Parametric methods outperformed non-parametric methods in comparisons of discrete numerical variables

<p>Abstract</p> <p>Background</p> <p>The number of events per individual is a widely reported variable in medical research papers. Such variables are the most common representation of the general variable type called discrete numerical. There is currently no consensus on how to compare and present such variables, and recommendations are lacking. The objective of this paper is to present recommendations for analysis and presentation of results for discrete numerical variables.</p> <p>Methods</p> <p>Two simulation studies were used to investigate the performance of hypothesis tests and confidence interval methods for variables with outcomes {0, 1, 2}, {0, 1, 2, 3}, {0, 1, 2, 3, 4}, and {0, 1, 2, 3, 4, 5}, using the difference between the means as an effect measure.</p> <p>Results</p> <p>The Welch U test (the T test with adjustment for unequal variances) and its associated confidence interval performed well for almost all situations considered. The Brunner-Munzel test also performed well, except for small sample sizes (10 in each group). The ordinary T test, the Wilcoxon-Mann-Whitney test, the percentile bootstrap interval, and the bootstrap-<it>t </it>interval did not perform satisfactorily.</p> <p>Conclusions</p> <p>The difference between the means is an appropriate effect measure for comparing two independent discrete numerical variables that has both lower and upper bounds. To analyze this problem, we encourage more frequent use of parametric hypothesis tests and confidence intervals.</p

Calculating unreported confidence intervals for paired data

<p>Abstract</p> <p>Background</p> <p>Confidence intervals (or associated standard errors) facilitate assessment of the practical importance of the findings of a health study, and their incorporation into a meta-analysis. For paired design studies, these items are often not reported. Since the descriptive statistics for such studies are usually presented in the same way as for unpaired designs, direct computation of the standard error is not possible without additional information.</p> <p>Methods</p> <p>Elementary, well-known relationships between standard errors and <it>p</it>-values were used to develop computation schemes for paired mean difference, risk difference, risk ratio and odds ratio.</p> <p>Results</p> <p>Unreported confidence intervals for large sample paired binary and numeric data can be computed fairly accurately using simple methods provided the <it>p</it>-value is given. In the case of paired binary data, the design based 2 × 2 table can be reconstructed as well.</p> <p>Conclusions</p> <p>Our results will facilitate appropriate interpretation of paired design studies, and their incorporation into meta-analyses.</p

Perceived acceptability of wearable devices for the treatment of mental health problems

OBJECTIVE: This study examined the potential acceptability of wearable devices (e.g., smart headbands, wristbands, and watches) aimed at treating mental health disorders, relative to conventional approaches. METHODS: A questionnaire assessed perceptions of wearable and nonwearable treatments, along with demographic and psychological information. Respondents (N = 427) were adults from a community sample (Mage  = 44.6, SDage  = 15.3) which included current (30.2%) and former (53.9%) mental health help-seekers. RESULTS: Perceived effectiveness of wearables was a strong predictor of interest in using them as adjuncts to talk therapies, or as an alternative to self-help options (e.g., smartphone applications). Devices were more appealing to those with negative evaluations of psychological therapy and less experience in help-seeking. CONCLUSIONS: Interest in using wearable devices was strong, particularly when devices were seen as effective. Clients with negative attitudes to conventional therapies may be more responsive to using wearable devices as a less directive treatment approach.Hugh Hunkin, Daniel L. King, Ian T. Zaja

Outcome based subgroup analysis: a neglected concern

A subgroup of clinical trial subjects identified by baseline characteristics is a proper subgroup while a subgroup determined by post randomization events or measures is an improper subgroup. Both types of subgroups are often analyzed in clinical trial papers. Yet, the extensive scrutiny of subgroup analyses has almost exclusively attended to the former. The analysis of improper subgroups thereby not only flourishes in numerous disguised ways but also does so without a corresponding awareness of its pitfalls. Comparisons of the grade of angina in a heart disease trial, for example, usually include only the survivors. This paper highlights some of the distinct ways in which outcome based subgroup analysis occurs, describes the hazards associated with it, and proposes a simple alternative approach to counter its analytic bias. Data from six published trials show that outcome based subgroup analysis, like proper subgroup analysis, may be performed in a post-hoc fashion, overdone, selectively reported, and over interpreted. Six hypothetical trial scenarios illustrate the forms of hidden bias related to it. That bias can, however, be addressed by assigning clinically appropriate scores to the usually excluded subjects and performing an analysis that includes all the randomized subjects. A greater level of awareness about the practice and pitfalls of outcome based subgroup analysis is needed. When required, such an analysis should maintain the integrity of randomization. This issue needs greater practical and methodologic attention than has been accorded to it thus far

Device-measured physical activity, adiposity and mortality: a harmonised meta-analysis of eight prospective cohort studies.

