Statistical Primer for Athletic Trainers: The Difference Between Statistical and Clinical Meaningfulness

Limb Dominance Effects on Seated Single-Arm Shot-Put Limb Symmetry Indices Following Shoulder Rehabilitation

CONTEXT

Typically, most clinical return to activity guidelines recommend that an injured shoulder achieve a 90% to 100% functional performance test limb symmetry index (LSI); however, as previous research demonstrated a 103% to 111% dominant limb bias in seated single-arm shot-put test (SSASPT) performance, the typical criteria might not be appropriate for interpreting SSASPT LSI. Thus, the current objective was to evaluate SSASP LSI differences between dominant and nondominant involved shoulders and to determine how many patients met the suggested 90% to 100% LSI criteria, as well as the 103% for dominant (89% for nondominant) normative SSASPT threshold reported in the literature, at the time of discharge.

DESIGN

Cross-sectional.

METHODS

Patients with shoulder injury or surgery (n = 78) completed the SSASPT at the time of discharge from rehabilitation and were grouped according to whether the involved shoulder was the dominant (n = 42) or nondominant (n = 32) limb. LSI (involved/uninvolved × 100) was computed from the average of 3 SSASPT trial distances completed with each limb.

RESULTS

The LSI for the nondominant involved group (88.9% [12.4%]) was significantly less (confidence intervalDiff, -12.1% to -22.1%) than the dominant involved group (106.0% [9.3%]). While 95.2% of patients in the dominant involved group exhibited LSI > 90%, only 43.8% of patients in the nondominant involved group attained LSI > 90%. Across the entire cohort, the odds of a nondominant involved LSI being below the respective SSASPT normative range were 2.04 (95% confidence interval, 0.80-5.21) times higher than the odds of a dominant involved LSI being below the normative range.

CONCLUSIONS

Patients with dominant limb involvement exhibited higher LSI than patients with nondominant limb involvement at discharge from rehabilitation. Particularly when the nondominant shoulder is involved, these results suggest that patients with shoulder injury and surgery may require longer rehabilitation to attain higher levels of upper-extremity function.

Comparative radiological outcomes and complications of sacral-2-alar iliac screw versus iliac screw for sacropelvic fixation

PURPOSE

To compare the outcomes of sacropelvic fixation (SPF) using sacral-2-alar iliac (S2AI) screw with SPF using iliac screw (IS).

METHODS

A comprehensive search of PubMed, EMBASE, Cochrane Central Register of Controlled Trials and Scopus was performed for comparative studies between S2AI and IS for SPF. Two independent investigators selected qualified studies and extracted data indispensably. With 95% confidence intervals (CI), the odds ratio (OR) was applied to dichotomous outcomes and standardized mean difference (SMD) was applied to continuous outcomes for each item.

RESULTS

We included data from thirteen studies involving 722 patients (S2AI, 357 patients; IS, 365 patients). In the pediatric population, the S2AI group had a smaller pelvic obliquity (PO) than the IS group at final follow-up (SMD, - 0.38; 95% CI, - 0.72 to - 0.04). Patients who underwent S2AI screws showed reduced rates of re-operation (S2AI, 13%; IS, 28%), implant failure (S2AI, 12%; IS, 26%) [screw loosening (S2AI, 8%; IS, 20%); screw breakage (S2AI, 2%; IS, 12%)], implant prominence (S2AI, 2%; IS, 14%), pseudarthrosis (S2AI, 3%; IS, 15%), wound infection (S2AI, 8%; IS, 22%) and less blood loss (S2AI, 2035.4 ml; IS, 2708.4 ml).

CONCLUSION

Radiological outcomes indicate an effective maintenance of the correction and arrest of progression of deformity by S2AI, which is equal or better than IS. SPF with S2AI screw has obviously lower incidence of postoperative complications and less blood loss. Given these advantages, the S2AI screw seems to be a beneficial alternative to IS.

Suprapubic Versus Urethral Catheter for Urinary Drainage After Robot-Assisted Radical Prostatectomy

PURPOSE OF REVIEW

To review the evidence regarding the usage of suprapubic tube (SPT) versus indwelling urethral catheter (IUC) after robot-assisted radical prostatectomy (RARP).

RECENT FINDINGS

Available data on the use of SPT for urinary drainage after RARP is somewhat limited mostly because of the variations of study designs and non-standardized outcomes. Although it may provide some mild benefit in terms of catheter-related pain and discomfort, the benefit seems not to be clinically significant. The evidence in the literature so far does not support routine usage of SPT as the primary urinary drainage method after RARP. Further higher-quality studies that can show clinically significant advantages over IUC are still needed to justify its usage.

