Improving the Quality of EOS Clinical Research:A Step-by-Step Guide

Abstract

Conducting high-quality research in early onset scoliosis (EOS) is challenging, requiring trained biostatisticians who develop theoretical and statistical methods to analyze data in support of evidence-based decision-making. Epidemiologists provide empirical confirmation of disease processes, identifying factors that affect prognosis to guide the process toward clinical relevancy. Within each step in the study process, there are important principles that investigators can apply to improve the quality of research in EOS.One must ask an important research question that tests a focused, testable hypothesis. From this, create a study design with appropriate patient cohorts according to established inclusion/exclusion criteria. Specify the variables hypothesized to impact dependent measures of outcomes that reflect disease pathophysiology, treatment, and/or prevention. The data is to be analyzed with applicable statistical tests based upon power calculations with an estimate of the extent of variation in the dependent variables. Finally, we interpret results established on appropriately powered statistical tests in support/rejection of the hypothesis.These points, as relevant to early onset scoliosis (EOS) research, can be illustrated through an example of a retrospective de novo study identifying risk factors for increased mortality and decreased health related quality of life (HRQoL) in EOS patients with cerebral palsy (CP) undergoing spine surgery. Key Concepts:• There are many unanswered questions in the management of early onset scoliosis.• Impactful clinical research in this field and in all of pediatric orthopaedics requires a team of clinicians, epidemiologists, and biostatisticians, each contributing in their areas of expertise. • Determining a testable hypothesis is the first step of careful study design with clearly defined independent and dependent variables. • A variety of study designs can be considered, each with their own potential bias, confounding effects, chance, and risk for reverse causation. • A basic understanding of p-values, accuracy, precision, and relative risk is important to consider when determining if statistical findings have clinical importance.