Expected Sample Size Research Articles

Sample size recalculation in three-stage clinical trials and its evaluation

BackgroundIn clinical trials, the determination of an adequate sample size is a challenging task, mainly due to the uncertainty about the value of the effect size and nuisance parameters. One method to deal with this uncertainty is a sample size recalculation. Thereby, an interim analysis is performed based on which the sample size for the remaining trial is adapted. With few exceptions, previous literature has only examined the potential of recalculation in two-stage trials.MethodsIn our research, we address sample size recalculation in three-stage trials, i.e. trials with two pre-planned interim analyses. We show how recalculation rules from two-stage trials can be modified to be applicable to three-stage trials. We also illustrate how a performance measure, recently suggested for two-stage trial recalculation (the conditional performance score) can be applied to evaluate recalculation rules in three-stage trials, and we describe performance evaluation in those trials from the global point of view. To assess the potential of recalculation in three-stage trials, we compare, in a simulation study, two-stage group sequential designs with three-stage group sequential designs as well as multiple three-stage designs with recalculation.ResultsWhile we observe a notable favorable effect in terms of power and expected sample size by using three-stage designs compared to two-stage designs, the benefits of recalculation rules appear less clear and are dependent on the performance measures applied.ConclusionsSample size recalculation is also applicable in three-stage designs. However, the extent to which recalculation brings benefits depends on which trial characteristics are most important to the applicants.

Pengaruh Gaya Kepemimpinan terhadap Kinerja Karyawan melalui Disiplin Kerja pada PT. Padma Soode Indonesia

This research aims to analyze leadership style on employee performance through work discipline at PT. Padma Soode Indonesia. With three variables, namely leadership style as the independent variable (X), employee performance as the mediating variable (Z), and finally work discipline as the commitment variable (Y). This form of research examines quantitatively with primary and secondary data used in this investigation. Direct observation, interviews, and questionnaires were used to collect primary data. The author uses library data from documentation, reference books and research journals as secondary data. Processing of data that is ready to be obtained must be carried out using the SPSS version 26 program. This research stage includes a planning stage, an implementation stage, and a writing stage. The population of this study was 300 employees (respondents) in the stamping division, who used simple random sampling techniques to take samples from the existing population by means of simple random sampling, which can be done using a lottery model, namely all members of the population are given serial numbers, Then random numbers were taken with the number of numbers taken, according to the expected sample size and using the Slovin formula, the population was 171 employees (respondents). The results of this research show that leadership style influences employee performance in a statistically tested manner. Leadership style's influence on work discipline has not been statistically tested. Work discipline has a statistically proven effect on employee performance. Leadership style influences employee performance through statistically proven work discipline. Apart from that, the results of this research for the leadership style variable, advice that can be given is that leaders must communicate frequently with their subordinates in order to build trust and relationships, increase involvement and productivity, and generate feedback and ideas. For the work discipline variable, the advice that can be given is that employees must communicate with all team members in order to increase collaboration and cooperation, increase collaboration and cooperation, increase coordination and efficiency, build strong relationships. For employee performance variables, suggestions that can be given are that employees must meet the given deadlines in order to increase efficiency and productivity, build trust and credibility, improve the quality of work, support the achievement of goals.

Cross-validated risk scores adaptive enrichment (CADEN) design

We propose a Cross-validated ADaptive ENrichment design (CADEN) in which a trial population is enriched with a subpopulation of patients who are predicted to benefit from the treatment more than an average patient (the sensitive group). This subpopulation is found using a risk score constructed from the baseline (potentially high-dimensional) information about patients. The design incorporates an early stopping rule for futility. Simulation studies are used to assess the properties of CADEN against the original (non-enrichment) cross-validated risk scores (CVRS) design which constructs a risk score at the end of the trial. We show that when there exists a sensitive group of patients, CADEN achieves a higher power and a reduction in the expected sample size compared to the CVRS design. We illustrate the application of the design in two real clinical trials. We conclude that the new design offers improved statistical efficiency over the existing non-enrichment method, as well as increased benefit to patients. The method has been implemented in an R package caden.

A multi-arm multi-stage platform design that allows preplanned addition of arms while still controlling the family-wise error.

There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms.

Using an early outcome as the sole source of information of interim decisions regarding treatment effect on a long-term endpoint: The non-Gaussian case.

