Sample size calculation for a stepped wedge trial.

BACKGROUND: Stepped wedge trials (SWTs) can be considered as a variant of a clustered randomised trial, although in many ways they embed additional complications from the point of view of statistical design and analysis. While the literature is rich for standard parallel or clustered randomised clinical trials (CRTs), it is much less so for SWTs. The specific features of SWTs need to be addressed properly in the sample size calculations to ensure valid estimates of the intervention effect. METHODS: We critically review the available literature on analytical methods to perform sample size and power calculations in a SWT. In particular, we highlight the specific assumptions underlying currently used methods and comment on their validity and potential for extensions. Finally, we propose the use of simulation-based methods to overcome some of the limitations of analytical formulae. We performed a simulation exercise in which we compared simulation-based sample size computations with analytical methods and assessed the impact of varying the basic parameters to the resulting sample size/power, in the case of continuous and binary outcomes and assuming both cross-sectional data and the closed cohort design. RESULTS: We compared the sample size requirements for a SWT in comparison to CRTs based on comparable number of measurements in each cluster. In line with the existing literature, we found that when the level of correlation within the clusters is relatively high (for example, greater than 0.1), the SWT requires a smaller number of clusters. For low values of the intracluster correlation, the two designs produce more similar requirements in terms of total number of clusters. We validated our simulation-based approach and compared the results of sample size calculations to analytical methods; the simulation-based procedures perform well, producing results that are extremely similar to the analytical methods. We found that usually the SWT is relatively insensitive to variations in the intracluster correlation, and that failure to account for a potential time effect will artificially and grossly overestimate the power of a study. CONCLUSIONS: We provide a framework for handling the sample size and power calculations of a SWT and suggest that simulation-based procedures may be more effective, especially in dealing with the specific features of the study at hand. In selected situations and depending on the level of intracluster correlation and the cluster size, SWTs may be more efficient than comparable CRTs. However, the decision about the design to be implemented will be based on a wide range of considerations, including the cost associated with the number of clusters, number of measurements and the trial duration

Penalized Regression Methods With Modified Cross-Validation and Bootstrap Tuning Produce Better Prediction Models

Risk prediction models fitted using maximum likelihood estimation (MLE) are often overfitted resulting in predictions that are too extreme and a calibration slope (CS) less than 1. Penalized methods, such as Ridge and Lasso, have been suggested as a solution to this problem as they tend to shrink regression coefficients toward zero, resulting in predictions closer to the average. The amount of shrinkage is regulated by a tuning parameter, (Formula presented.) commonly selected via cross-validation (“standard tuning”). Though penalized methods have been found to improve calibration on average, they often over-shrink and exhibit large variability in the selected (Formula presented.) and hence the CS. This is a problem, particularly for small sample sizes, but also when using sample sizes recommended to control overfitting. We consider whether these problems are partly due to selecting (Formula presented.) using cross-validation with “training” datasets of reduced size compared to the original development sample, resulting in an over-estimation of (Formula presented.) and, hence, excessive shrinkage. We propose a modified cross-validation tuning method (“modified tuning”), which estimates (Formula presented.) from a pseudo-development dataset obtained via bootstrapping from the original dataset, albeit of larger size, such that the resulting cross-validation training datasets are of the same size as the original dataset. Modified tuning can be easily implemented in standard software and is closely related to bootstrap selection of the tuning parameter (“bootstrap tuning”). We evaluated modified and bootstrap tuning for Ridge and Lasso in simulated and real data using recommended sample sizes, and sizes slightly lower and higher. They substantially improved the selection of (Formula presented.), resulting in improved CS compared to the standard tuning method. They also improved predictions compared to MLE

Multi-domain quantitative recovery following Radical Cystectomy for patients within the iROC (Robot Assisted Radical Cystectomy with intracorporeal urinary diversion versus Open Radical Cystectomy) Randomised Controlled Trial: The first 30 patients

Many patients develop complications after radical cystectomy (RC) [1]. Reductions in morbidity have occurred through centralisation and technical improvements [2], and perhaps through robot-assisted RC (RARC). Whilst RARC is gaining popularity, there are concerns about oncological safety [3] and extracorporeal reconstruction [4], and randomised controlled trials (RCTs) find little difference [5]. We are conducting a prospective RCT comparing open RC and RARC with mandated intracorporeal reconstruction (Robot-assisted Radical Cystectomy with intracorporeal urinary diversion versus Open Radical Cystectomy [iROC] trial) [6]

The use of basic fibroblast growth factor to improve vocal function: A systematic review and meta-analysis

