Diagnostic uncertainty and recall bias in chronic low back pain

Patients’ beliefs about the origin of their pain and their cognitive processing of pain-related information have both been shown to be associated with poorer prognosis in low back pain (LBP), but the relationship between specific beliefs and specific cognitive processes is not known. The aim of this study was to examine the relationship between diagnostic uncertainty and recall bias in 2 groups of chronic LBP patients, those who were certain about their diagnosis and those who believed that their pain was due to an undiagnosed problem. Patients (N = 68) endorsed and subsequently recalled pain, illness, depression, and neutral stimuli. They also provided measures of pain, diagnostic status, mood, and disability. Both groups exhibited a recall bias for pain stimuli, but only the group with diagnostic uncertainty also displayed a recall bias for illness-related stimuli. This bias remained after controlling for depression and disability. Sensitivity analyses using grouping by diagnosis/explanation received supported these findings. Higher levels of depression and disability were found in the group with diagnostic uncertainty, but levels of pain intensity did not differ between the groups. Although the methodology does not provide information on causality, the results provide evidence for a relationship between diagnostic uncertainty and recall bias for negative health-related stimuli in chronic LBP patients.     

ACCOUNTING FOR DROPOUT BIAS USING MIXED-EFFECTS MODELS

Treatment effects are often evaluated by comparing change over time in outcome measures. However, valid analyses of longitudinal data can be problematic when subjects discontinue (dropout) prior to completing the study. This study assessed the merits of likelihood-based repeated measures analyses (MMRM) compared with fixed-effects analysis of variance where missing values were imputed using the last observation carried forward approach (LOCF) in accounting for dropout bias. Comparisons were made in simulated data and in data from a randomized clinical trial. Subject dropout was introduced in the simulated data to generate ignorable and nonignorable missingness. Estimates of treatment group differences in mean change from baseline to endpoint from MMRM were, on average, markedly closer to the true value than estimates from LOCF in every scenario simulated. Standard errors and confidence intervals from MMRM accurately reflected the uncertainty of the estimates, whereas standard errors and confidence intervals from LOCF underestimated uncertainty.     

Sigma metric analysis for performance of creatinine with fresh frozen serum

Background: Six sigma provides an objective and quantitative methodology to describe the laboratory testing performance. In this study, we conducted a national trueness verification scheme with fresh frozen serum (FFS) for serum creatinine to evaluate its performance in China. Methods: Two different concentration levels of FFS, targeted with reference method, were sent to 98 laboratories in China. Imprecision and bias of the measurement procedure were calculated for each participant to further evaluate the sigma value. Quality goal index (QGI) analysis was used to investigate the reason of unacceptable performance for laboratories with σ?Results: Our study indicated that the sample with high concentration of creatinine had preferable sigma values. For the enzymatic method, 7.0% (5/71) to 45.1% (32/71) of the laboratories need to improve their measurement procedures (σ??1.2). Only 3.1–5.3% of the laboratories should improve both of the precision and trueness. Conclusions: Sigma metric analysis of the serum creatinine assays is disappointing, which was mainly due to the unacceptable analytical bias according to the QGI analysis. Further effort is needed to enhance the trueness of the creatinine measurement.     

Beta prime regression with application to risky behavior frequency screening

Our aim is to model the frequency of certain behavioral acts, especially those that are likely to transmit communicable diseases between persons. We develop a generalized linear model on the basis of the beta prime distribution to model the responses to a survey question of the form, ‘When was the last time that you engaged in this behavior?’ Intuitively, individuals reporting more recent events are more likely to have greater frequency of the risky behavior. The beta prime distribution is especially suited to this application because of its long tail. We adjust for length‐biased sampling. We show how to use this distribution as the basis of a linear regression model that accounts for differences in demographic and psychological characteristics of the respondents. We discuss estimation of parameters, residuals, tests for heterogeneity of these parameters, and jackknife measures of influence. We apply the methods to a survey of alcohol abuse use among individuals who are at high risk for spreading HIV and other communicable diseases in a study conducted in Saint Petersburg, Russia. Copyright © 2013 John Wiley & Sons, Ltd.     

Group sequential enrichment design incorporating subgroup selection

An important component of clinical trials in drug development is the analysis of treatment efficacy in patient subgroups (subpopulations). Because of concerns of multiplicity and of the small sample sizes often involved, such analyses can present substantial statistical challenges and may lead to misleading conclusions. As a confirmatory seamless phase II/III design, we propose an adaptive enrichment group sequential procedure whereby resources can be concentrated on subgroups most likely to respond to treatment. Stopping boundaries are defined through upper and lower spending functions. The procedure is presented in terms of the efficient score, enabling the analysis of normal, binary, or time‐to‐event data. It addresses the dilution effect by eliminating populations at the first stage that appear likely to derive no therapeutic benefit. It subsequently proceeds with the definitive assessment of treatment efficacy among the remaining pooled populations using a group sequential design. The procedure provides strong protection of familywise type I error rate, and we employ a bootstrap algorithm to obtain point and interval estimates that are adjusted for the selection bias. We give examples to demonstrate how the design is used. We make comparisons with adaptive two‐stage combination test procedures and with a group sequential test that does not account for the presence of subgroups. Numerical results show that the procedure has high power to detect subgroup‐specific effects and the use of multiple interim analysis points can lead to substantial sample size savings. Copyright © 2013 John Wiley & Sons, Ltd.     

Attentional Bias in specific symmetry and cleaning dimensions of obsessive-compulsive disorder

Attentional bias (AB) refers to increased allocation of attention on threat stimuli when compared to neutral stimuli. It is not clear if AB occurs in subjects with obsessive-compulsive disorder (OCD). We tested AB for symmetry and cleaning symptoms of OCD. Sixty-two patients with OCD and 40 healthy controls matched by gender, age and IQ, completed a computerized dot-probe task where two pictures (with symmetry or cleaning related content) were shown. The probe appeared in the location previously occupied by one of the pictures. Within-subjects linear mixed-effect models were used to investigate the effects of the factors: group (patients vs controls), OCD dimension (cleaning vs symmetry), task condition (neutral, congruent and incongruent), and the interaction among them. We also correlated AB scores with the clinical and demographic variables. No positive interaction resulted among the factors, but positive results were observed in group and condition, separately. Patients were significantly slower than controls (p-value = 0.014) (an effect that was accounted for by depression and anxiety symptoms and comorbidity) and the neutral condition was significantly faster when compared the other two conditions (congruent and incongruent, p-value = 0.013). No association was found between AB scores and clinical symptoms. There was no AB toward specific, content-related, stimuli in this sample of OCD patients.