In randomized clinical trials that use a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In such trials, an attractive option is to consider an interim analysis based solely on an early outcome that could be used to expedite the evaluation of treatment's efficacy. Garcia Barrado et al. (Pharm Stat. 2022; 21: 209-219) developed a methodology that allows introducing such an early interim analysis for the case when both the early outcome and the long-term endpoint are normally-distributed, continuous variables. We extend the methodology to any combination of the early-outcome and long-term-endpoint types. As an example, we consider the case of a binary outcome and a time-to-event endpoint. We further evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) of a trial with such an interim analysis in function of the properties of the early outcome as a surrogate for the long-term endpoint.

177Lu-edotreotide versus everolimus in patients with advanced neuroendocrine tumors of lung or thymic origin: The phase 3 randomized LEVEL, GETNE-T2217 trial.

TPS3177 Background: Everolimus is the only approved drug for the treatment of patients with neuroendocrine tumors (NETs) of lung and thymic origin, showing a median progression-free survival (PFS) of 11 months. Retrospective data for peptide receptor radionuclide therapy (PRRT) have demonstrated promising activity in somatostatin receptor (SSTR)-positive lung NETs. This study aims to compare the efficacy, safety, and patient-reported outcomes of 177Lu-edotreotide versus the standard of care everolimus in patients with advanced lung and thymic NETs. Methods: The LEVEL trial is a randomized, open-label, phase 3 international trial of 177Lu-edotreotide versus everolimus in patients with progressive, advanced, and well/moderately differentiated NETs of lung (typical/atypical) or thymic origin. Patients could be treatment naïve or have progressed (PD) on somatostatin analogues or ≤2 additional systemic treatments. Prior PRRT or mTOR inhibitors are not permitted. Eligible patients are randomly assigned 3:2 to 6 cycles of 177Lu-edotreotide (7.5±0.7 GBq / cycle) or to oral everolimus 10 mg once daily until PD or unacceptable toxicity. Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans are performed every 12 weeks until PD. Blood samples are analyzed at baseline, at 1st tumor assessment, and at PD for pharmacodynamic endpoints. Archival tumor tissue samples will be analyzed for ancillary studies. The primary endpoint is PFS according to RECIST v1.1. Secondary endpoints include overall response rate, duration of response, overall survival, safety, and quality of life (assessed through EORTC QLQ-C30). The expected sample size is 120 patients using a two-sided group sequential Lan-DeMets with O’Brien-Fleming-like boundaries test to demonstrate statistically significant risk reduction of 46.4% (HR= 0.536) in PFS between arms (α=0.05, β=0.2). The study has received institutional review board/ethics committee approval. Recruitment started in Oct 2023. Thirteen patients are already included. Clinical trial information: NCT05918302 .

Study on effect of anti-bribery management system to the quality of authority organization operational management in construction industry

Abstract Unethical behaviours are quite common in the construction industries. This study aims to assess the impact of unethical behaviours within organization, and the effectiveness of implementing Anti-Bribery Management System (ABMS) within organization. Among many of the government organizations, the main target respondents for this study were workers such as consultants and contractors who had worked with organization ’B’. The organization is called as Organization ’B’ as this topic research might be sensitive. In this study, distribution of questionnaires via online platform was used as a quantitative method where a total of 54 respondents out of 146 expected sample sizes were obtained. Majority of the respondents are agreeable towards the unethical behaviour will contribute to lower the quality of their works as the mean value is greater than 4.0. Plus, the implementing of ABMS are also shows positive impact in the quality of their works as the mean value is also greater than 4.0. From overall respondents, the top 3 variables for unethical behaviour are ’Hard Control Measures’, ’Conflict of Interest’, and ’Lack Supervision’. As for the implementation of ABMS, the top 3 variables are ’ABMS Qualification’, ’Leadership’, and ’Policies and Procedures’.

A comparison of alternative ranking methods in two-stage clinical trials with multiple interventions: An application to the anxiolysis for laceration repair in children trial.

Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care. We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (Pbest) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either Pbest or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either Pbest or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial. As the number of interventions increases, the SUCRA results in a higher predictive power compared with Pbest. Using Pbest results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using Pbest and the SUCRA, respectively. Thus, we chose Pbest as the ranking method for the ALICE trial. Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than Pbest. Future work should consider whether other ranking methods may also be relevant for clinical trial design.

