Repeated measures in clinical trials: simple strategies for analysis using summary measures

Patient Preference of Apalutamide Versus Enzalutamide for Recurrent or Metastatic Hormone-sensitive Prostate Cancer: An Open-label, Randomized, Crossover Trial

BACKGROUND AND OBJECTIVE

Treatment preference regarding apalutamide versus enzalutamide in prostate cancer (PCa) and the factors influencing decisions are largely unknown. Our aim was to investigate the preference for apalutamide versus enzalutamide among prostate cancer patients and their physicians and caregivers, and factors influencing their decision.

METHODS

This was a prospective, open-label, randomized, crossover trial. Patients with recurrence of localized PCa or with metastatic disease not considered as high-risk or high-volume and on continued androgen deprivation therapy were recruited. All subjects received a trial of two agents, apalutamide (A) and enzalutamide (E), for 12 wk each, with a 5-wk washout period in between. The sequencing of the drugs was randomized. The primary outcome was patient preference for one the drugs, assessed at the end of the study. Other outcomes included factors influencing patient preference, a comparison of side-effect profiles, and patients' quality of life (QoL). Physician and caregiver preferences for the drugs and factors affecting their choice were also assessed.

KEY FINDINGS AND LIMITATIONS

A total of 74 patients met the eligibility criteria and were randomized to the A → E or E → A arm. Of these, 66 patients (89.1%; 32 A → E, 34 E → A) completed the study. Baseline characteristics were comparable between the two groups, and ∼90% of the patients had low-volume metastatic disease. After completion of both treatments for 12 wk each, the difference in preference for A over E was 17.8%, with similar trends for preference of A over E among physicians (18.2%) and caregivers (22.4%). Fewer side effect was the most critical factor influencing the preference of patients. Among the side effects, less fatigue was the benefit of A over E most frequently reported. No notable difference in QoL was observed between the two drugs. However, the study was terminated on interim analysis and the results might not be conclusive.

CONCLUSIONS

There was a trend for preference of A over E among patients with predominantly low-volume recurrent or metastatic PCa and their physicians and caregivers. Fewer side effects was the most critical factor influencing their choice.

PATIENT SUMMARY

Patients with low-volume recurrent or metastatic prostate cancer tended to prefer treatment with apalutamide over enzalutamide. Side effects were the most critical factor influencing treatment preference.

Comparison of model-based versus deep learning-based image reconstruction for thin-slice T2-weighted spin-echo prostate MRI

PURPOSE

To compare a previous model-based image reconstruction (MBIR) with a newly developed deep learning (DL)-based image reconstruction for providing improved signal-to-noise ratio (SNR) in high through-plane resolution (1 mm) T2-weighted spin-echo (T2SE) prostate MRI.

METHODS

Large-area contrast and high-contrast spatial resolution of the reconstruction methods were assessed quantitatively in experimental phantom studies. The methods were next evaluated radiologically in 17 subjects at 3.0 Tesla for whom prostate MRI was clinically indicated. For each subject, the axial T2SE raw data were directed to MBIR and to the DL reconstruction at three vendor-provided levels: (L)ow, (M)edium, and (H)igh. Thin-slice images from the four reconstructions were compared using evaluation criteria related to SNR, sharpness, contrast fidelity, and reviewer preference. Results were compared using the Wilcoxon signed-rank test using Bonferroni correction, and inter-reader comparisons were done using the Cohen and Krippendorf tests.

RESULTS

Baseline contrast and resolution in phantom studies were equivalent for all four reconstruction pathways as desired. In vivo, all three DL levels (L, M, H) provided improved SNR versus MBIR. For virtually, all other evaluation criteria DL L and M were superior to MBIR. DL L and M were evaluated as superior to DL H in fidelity of contrast. For 44 of the 51 evaluations, the DL M reconstruction was preferred.

CONCLUSION

The deep learning reconstruction method provides significant SNR improvement in thin-slice (1 mm) T2SE images of the prostate while retaining image contrast. However, if taken to too high a level (DL High), both radiological sharpness and fidelity of contrast diminish.

Cluster Randomized Trials with a Pretest and Posttest: Equivalence of Three-, Two- and One-Level Analyses, and Sample Size Calculation

In a cluster randomized trial clusters of persons, for instance, schools or health centers, are assigned to treatments, and all persons in the same cluster get the same treatment. Although less powerful than individual randomization, cluster randomization is a good alternative if individual randomization is impossible or leads to severe treatment contamination (carry-over). Focusing on cluster randomized trials with a pretest and post-test of a quantitative outcome, this paper shows the equivalence of four methods of analysis: a three-level mixed (multilevel) regression for repeated measures with as levels cluster, person, and time, and allowing for unstructured between-cluster and within-cluster covariance matrices; a two-level mixed regression with as levels cluster and person, using change from baseline as outcome; a two-level mixed regression with as levels cluster and time, using cluster means as data; a one-level analysis of cluster means of change from baseline. Subsequently, similar equivalences are shown between a constrained mixed model and methods using the pretest as covariate. All methods are also compared on a cluster randomized trial on mental health in children. From these equivalences follows a simple method to calculate the sample size for a cluster randomized trial with baseline measurement, which is demonstrated step-by-step.

