accounted for 152 out of 417 primary endpoints in coronavirus disease 2019 randomized trials, being more frequent among studies published in high-impact journals. Ordinal endpoints were the most common (54% of all composites), followed by binary or time-to-event (34%), numerical (11%), and hierarchical (1%). Composites predominated among trials enrolling patients with severe disease when compared to trials with a mild or moderate case mix (odds ratio = 1.72). Adaptations of the seven-point World Health Organization scale occurred in 40% of the ordinal primary endpoints, which frequently underwent dichotomization for the statistical analyses. Mortality accounted for a median of 24% (interquartile range: 6%–48%) of all events when included in the composite. The median point estimate of the Bias Attributable to Composite Outcomes index was 0.3 (interquartile range: −0.1 to 0.7), being significantly lower than 1 in 5 of 24 comparisons.

Composite endpoints were used in a significant proportion of coronavirus disease 2019 trials, especially those involving severely ill patients. This is likely due to the higher anticipated rates of competing events, such as death, in such studies. Ordinal composites were common but often not fully appreciated, reducing the potential gains in information and statistical efficiency. For studies with binary composites, death was the most frequent component, and, unexpectedly, composite outcome estimates were often closer to the null when compared to those for mortality death. Numerical composites were less common, and only two trials used hierarchical endpoints. These newer approaches may offer advantages over traditional binary and ordinal composites; however, their potential benefits warrant further scrutiny.

Composite endpoints accounted for more than a third of coronavirus disease 2019 trials’ primary endpoints; their use was more common among studies that included patients with severe disease and their point effect estimates tended to underestimate those for mortality.

Provisions of the Inflation Reduction Act mandating drug price negotiation by the Centers for Medicare & Medicaid Services have been criticized as a threat to pharmaceutical innovation. This study models potential impacts of the Inflation Reduction Act on drug approvals based on the differential contributions of large pharmaceutical companies and smaller biotechnology firms to clinical trials and the availability of capital.

This study examined research and development expense, revenue, and new investment (sale of common and preferred stock) by public biopharmaceutical companies and sponsorship of phased clinical trials in ClinicalTrials.gov. Financial data were incorporated in a model that estimates the number of drugs in each phase and approvals from reported phase-specific costs and transition rates, proportional sponsorship of trials by companies of different size, projected reductions in research and development spending based on company size, and three scenarios by which large companies may allocate reductions in research and development spending among clinical phases: (1) research and development proportionally reduced across phases; (2) research and development disproportionally reduced in phases 2–3; and (3) research and development disproportionately reduced in phases 1–2.

Financial data were examined for 1378 public biopharmaceutical companies (2000–2018). Research and development expense was associated with revenue for 79 large companies with market capitalization ≥$7 billion with a 10% reduction in revenue reducing research and development expense by 8.4%. For 1299 smaller companies with market capitalization <$7 billion, research and development was associated with new investment, but not revenue. Smaller companies sponsored 55.2% of phase 1, 55.6% of phase 2, and 49.8% of phase 3 trials in ClinicalTrials.gov 2013–2018. In a model of clinical development that apportions clinical trials between large and smaller companies and determines the number of trials based on research and development resources, 400 drugs entering development produced 47.3 approvals (11.83% rate). A 10% reduction in revenue, reflecting the upper boundary of observed changes 2000–2018, with (1) proportional reduction across phases 1–3 produced 45.1 approvals (4.61% reduction); (2) disproportional reduction of phases 2–3 produced 42.8 approvals (9.55% reduction); and (3) disproportional reduction of phases 1–2 produced 46.9 approvals (0.95% reduction).

This work suggests that the drug price negotiation provisions of the Inflation Reduction Act could have little or no impact on the number of drug approvals. While large pharmaceutical companies may reduce research and development spending, continued research and development by smaller companies and strategic allocation of research and development resources by large companies may mitigate any negative effects of the Inflation Reduction Act.

Randomized controlled trials with pragmatic intent aim to generate evidence that directly informs clinical decisions. Some have argued that the ethical protection of informed consent can be in tension with the goals of pragmatism. But the impact of other ethical protections on trial pragmatism has yet to be explored.

In this article, we analyze the relationship between additional ethical protections for vulnerable participants and the degree of pragmatism within the PRagmatic Explanatory Continuum Indicator Summary-2 (PRECIS-2) domains of trial design.

We analyze three example trials with pragmatic intent that include vulnerable participants.

The relationship between ethical protections and trial pragmatism is complex. In some cases, additional ethical protections for vulnerable participants can promote the pragmatism of some of the PRECIS-2 domains of trial design. When designing trials with pragmatic intent, researchers ought to look for opportunities wherein ethical protections enhance the degree of pragmatism.

Including women of childbearing age in a clinical trial makes it necessary to consider two factors from a bioethical perspective: first, the lack of knowledge about the potential teratogenic effects of an investigational product, and also, the principle of justice not to exclude any population from the benefits of research. The most common way to address this issue is by requiring volunteers to use contraceptives before, during, and a few weeks after the clinical trial. This work presents all the strategies used to promote contraception use and prevent pregnancy during the Alzheimer’s Prevention Initiative Autosomal-Dominant Alzheimer’s Disease (API ADAD) Colombia clinical trial. Two characteristics of this trial make it of special interest for closely monitoring contraception use. One is that the trial lasted more than 7 years, and the other is that participants could be carriers of the E280A PSEN1 mutation, leading to a mild cognitive impairment as early as their late 30s.

An individual medical evaluation to select the contraception method that best fits the volunteer was carried out during the screening visit, remitting to the gynecologist when necessary. All non-surgical contraception methods were supplied by the sponsor. Staff were trained on contraception counseling, correctly dispensing contraceptive drugs to volunteers, and identifying, reporting, and following up on pregnancies. Two comprehensive educational campaigns on contraception use were performed, and the intervention included all volunteers. In addition, volunteers were asked on an annual survey to evaluate the dispensing procedure. Finally, the effectiveness of these strategies was retrospectively evaluated, comparing by extrapolation the number of pregnancies presented throughout the trial with the General Fertility Rate in Colombia.

A total of 159 female volunteers were recruited. All strategies were implemented as planned, even during the COVID-19 contingency. Ten pregnancies occurred during the evaluation period (2015–2021). Two were planned; the rest were associated with a potential therapeutic failure or incorrect use of contraceptive methods for a contraceptive failure of 0.49% per year. Sixty percent of pregnancies led to an abortion, either miscarriage or therapeutic abortion. However, there was not enough data to associate the pregnancy outcome with the administration of the investigational product. Finally, we observed a lower fertility rate in women participating in the trial compared to the Colombian population.

The lower rates of contraceptive failure and the decrease in the incidence of pregnancies in women participating in the trial compared to the Colombian population across the 7 years of evaluation suggest that the strategies used in API ADAD Colombia were adequate and effective in addressing contraception use.