Empirical meropenem versus piperacillin/tazobactam for adult patients with sepsis (EMPRESS) trial: Protocol.

Piperacillin/tazobactam may be associated with less favourable outcomes than carbapenems in patients with severe bacterial infections, but the certainty of evidence is low. The Empirical Meropenem versus Piperacillin/Tazobactam for Adult Patients with Sepsis (EMPRESS) trial is an investigator-initiated, international, parallel-group, randomised, open-label, adaptive clinical trial with an integrated feasibility phase. We will randomise adult, critically ill patients with sepsis to empirical treatment with meropenem or piperacillin/tazobactam for up to 30 days. The primary outcome is 30-day all-cause mortality. The secondary outcomes are serious adverse reactions within 30 days; isolation precautions due to resistant bacteria within 30 days; days alive without life support and days alive and out of hospital within 30 and 90 days; 90- and 180-day all-cause mortality and 180-day health-related quality of life. EMPRESS will use Bayesian statistical models with weak to somewhat sceptical neutral priors. Adaptive analyses will be conducted after follow-up of the primary outcome for the first 400 participants concludes and after every 300 subsequent participants, with adaptive stopping for superiority/inferiority and practical equivalence (absolute risk difference <2.5%-points) and response-adaptive randomisation. The expected sample sizes in scenarios with no, small or large differences are 5189, 5859 and 2570 participants, with maximum 14,000 participants and ≥99% probability of conclusiveness across all scenarios. EMPRESS will compare the effects of empirical meropenem against piperacillin/tazobactam in adult, critically ill patients with sepsis. Due to the pragmatic, adaptive design with high probability of conclusiveness, the trial results are expected to directly inform clinical practice.

Optimal double acceptance sampling plans based on truncated life tests for Tsallis q-exponential distributions

Optimal double sampling plans for lot acceptance based on truncated life tests are derived by minimizing the expected sample size when the lifetime variable follows a Tsallis q-exponential distribution. The proposed test plans significantly reduce the inspection effort with respect to the best single sampling schemes. Some tables and figures are presented to analyze the behavior of the suggested test plans. The results show that the proposed lot inspection scheme clearly outperforms the standard single sampling plan. A justification is provided using a comparative study for existing double sampling plans. Finally, several applications are provided for illustrative purposes.

Upstrapping to determine futility: predicting future outcomes nonparametrically from past data

BackgroundClinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring.MethodsUpstrapping is motivated by the case resampling bootstrap and involves repeatedly sampling with replacement from the interim data to simulate thousands of fully enrolled trials. The p-value is calculated for each upstrapped trial and the proportion of upstrapped trials for which the p-value criteria are met is compared with a pre-specified decision threshold. To evaluate the potential utility for upstrapping as a form of interim futility monitoring, we conducted a simulation study considering different sample sizes with several different proposed calibration strategies for the upstrap. We first compared trial rejection rates across a selection of threshold combinations to validate the upstrapping method. Then, we applied upstrapping methods to simulated clinical trial data, directly comparing their performance with more traditional alpha-spending and conditional power interim monitoring methods for futility.ResultsThe method validation demonstrated that upstrapping is much more likely to find evidence of futility in the null scenario than the alternative across a variety of simulations settings. Our three proposed approaches for calibration of the upstrap had different strengths depending on the stopping rules used. Compared to O’Brien-Fleming group sequential methods, upstrapped approaches had type I error rates that differed by at most 1.7% and expected sample size was 2–22% lower in the null scenario, while in the alternative scenario power fluctuated between 15.7% lower and 0.2% higher and expected sample size was 0–15% lower.ConclusionsIn this proof-of-concept simulation study, we evaluated the potential for upstrapping as a resampling-based method for futility monitoring in clinical trials. The trade-offs in expected sample size, power, and type I error rate control indicate that the upstrap can be calibrated to implement futility monitoring with varying degrees of aggressiveness and that performance similarities can be identified relative to considered alpha-spending and conditional power futility monitoring methods.

