Incorrect use of the student t test in randomized trials of bilateral hip and knee arthroplasty patients

Methodological aspects of a randomized within-patient concurrent controlled design for clinical trials in spine surgery

INTRODUCTION

Randomized controlled trials are considered the highest level of evidence, but their feasibility in the surgical field is severely hampered by methodological and practical issues. Concurrent comparison between the experimental and control conditions within the same patient can be an effective strategy to mitigate some of these challenges and improve generalizability, mainly by the elimination of between-patient variability and reduction of the required sample size. This article aims (1) to describe the methodological aspects of a randomized within-patient controlled trial and (2) to quantify the added value of this design, based on a recently completed randomized within-patient controlled trial on bone grafts in instrumented lumbar posterolateral spinal fusion.

METHODS

Boundary conditions for the application of the randomized within-patient controlled trial design were identified. Between-patient variability was quantified by the intraclass correlation coefficient and concordance in the primary fusion outcome. Sample size, study duration and costs were compared with a classic randomized controlled trial design.

RESULTS

Boundary conditions include the concurrent application of the experimental and control conditions to identical but physically separated sites. Moreover, the outcome of interest should be local, uncorrelated and independently assessable. The spinal fusion outcomes within a patient were found to be more similar than between different patients (intraclass correlation coefficient 32% and concordance 64%), demonstrating a clear effect of patient-related factors. The randomized within-patient controlled trial design allowed a reduction of the sample size to one-third of a parallel-group randomized controlled trial, thereby halving the trial duration and costs.

CONCLUSION

When suitable, the randomized within-patient controlled trial is an efficient design that provides a solution to some of the considerable challenges of a classic randomized controlled trial in (spine) surgery. This design holds specific promise for efficacy studies of non-active bone grafts in instrumented posterolateral fusion surgery.

A simulation based method for assessing the statistical significance of logistic regression models after common variable selection procedures

Classification models can demonstrate apparent prediction accuracy even when there is no underlying relationship between the predictors and the response. Variable selection procedures can lead to false positive variable selections and overestimation of true model performance. A simulation study was conducted using logistic regression with forward stepwise, best subsets, and LASSO variable selection methods with varying total sample sizes (20, 50, 100, 200) and numbers of random noise predictor variables (3, 5, 10, 15, 20, 50). Using our critical values can help reduce needless follow-up on variables having no true association with the outcome.

Does bad inference drive out good?

The (mis)use of statistics in practice is widely debated, and a field where the debate is particularly active is medicine. Many scholars emphasize that a large proportion of published medical research contains statistical errors. It has been noted that top class journals like Nature Medicine and The New England Journal of Medicine publish a considerable proportion of papers that contain statistical errors and poorly document the application of statistical methods. This paper joins the debate on the (mis)use of statistics in the medical literature. Even though the validation process of a statistical result may be quite elusive, a careful assessment of underlying assumptions is central in medicine as well as in other fields where a statistical method is applied. Unfortunately, a careful assessment of underlying assumptions is missing in many papers, including those published in top class journals. In this paper, it is shown that nonparametric methods are good alternatives to parametric methods when the assumptions for the latter ones are not satisfied. A key point to solve the problem of the misuse of statistics in the medical literature is that all journals have their own statisticians to review the statistical method/analysis section in each submitted paper.

Evidence-based practice in nephrology: critical appraisal of nephrology clinical research: were the correct statistical tests used?

Nephrologists rely on valid clinical studies to inform their health care decisions. Knowledge of simple statistical principles equips the prudent nephrologist with the skills that allow him or her to critically evaluate clinical studies and to determine the validity of the results. Important in this process is knowing when certain statistical tests are used appropriately and if their application in interpreting research data will most likely lead to the most robust or valid conclusions. The research team bears the responsibility for determining the statistical analysis during the design phase of the study and subsequently for carrying out the appropriate analysis. This will ensure that bias is minimized and "valid" results are reported. We have summarized the important caveats and components in correctly choosing a statistical test with a series of tables. With this format, we wish to provide a tool for the nephrologist/researcher that he or she can use when required to decide if an appropriate statistical analysis plan was implemented for any particular study. We have included in these tables the types of statistical tests that might be used best for analysis of different types of comparisons on small and on larger patient samples.