Comparison of three sample size calculation methods for non-inferiority vaccine trials with multiple continuous co-primary endpoints

Seamless phase 2/3 design for trials with multiple co-primary endpoints using Bayesian predictive power

Seamless phase 2/3 design has become increasingly popular in clinical trials with a single endpoint. Trials that define success based on the achievement of all co-primary endpoints (CPEs) encounter the challenge of inflated type 2 error rates, often leading to an overly large sample size. To tackle this challenge, we introduced a seamless phase 2/3 design strategy that employs Bayesian predictive power (BPP) for futility monitoring and sample size re-estimation at interim analysis. The correlations among multiple CPEs are incorporated using a Dirichlet-multinomial distribution. An alternative approach based on conditional power (CP) was also discussed for comparison. A seamless phase 2/3 vaccine trial employing four binary endpoints under the non-inferior hypothesis serves as an example. Our results spotlight that, in scenarios with relatively small phase 2 sample sizes (e.g., 50 or 100 subjects), the BPP approach either outperforms or matches the CP approach in terms of overall power. Particularly, with n1 = 50 and ρ = 0, BPP showcases an overall power advantage over CP by as much as 8.54%. Furthermore, when the phase 2 stage enrolled more subjects (e.g., 150 or 200), especially with a phase 2 sample size of 200 and ρ = 0, the BPP approach evidences a peak difference of 5.76% in early stop probability over the CP approach, emphasizing its better efficiency in terminating futile trials. It's noteworthy that both BPP and CP methodologies maintained type 1 error rates under 2.5%. In conclusion, the integration of the Dirichlet-Multinominal model with the BPP approach offers improvement in certain scenarios over the CP approach for seamless phase 2/3 trials with multiple CPEs.

Cost, time savings and effectiveness of wearable devices for remote monitoring of patient rehabilitation after total knee arthroplasty: study protocol for a randomized controlled trial

BACKGROUND

Total knee arthroplasty (TKA) is a surgical procedure primarily used to treat patients with end-stage knee osteoarthritis (KOA). Postoperative physical exercise is a critical part of the overall treatment of KOA and can bring significant benefits to the patients' recovery. Wearable devices can monitor patients' exercise data and upload it to the physician's workstation. This allows the rehabilitation physician to make timely adjustments based on the patients' movement feedback, and the surgeon can be informed of the patients' functional status. Overall, this study aims to evaluate the effectiveness of using wearable monitoring devices for rehabilitation exercise after TKA, with a focus on cost, time savings, and patient outcomes.

METHOD/DESIGN

This is a single-center, single-blinded, parallel randomized controlled trial conducted at Xi'an Honghui Hospital, a regional orthopedic medical center. Eligible patients will be recruited to participate in the study, and baseline data collection and clinical assessments will be conducted at the time of admission. Using the principle of random allocation, recruited patients will be divided into either the experimental or control group. Both groups will undergo a standard, widely promoted rehabilitation program. The patients in the experimental group will wear equipment to detect and track mobility in the lower limbs. All patients will return to the outpatient clinic for follow-up assessments at 2 weeks, 12 weeks, and 24 weeks after discharge, where outcome indicators will be measured. The primary outcome will be the cost and time after discharge, while secondary outcomes will include the 6-min walk test (6MWT), range of motion (ROM), visual analog scale (VAS), American Knee Society Score (KSS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC).

DISCUSSION

We should encourage the adoption of novel, easy-to-use, supervised devices if they prove to be beneficial for patients in terms of cost, time, and effectiveness after TKA. This type of device is particularly important for people in remote rural areas, those with limited financial resources, and those who are reluctant to return to hospitals for follow-up care. Trial registration Chinese Clinical Trial Registry ChiCTR2300068418. Registered on 17 February 2023.

Sample size calculations for continuous outcomes in clinical nutrition

In nutrition research, sample size calculations for continuous outcomes are important for the planning phase of many randomized trials and could also be relevant for some observational studies such as cohort and cross-sectional studies. However, only little literature dedicated to this topic exists within nutritional science. This article reviews the most common methods for sample size calculations in nutrition research. Approximate formulas are used for explaining concepts and requirements and for working through examples from the literature. Sample size calculations for the various study designs, which are covered, may all be seen as extensions of the sample size calculation for the basic two-group comparison through the application of suitable scaling factors and, possibly, modification of the significance level. The latter is needed for sample size calculations for multi-group designs and designs involving multiple primary outcomes. Like cluster-randomized designs, these types of study designs may be more challenging than standard sample size calculations. In such non-standard scenarios, there may be a need for consulting a biostatistician. Finally, it should be stressed that there may be many ways to plan a study. The final sample size calculation provided for a grant applicant, study protocol, or publication will often not only depend on considerations and input information as described in this article but will also involve restrictions in terms of logistics and/or resources.

Hypertension is associated with antibody response and breakthrough infection in health care workers following vaccination with inactivated SARS-CoV-2

Several types of vaccines have been developed to prevent the coronavirus disease 2019 (COVID-19). It is important to understand whether demographic and clinical variables affect the effectiveness of various types of vaccines. This study analysed the association between demographic/clinical factors, antibody response and vaccine effectiveness in healthcare workers vaccinated with inactivated virus.

We enrolled 101 healthcare workers who received two doses of inactivated viral vaccine (CoronaVac). Blood samples were analysed at 1, 3, and 5 months after the second dose of vaccination. Data regarding demographic characteristics, medical histories, and clinical parameters were collected by interview and medical examination. In a separate retrospective study, we analysed the incidence of vaccine breakthrough infection on 2714 healthcare workers who received two doses of inactivated viral vaccine. Medical histories and demographic data were collected using a structured self-reported questionnaire.

We found that antibody titres markedly increased at 1 month after vaccination but gradually decreased at 3–5 months post-vaccination. We observed a significant association between age (≥40 years) and antibody level, whereas sex and body mass index (BMI) exhibited no effect on antibody titres. Amongst clinical variables analysed, high blood pressure and history of hypertension were significantly correlated with lower antibody titres. Consistently, we found a significant association in the retrospective study between hypertension and the incidence of breakthrough infection.

In conclusion, our results showed that hypertension is associated with lower antibody titres and breakthrough infection following COVID-19 vaccination. Thus, blood pressure control might be important to improve the efficacy of inactivated virus vaccine.