The Bayesian Group-Sequential Predictive Evidence Value Design for Phase II Clinical Trials with Binary Endpoints

AbstractIn clinical research, the initial efficacy of a new agent is typically assessed in a phase IIA study. Bayesian group-sequential designs are often based on predictive probability of trial success. In this paper, the novel Bayesian group-sequential predictive evidence value design is introduced, and we prove that the predictive probability approach is a special case of it. A comparison with Simon’s two-stage and competing Bayesian designs based on phase IIA cancer trials is provided. Results show that the novel design can improve operating characteristics such as the false-positive rate, probability of early stopping for futility and expected sample size of the trial. Given these advantages, the predictive evidence value design constitutes an important addition to the biostatistician’s toolbelt when planning a phase IIA trial the Bayesian way, in particular, when small sample sizes and a large probability for early termination under the null hypothesis are desired.

Optimal futility stopping boundaries for binary endpoints

BackgroundGroup sequential designs incorporating the option to stop for futility at the time point of an interim analysis can save time and resources. Thereby, the choice of the futility boundary importantly impacts the design’s resulting performance characteristics, including the power and probability to correctly or wrongly stop for futility. Several authors contributed to the topic of selecting good futility boundaries. For binary endpoints, Simon’s designs (Control Clin Trials 10:1–10, 1989) are commonly used two-stage designs for single-arm phase II studies incorporating futility stopping. However, Simon’s optimal design frequently yields an undesirably high probability of falsely declaring futility after the first stage, and in Simon’s minimax design often a high proportion of the planned sample size is already evaluated at the interim analysis leaving only limited benefit in case of an early stop.MethodsThis work focuses on the optimality criteria introduced by Schüler et al. (BMC Med Res Methodol 17:119, 2017) and extends their approach to binary endpoints in single-arm phase II studies. An algorithm for deriving optimized futility boundaries is introduced, and the performance of study designs implementing this concept of optimal futility boundaries is compared to the common Simon’s minimax and optimal designs, as well as modified versions of these designs by Kim et al. (Oncotarget 10:4255–61, 2019).ResultsThe introduced optimized futility boundaries aim to maximize the probability of correctly stopping for futility in case of small or opposite effects while also setting constraints on the time point of the interim analysis, the power loss, and the probability of stopping the study wrongly, i.e. stopping the study even though the treatment effect shows promise. Overall, the operating characteristics, such as maximum sample size and expected sample size, are comparable to those of the classical and modified Simon’s designs and sometimes better. Unlike Simon’s designs, which have binding stopping rules, the optimized futility boundaries proposed here are not adjusted to exhaust the full targeted nominal significance level and are thus still valid for non-binding applications.ConclusionsThe choice of the futility boundary and the time point of the interim analysis have a major impact on the properties of the study design. Therefore, they should be thoroughly investigated at the planning stage. The introduced method of selecting optimal futility boundaries provides a more flexible alternative to Simon’s designs with non-binding stopping rules. The probability of wrongly stopping for futility is minimized and the optimized futility boundaries don’t exhibit the unfavorable properties of an undesirably high probability of falsely declaring futility or a high proportion of the planned sample evaluated at the interim time point.

The use of real-world data for clinical investigation of effectiveness in drug development

ABSTRACT With the growing interest in leveraging real-world data (RWD) to support effectiveness evaluations for new indications, new target populations, and post-market performance, the United States Food and Drug Administration has published several guidance documents on RWD sources and real-world studies (RWS) to assist sponsors in generating credible real-world evidence (RWE). Meanwhile, the randomized controlled trial (RCT) remains the gold standard in drug evaluation. Along this line, we propose a hybrid two-stage adaptive design to evaluate effectiveness based on evidence from both RCT and RWS. At the first stage, a typical non-inferiority test is conducted using RCT data to test for not-ineffectiveness. Once not-ineffectiveness is established, the study proceeds to the second stage to conduct an RWS and test for effectiveness using integrated information from RCT and RWD. The composite likelihood approach is implemented as a down-weighing strategy to account for the impact of high variability in RWS population. An optimal sample size determination procedure for RCT and RWS is introduced, aiming to achieve the minimal expected sample size. Through extensive numerical study, the proposed design demonstrates the ability to control type I error inflation in most cases and consistently maintain statistical power above the desired level. In general, this RCT/RWS hybrid two-stage adaptive design is beneficial for effectiveness evaluations in drug development, especially for oncology and rare diseases.