Salivary Electrostimulation in the Treatment of Radiation Therapy-Induced Xerostomia (LEONIDAS-2): A Multicenter, Randomized, Double-Masked, Sham-Controlled, Phase 3 Trial

PURPOSE

Radiation therapy-induced xerostomia significantly affects quality of life in head and neck cancer survivors. Neuro-electrostimulation of the salivary glands may safely increase natural salivation and reduce dry mouth symptoms.

METHODS AND MATERIALS

This multicenter, double-masked, randomized, sham-controlled clinical trial assessed the long-term effects of a commercially available intraoral neuro-electrostimulating device in lessening xerostomia symptoms, increasing salivary flow, and improving quality of life in individuals with radiation therapy-induced xerostomia. Using a computer-generated randomization list, participants were assigned (1:1) to an active intraoral custom-made removable electrostimulating device or a sham device to be used for 12 months. The primary outcome was the proportion of patients reporting a 30% improvement on the xerostomia visual analog scale at 12 months. A number of secondary and exploratory outcomes were also assessed through validated measurements (sialometry and visual analog scale) and quality-of-life questionnaires (EORTC QLQ-H&N35, OH-QoL16, and SF-36).

RESULTS

As per protocol, 86 participants were recruited. Intention-to-treat analyses showed no statistical evidence of a difference between the study groups with respect to the primary outcome or for any of the secondary clinical or quality-of-life outcomes. Exploratory analyses showed a statistically significant difference in the changes over time of the dry mouth subscale score of the EORTC QLQ-H&N35 in favor of the active intervention.

CONCLUSIONS

LEONIDAS-2 did not meet the primary and secondary outcomes.

How important is the linearity assumption in a sample size calculation for a randomised controlled trial where treatment is anticipated to affect a rate of change?

BACKGROUND

For certain conditions, treatments aim to lessen deterioration over time. A trial outcome could be change in a continuous measure, analysed using a random slopes model with a different slope in each treatment group. A sample size for a trial with a particular schedule of visits (e.g. annually for three years) can be obtained using a two-stage process. First, relevant (co-) variances are estimated from a pre-existing dataset e.g. an observational study conducted in a similar setting. Second, standard formulae are used to calculate sample size. However, the random slopes model assumes linear trajectories with any difference in group means increasing proportionally to follow-up time. The impact of these assumptions failing is unclear.

METHODS

We used simulation to assess the impact of a non-linear trajectory and/or non-proportional treatment effect on the proposed trial's power. We used four trajectories, both linear and non-linear, and simulated observational studies to calculate sample sizes. Trials of this size were then simulated, with treatment effects proportional or non-proportional to time.

RESULTS

For a proportional treatment effect and a trial visit schedule matching the observational study, powers are close to nominal even for non-linear trajectories. However, if the schedule does not match the observational study, powers can be above or below nominal levels, with the extent of this depending on parameters such as the residual error variance. For a non-proportional treatment effect, using a random slopes model can lead to powers far from nominal levels.

CONCLUSIONS

If trajectories are suspected to be non-linear, observational data used to inform power calculations should have the same visit schedule as the proposed trial where possible. Additionally, if the treatment effect is expected to be non-proportional, the random slopes model should not be used. A model allowing trajectories to vary freely over time could be used instead, either as a second line analysis method (bearing in mind that power will be lost) or when powering the trial.

Bayesian borrowing for basket trials with longitudinal outcomes

Basket trials are a novel clinical trial design in which a single intervention is investigated in multiple patient subgroups, or "baskets." They offer the opportunity to share information between subgroups, potentially increasing power to detect treatment effects. Basket trials offer several advantages over running a series of separate trials, including reduced sample sizes, increased efficiency, and reduced costs. Primarily, basket trials have been undertaken in Phase II oncology settings, but could be a promising design in other areas where a shared underlying biological mechanism drives different diseases. One such area is chronic aging-related diseases. However, trials in this area frequently have longitudinal outcomes, and therefore suitable methods are needed to share information in this setting. In this paper, we extend three Bayesian borrowing methods for a basket design with continuous longitudinal endpoints. We demonstrate our methods on a real-world dataset and in a simulation study where the aim is to detect positive basketwise treatment effects. Methods are compared with standalone analysis of each basket without borrowing. Our results confirm that methods that share information can improve power to detect positive treatment effects and increase precision over independent analysis in many scenarios. In highly heterogeneous scenarios, there is a trade-off between increased power and increased risk of type I errors. Our proposed methods for basket trials with continuous longitudinal outcomes aim to facilitate their applicability in the area of aging related diseases. Choice of method should be made based on trial priorities and the expected basketwise distribution of treatment effects.