Statistical Primer for Athletic Trainers: Understanding the Role of Statistical Power in Comparative Athletic Training Research

OBJECTIVE

  To describe the concept of statistical power as related to comparative interventions and how various factors, including sample size, affect statistical power.

BACKGROUND

  Having a sufficiently sized sample for a study is necessary for an investigation to demonstrate that an effective treatment is statistically superior. Many researchers fail to conduct and report a priori sample-size estimates, which then makes it difficult to interpret nonsignificant results and causes the clinician to question the planning of the research design.

DESCRIPTION

  Statistical power is the probability of statistically detecting a treatment effect when one truly exists. The α level, a measure of differences between groups, the variability of the data, and the sample size all affect statistical power.

RECOMMENDATIONS

  Authors should conduct and provide the results of a priori sample-size estimations in the literature. This will assist clinicians in determining whether the lack of a statistically significant treatment effect is due to an underpowered study or to a treatment's actually having no effect.

Principles of Statistics: What the Sports Medicine Professional Needs to Know

Understanding the results and statistics reported in original research remains a large challenge for many sports medicine practitioners and, in turn, may be among one of the biggest barriers to integrating research into sports medicine practice. The purpose of this article is to provide minimal essentials a sports medicine practitioner needs to know about interpreting statistics and research results to facilitate the incorporation of the latest evidence into practice. Topics covered include the difference between statistical significance and clinical meaningfulness; effect sizes and confidence intervals; reliability statistics, including the minimal detectable difference and minimal important difference; and statistical power.

Statistical Primer for Athletic Trainers: The Essentials of Understanding Measures of Reliability and Minimal Important Change

OBJECTIVE

  To describe the concepts of measurement reliability and minimal important change.

BACKGROUND

  All measurements have some magnitude of error. Because clinical practice involves measurement, clinicians need to understand measurement reliability. The reliability of an instrument is integral in determining if a change in patient status is meaningful.

DESCRIPTION

  Measurement reliability is the extent to which a test result is consistent and free of error. Three perspectives of reliability-relative reliability, systematic bias, and absolute reliability-are often reported. However, absolute reliability statistics, such as the minimal detectable difference, are most relevant to clinicians because they provide an expected error estimate. The minimal important difference is the smallest change in a treatment outcome that the patient would identify as important.

RECOMMENDATIONS

  Clinicians should use absolute reliability characteristics, preferably the minimal detectable difference, to determine the extent of error around a patient's measurement. The minimal detectable difference, coupled with an appropriately estimated minimal important difference, can assist the practitioner in identifying clinically meaningful changes in patients.

Statistical Primer for Athletic Trainers: Using Confidence Intervals and Effect Sizes to Evaluate Clinical Meaningfulness

OBJECTIVE

To describe confidence intervals (CIs) and effect sizes and provide practical examples to assist clinicians in assessing clinical meaningfulness.

BACKGROUND

As discussed in our first article in 2015, which addressed the difference between statistical significance and clinical meaningfulness, evaluating the clinical meaningfulness of a research study remains a challenge to many readers. In this paper, we will build on this topic by examining CIs and effect sizes.

DESCRIPTION

A CI is a range estimated from sample data (the data we collect) that is likely to include the population parameter (value) of interest. Conceptually, this constitutes the lower and upper limits of the sample data, which would likely include, for example, the mean from the unknown population. An effect size is the magnitude of difference between 2 means. When a statistically significant difference exists between 2 means, effect size is used to describe how large or small that difference actually is. Confidence intervals and effect sizes enhance the practical interpretation of research results.

RECOMMENDATIONS

Along with statistical significance, the CI and effect size can assist practitioners in better understanding the clinical meaningfulness of a research study.

A Statistical Method and Tool to Account for Indirect Calorimetry Differential Measurement Error in a Single-Subject Analysis

Indirect calorimetry and oxygen consumption (VO₂) are accepted tools in human physiology research. It has been shown that indirect calorimetry systems exhibit differential measurement error, where the error of a device is systematically different depending on the volume of gas flow. Moreover, systems commonly report multiple decimal places of precision, giving the clinician a false sense of device accuracy. The purpose of this manuscript is to demonstrate the use of a novel statistical tool which models the reliability of two specific indirect calorimetry systems, Douglas bag and Parvomedics 2400 TrueOne, as univariate normal distributions and implements the distribution overlapping coefficient to determine the likelihood that two VO₂ measures are the same. A command line implementation of the tool is available for the R programming language as well as a web-based graphical user interface (GUI). This tool is valuable for clinicians performing a single-subject analysis as well as researchers interested in determining if their observed differences exceed the error of the device.