Team Communication and Employee Performance in Commercial Banks in Tanzania

This study established the influence of team communication on employee performance in commercial banks in Tanzania. The study was anchored on the Katzenback and Smith model. The study adopted a descriptive survey research design and targeted all 39 operational commercial bank headquarters. The unit of observation comprised senior managers, managers, supervisors, and lower-level employees in each of the commercial banks under review thus making a total of 1480 of the targeted respondents. A simple random sampling technique was used to select the respondents; however, the expected sample size was 445 respondents with each senior manager, manager, supervisor, and lower-level employee contributing 30 percent of the total sample size. Both descriptive and inferential statistics were used to analyze the data with the assistance of statistical package for social sciences version 26. The study established that team communication influences employee performance in commercial banks. The study recommended that banks and many other organizations need to put in place mechanisms on how to evaluate -employee-based team performance rather than the rule thumb individual-based performance evaluation. The study has also pointed out strategic areas of importance into which managers in banks can look into the respective banks such as team communication that if well accommodated influence employee performance. In addition to other recommendations, it was proposed that, in light of these findings, work organizations evaluate their high-performance work system policy and activities by the degree to which they contribute directly to the accomplishment of the team’s strategic goals and objectives through the improvement of the employee performance.

Neurologijos srityje vartojamų vaistinių preparatų klinikinių tyrimų tendencijos Lietuvoje ir Europoje

Background. As the global burden of neurological diseases has been increasing, clinical trials have become more important. Yet, no systematic analysis of clinical trials of neurological medicinal products has been done in Europe and, specifically, in Lithuania. The aim of this study is to evaluate the tendencies of characteristics and registration dynamics of clinical trials of neurological medicines in Europe and Lithuania.Materials and methods. Analysis of data from the clinical trials register of the European Union drug regulatory authorities (EudraCT) has been performed. Applications of clinical trials of neurological medicines included in the register during the period between 2012–2021 have been reviewed. The following aspects have been evaluated: the phase of the trial, the sponsor status, the neurological condition (indication), the expected sample size of the trial, the trial design, and the number of member countries.Results. 2,242 clinical trials of neurological medicines have been registered in Europe during the period of 2012–2021, and they comprised 6.49% of all clinical trials registered in Europe. The annual registration rates did not change significantly during the period of 2012–2021 (R² = 0.039; p = 0.606). The majority of the trials (72.18%) were sponsored by commercial pharmaceutical companies. Most trials (31.73%) were registered as phase I trials. The majority of the trials (363 (17.20%)) were clinical trials for Alzheimer’s disease and other dementias. During the period of 2012–2021, there was a significant decrease in the annual registration rate of clinical trials for Alzheimer’s disease and other dementias (R² = 0.751, p = 0.001) and an increase of clinical trials for myoneural junction and muscle diseases and motor neuron disease (R² = 0.582, p = 0.010 and R² = 0.635, p = 0.006, accordingly). Commercial trials had a higher planned number of subjects (p = 0.014). Multiple member states participated in commercial trials more frequently (p &lt; 0.001). Fifty-five clinical trials of neurological medicines were registered in Lithuania during the period of 2012–2021, and they comprised 8.08% of all clinical trials registered in Lithuania. The annual registration rate has decreased significantly (R² = 0.401, p = 0.049). Almost all trials in Lithuania (53 out of 55) were sponsored by commercial companies. The majority of trials (23 (42%)) were trials for demyelinating diseases of the CNS.Conclusions. The annual registration rates of trials of neurological medicines remain stable in Europe while decreasing in Lithuania. Commercial clinical trials have more participants, and are more commonly conducted in multiple member states.

Optimal timing for an accelerated interim futility analysis incorporating real world data