BACKGROUND: The joint associations of total and intensity-specific physical activity with obesity in relation to all-cause mortality risk are unclear. METHODS: We included 34 492 adults (72% women, median age 62.1 years, 2034 deaths during follow-up) in a harmonised meta-analysis of eight population-based prospective cohort studies with mean follow-up ranging from 6.0 to 14.5 years. Standard body mass index categories were cross-classified with sample tertiles of device-measured total, light-to-vigorous and moderate-to-vigorous physical activity and sedentary time. In five cohorts with waist circumference available, high and low waist circumference was combined with tertiles of moderate-to-vigorous physical activity. RESULTS: There was an inverse dose-response relationship between higher levels of total and intensity-specific physical activity and mortality risk in those who were normal weight and overweight. In individuals with obesity, the inverse dose-response relationship was only observed for total physical activity. Similarly, lower levels of sedentary time were associated with lower mortality risk in normal weight and overweight individuals but there was no association between sedentary time and risk of mortality in those who were obese. Compared with the obese-low total physical activity reference, the HRs were 0.59 (95% CI 0.44 to 0.79) for normal weight-high total activity and 0.67 (95% CI 0.48 to 0.94) for obese-high total activity. In contrast, normal weight-low total physical activity was associated with a higher risk of mortality compared with the obese-low total physical activity reference (1.28; 95% CI 0.99 to 1.67). CONCLUSIONS: Higher levels of physical activity were associated with lower risk of mortality irrespective of weight status. Compared with obesity-low physical activity, there was no survival benefit of being normal weight if physical activity levels were low

Approximate confidence intervals for a linear combination of binomial proportions: A new variant

We propose a new adjustment for constructing an improved version of theWald interval for linear combinations of binomial proportions, which addresses the presence of extremal samples. A comparative simulation study was carried out to investigate the performance of this new variant with respect to the exact coverage probability, expected interval length, and mesial and distal noncoverage probabilities. Additionally, we discuss the application of a criterion for interpreting interval location in the case of small samples and/or in situations in which extremal observations exist. The confidence intervals obtained from the new variant performed better for some evaluation measures

Joint associations of accelerometer measured physical activity and sedentary time with all-cause mortality: a harmonised meta-analysis in more than 44 000 middle-aged and older individuals

Funder: National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care East MidlandsFunder: National Institute on AgingFunder: Stockholms Läns Landsting; FundRef: http://dx.doi.org/10.13039/501100004348Funder: Norwegian Directorate for Public HealthFunder: Centrum for Idrottsforskning; FundRef: http://dx.doi.org/10.13039/501100005350Funder: The Coca-Cola CompanyObjectives: To examine the joint associations of accelerometer-measured physical activity and sedentary time with all-cause mortality. Methods: We conducted a harmonised meta-analysis including nine prospective cohort studies from four countries. 44 370 men and women were followed for 4.0 to 14.5 years during which 3451 participants died (7.8% mortality rate). Associations between different combinations of moderate-to-vigorous intensity physical activity (MVPA) and sedentary time were analysed at study level using Cox proportional hazards regression analysis and summarised using random effects meta-analysis. Results: Across cohorts, the average time spent sedentary ranged from 8.5 hours/day to 10.5 hours/day and 8 min/day to 35 min/day for MVPA. Compared with the referent group (highest physical activity/lowest sedentary time), the risk of death increased with lower levels of MVPA and greater amounts of sedentary time. Among those in the highest third of MVPA, the risk of death was not statistically different from the referent for those in the middle (16%; 95% CI 0.87% to 1.54%) and highest (40%; 95% CI 0.87% to 2.26%) thirds of sedentary time. Those in the lowest third of MVPA had a greater risk of death in all combinations with sedentary time; 65% (95% CI 1.25% to 2.19%), 65% (95% CI 1.24% to 2.21%) and 263% (95% CI 1.93% to 3.57%), respectively. Conclusion: Higher sedentary time is associated with higher mortality in less active individuals when measured by accelerometry. About 30–40 min of MVPA per day attenuate the association between sedentary time and risk of death, which is lower than previous estimates from self-reported data