We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.

A systematic review of prospective cohort studies in low back pain. To evaluate the evidence implicating psychological factors in the development of chronicity in low back pain. The biopsychosocial model is gaining acceptance in low back pain, and has provided a basis for screening measurements, guidelines and interventions; however, to date, the unique contribution of psychological factors in the transition from an acute presentation to chronicity has not been rigorously assessed. A systematic literature search was followed by the application of three sets of criteria to each study: methodologic quality, quality of measurement of psychological factors, and quality of statistical analysis. Two reviewers blindly coded each study, followed by independent assessment by a statistician. Studies were divided into three environments: primary care settings, pain clinics, and workplace. Twenty-five publications (18 cohorts) included psychological factors at baseline. Six of these met acceptability criteria for methodology, psychological measurement, and statistical analysis. Increased risk of chronicity (persisting symptoms and/or disability) from psychological distress/depressive mood and, to a lesser extent, somatization emerged as the main findings. Acceptable evidence generally was not found for other psychological factors, although weak support emerged for the role of catastrophizing as a coping strategy. Psychological factors (notably distress, depressive mood, and somatization) are implicated in the transition to chronic low back pain. The development and testing of clinical interventions specifically targeting these factors is indicated. In view of the importance attributed to other psychological factors (particularly coping strategies and fear avoidance) there is a need to clarify their role in back-related disability through rigorous prospective studies.

One conceptualization of meta-analysis is that studies within the meta-analysis are sampled from populations with mean effect sizes that vary (random-effects models). The consequences of not applying such models and the comparison of different methods have been hotly debated. A Monte Carlo study compared the efficacy of Hedges and Vevea's random-effects methods of meta-analysis with Hunter and Schmidt's, over a wide range of conditions, as the variability in population correlations increases. (a) The Hunter-Schmidt method produced estimates of the average correlation with the least error, although estimates from both methods were very accurate; (b) confidence intervals from Hunter and Schmidt's method were always slightly too narrow but became more accurate than those from Hedges and Vevea's method as the number of studies included in the meta-analysis, the size of the true correlation, and the variability of correlations increased; and (c) the study weights did not explain the differences between the methods.