BackgroundRandomized controlled trials include interim monitoring guidelines to stop early for safety, efficacy, or futility. Futility monitoring facilitates re-allocation of limited resources. However, conventional methods for interim futility monitoring require a trial to accrue nearly half of the outcome data to make a reliable early stopping decision, limiting its benefit. As early stopping for futility will not inflate type-I error, these analyses are an appealing venue for incorporating external data to improve efficiency. MethodsWe propose a Bayesian approach to futility monitoring leveraging real world data using Semi-Supervised MIXture Multi-source Exchangeability Models, which accounts for both measured and unmeasured differences between data sources. We implement futility monitoring using predictive probabilities and investigate the optimal timing with respect to the expected sample size under the null hypothesis. Because we only incorporate external data during the interim futility analysis the proposed design is not limited by type-I error inflation. ResultsWhen the external and trial data are exchangeable, the proposed method provides a roughly 70 person reduction in expected sample size under the null. Under scenarios where exchangeability does not hold, our approach still provides a 10–20 person reduction in expected sample size under the null with about 80% power. ConclusionsExternal data borrowing in interim futility monitoring is a promising venue to improve trial efficiency without type-I error inflation. Approaches that are acceptable to regulatory authorities and leverage the complementary strengths of real world and trial data are vital to more efficiently allocate limited resources amongst clinical trials.

Optimal estimation of reliability parameter for inverse Pareto distribution with application to insurance data

PurposeThe purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).Design/methodology/approachWe estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.FindingsWe state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.Originality/valueThis study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

Outcome of Olecranon Fracture Treated with Tension Band Wiring Versus Anatomical Locking Plate Fixation

Aim: Up to 10% of all upper limb fractures involve the olecranon, and this type of fracture is the most common osseous injury of the elbow joint. These show a bimodal distribution occurring in younger patients due to high-energy trauma and in elderly cohorts with low bone quality after low-energy falls. Objective: To compare functional outcome of tension band wiring versus anatomical locking plate fixation for reduction of olecranon fracture Materials and Methods: This randomized controlled trial study was conducted in the Department of Orthopedic, Lady Reading Hospital, Medical Teaching Institute Peshawar from December 2022 to June 2023. Non-probability consecutive sampling technique was used in this study. Sample size was calculated using WHO calculator keeping 95% confidence interval 80 % power of study, 10% absolute precision, mean Mayo elbow score was 84.0±9.3 in the TBW group and 88.3±9.1 in the locking plate fixation group (4). Expected sample size is 32 patients in each group, total 72 patients. Patients of both genders have age range from 20 to 70 year with Mayo type 2A olecranon fractures, more than 2 mm of joint displacement determined on X-ray elbow, and elbow extension loss determined on physical examination presented within 7 days of the injury were included in the study while Patients with open fracture and revision surgery were excluded from the study. Results: In this study age distribution among 72 patients was analyzed as n= 20-30 Years 17(27.4%) 31-40 Years 14(22.6%) 41-50.Years 11(17.7%) 51-70 Years 20(32.3%). Mean age was 47.1 Years with SD ± 2.87. Gender wise Distribution among 72 Patients was analyzed as Male were 31(50.0%) and female were 31(50.0%). Distribution of duration of disease among 72 patients were analysed as n= 1-2 weeks were 47(75.8%) and 3-4 weeks were 15(24.2%) Practical Implication: There are no established guidelines for the treatment of olecranon fracture in our institution. Most olecranon fractures are treated according to surgeon preferences and surgical experience of the surgeon. Previous studies showed variables results with no consensus. We will provide rationale basis for universal use of standard treatment of olecranon fracture. Conclusion: Although there were no statistically significant differences in clinical outcomes between the two groups, the ALP group had a higher proportion of any complication than the RP group. Keywords: Anatomical locking plate; complications; eyelet wire; olecranon fracture; tension band wiring.

Covariate adjustment in Bayesian adaptive randomized controlled trials.

In conventional randomized controlled trials, adjustment for baseline values of covariates known to be at least moderately associated with the outcome increases the power of the trial. Recent work has shown a particular benefit for more flexible frequentist designs, such as information adaptive and adaptive multi-arm designs. However, covariate adjustment has not been characterized within the more flexible Bayesian adaptive designs, despite their growing popularity. We focus on a subclass of these which allow for early stopping at an interim analysis given evidence of treatment superiority. We consider both collapsible and non-collapsible estimands and show how to obtain posterior samples of marginal estimands from adjusted analyses. We describe several estimands for three common outcome types. We perform a simulation study to assess the impact of covariate adjustment using a variety of adjustment models in several different scenarios. This is followed by a real-world application of the compared approaches to a COVID-19 trial with a binary endpoint. For all scenarios, it is shown that covariate adjustment increases power and the probability of stopping the trials early, and decreases the expected sample sizes as compared to unadjusted analyses.