OBJECTIVES: This systematic review and meta-analysis examines if intralaryngeal injection of basic fibroblast growth factor 2 (FGF2) can improve voice outcomes in those with vocal disability. DESIGN: A Systematic review of original human studies reporting voice outcomes following intra-laryngeal injection of basic fibroblast growth factor 2 in those with vocal dysfunction. Databases searched were Medline (1946-July 2022), Embase (1947-July 2022), Cochrane database and Google Scholar. SETTING: Secondary or tertiary care centres that undertook the management of voice pathology Hospital. PARTICIPANTS: Inclusion criteria were original human studies reporting voice outcome measurements following intralaryngeal injection of FGF2 to treat vocal fold atrophy, vocal fold scarring, vocal fold sulcus or vocal fold palsy. Articles not written in English, studies that did not include human subjects and studies where voice outcome measures were not recorded before and after FGF2 injection were excluded from the review. MAIN OUTCOME MEASURES: The primary outcome measure was maximum phonation time. Secondary outcome measures included acoustic analysis, glottic closure, mucosal wave formation, voice handicap index and GRBAS scale. RESULTS: Fourteen articles were included out of a search of 1023 and one article was included from scanning reference lists. All studies had a single arm design without control groups. Conditions treated were vocal fold atrophy (n = 186), vocal cord paralysis (n = 74), vocal fold fibrosis (n = 74) and vocal fold sulcus (n = 56). A meta-analysis of six articles reporting on the use of FGF2 in patients with vocal fold atrophy showed a significant increase of mean maximum phonation time of 5.2 s (95% CI: 3.4-7.0) at 3-6 months following injection. A significant improvement in maximum phonation time, voice handicap index and glottic closure was found following injection in most studies assessed. No major adverse events were reported following injection. CONCLUSIONS: To date, intralaryngeal injection of basic FGF2 appears to be safe and it may be able to improve voice outcomes in those with vocal dysfunction, especially vocal fold atrophy. Randomised controlled trials are needed to further evaluate efficacy and support the wider use of this therapy

The Value of Blood-Based Measures of Liver Function and Urate in Lung Cancer Risk Prediction: A Cohort Study and Health Economic Analysis

BACKGROUND: Several studies have reported associations between low-cost blood-based measurements and lung cancer but their role in risk prediction is unclear. We examined the value of expanding lung cancer risk models for targeting low-dose computed tomography (LDCT) to include blood measurements of liver function and urate. METHODS: We analysed a cohort of 388,199 UK Biobank participants with 1,873 events and calculated the c-index and fraction of new information (FNI) for models expanded to include combinations of blood measurements, lung function (forced expiratory volume in 1 second - FEV1), alcohol status and waist circumference. We calculated the hypothetical cost per lung cancer case detected by LDCT for different scenarios using a threshold of ≥ 1.51% risk at 6 years. RESULTS: The c-index was 0.805 (95%CI:0.794-0.816) for the model containing conventional predictors. Expanding to include blood measurements increased the c-index to 0.815 (95%CI: 0.804-0.826;p<0.0001;FNI:0.06). Expanding to include FEV1, alcohol status, and waist circumference increased the c-index to 0.811 (95%CI:0.800-0.822;p<0.0001;FNI:0.04). The c-index for the fully expanded model containing all variables was 0.819 (95%CI:0.808-0.830; p<0.0001;FNI:0.09). Model expansion had a greater impact on the c-index and FNI for people with a history of smoking cigarettes relative to the full cohort. Compared with the conventional risk model, the expanded models reduced the number of participants meeting the criteria for LDCT screening by 15-21%, and lung cancer cases detected by 7-8%. The additional cost per lung cancer case detected relative to the conventional model was £1,018 for the addition of blood tests and £9,775 for the fully expanded model. CONCLUSION: Blood measurements of liver function and urate improved lung cancer risk prediction compared with a model containing conventional risk factors. However, there was no evidence that model expansion would improve the cost per lung cancer case detected in UK health care settings

A note on obtaining correct marginal predictions from a random intercepts model for binary outcomes.

BACKGROUND: Clustered data with binary outcomes are often analysed using random intercepts models or generalised estimating equations (GEE) resulting in cluster-specific or 'population-average' inference, respectively. METHODS: When a random effects model is fitted to clustered data, predictions may be produced for a member of an existing cluster by using estimates of the fixed effects (regression coefficients) and the random effect for the cluster (conditional risk calculation), or for a member of a new cluster (marginal risk calculation). We focus on the second. Marginal risk calculation from a random effects model is obtained by integrating over the distribution of random effects. However, in practice marginal risks are often obtained, incorrectly, using only estimates of the fixed effects (i.e. by effectively setting the random effects to zero). We compare these two approaches to marginal risk calculation in terms of model calibration. RESULTS: In simulation studies, it has been seen that use of the incorrect marginal risk calculation from random effects models results in poorly calibrated overall marginal predictions (calibration slope <1 and calibration in the large ≠ 0) with mis-calibration becoming worse with higher degrees of clustering. We clarify that this was due to the incorrect calculation of marginal predictions from a random intercepts model and explain intuitively why this approach is incorrect. We show via simulation that the correct calculation of marginal risks from a random intercepts model results in predictions with excellent calibration. CONCLUSION: The logistic random intercepts model can be used to obtain valid marginal predictions by integrating over the distribution of random effects