Sensitivity Analysis: A Method to Promote Certainty and Transparency in Nursing and Health Research

Nursing and health researchers may be presented with uncertainty regarding the utilization or legitimacy of methodological or analytic decisions. Sensitivity analyses are purposed to gain insight and certainty about the validity of research findings reported. Reporting guidelines and health research methodologists have emphasized the importance of utilizing and reporting sensitivity analyses in clinical research. However, sensitivity analyses are underreported in nursing and health research. The aim of this methodological overview is to provide an introduction to the purpose, conduct, interpretation, and reporting of sensitivity analyses, using a series of simulated and contemporary case examples.

A proof of concept pilot trial of probiotics in symptomatic oral lichen planus (CABRIO)

OBJECTIVE

To preliminary evaluate the clinical effects of probiotics in individuals with symptomatic oral lichen planus and the possible mechanisms of action.

SUBJECTS AND METHODS

A group of 30 individuals with symptomatic oral lichen planus were recruited in a randomised double-blind parallel group controlled (1:1) proof-of-concept pilot trial of probiotic VSL#3 vs placebo. Efficacy outcomes included changes in pain numeric rating scale, oral disease severity score and the chronic oral mucosal disease questionnaire. Adverse effects, home diary and withdrawals were assessed as feasibility outcomes. Mechanistic outcomes included changes in salivary and serum levels of CXCL10 and IFN-γ and in oral microbial composition.

RESULTS

The probiotic VSL#3 was safe and well tolerated. We observed no statistically significant change in pain, disease activity, quality of life, serum/salivary CXCL10 or oral microbial composition with respect to placebo. Salivary IFN-γ levels demonstrate a trend for a reduced level in the active group (p = 0.082) after 30 days of probiotic consumption.

CONCLUSIONS

The present proof-of-concept study provides some weak not convincing indication of biological and clinical effects of probiotic VSL#3 in individuals with painful oral lichen planus. Further research in this field is needed, with the current study providing useful information to the design of future clinical trials.

A mixed effects model for analyzing area under the curve of longitudinally measured biomarkers with missing data

A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. However, this model assumes the outcome to be normally distributed, which is often violated in biomarker data. In this paper, we propose to use a LMM on log-transformed biomarkers, based on which statistical inference for the ratio, rather than difference, of AUC between treatment groups is provided. The proposed method can not only handle the potential baseline imbalance in a randomized trail but also circumvent the estimation of the nuisance variance parameters in the log-normal model. The proposed model is applied to a recently completed large randomized trial studying the effect of nicotine reduction on biomarker exposure of smokers.

Do Children With Functional Abdominal Pain Benefit More From a Pain-Specific Cognitive-Behavioral Intervention Than From an Unspecific Attention Control Intervention? Results of a Randomized Controlled Trial

INTRODUCTION

We aimed to compare the efficacy of cognitive-behavioral therapy (CBT) among children with functional abdominal pain with an attention control (AC), hypothesizing the superiority of CBT group intervention regarding pain intensity (primary outcome), pain duration and frequency (further primary outcomes), functional disability, and quality of life and coping strategies (key secondary outcomes).

METHODS

We conducted a prospective, multicenter, randomized controlled efficacy trial (RCT) with 4 time points (before intervention, after intervention, 3-month follow-up, and 12-month follow-up). One hundred twenty-seven children aged 7-12 years were randomized to either the CBT (n = 63; 55.6% girls) or the AC (n = 64; 57.8% girls).

RESULTS

Primary endpoint analysis of the logarithmized area under the pain intensity curve showed no significant difference between groups (mean reduction = 49.04%, 95% confidence interval [CI] -19.98%-78.36%). Treatment success rates were comparable (adjusted odds ratio = 0.53, 95% CI 0.21-1.34, number needed to treat = 16). However, time trend analyses over the course of 1 year revealed a significantly greater reduction in pain intensity (40.9%, 95% CI 2.7%-64.1%) and pain duration (43.6%, 95% CI 6.2%-66.1%) in the CBT compared with the AC, but not in pain frequency per day (1.2, 95% CI -2.7 to 5.2). In the long term, children in the CBT benefitted slightly more than those in the AC with respect to functional disability, quality of life, and coping strategies.

DISCUSSION

Both interventions were effective, which underlines the role of time and attention for treatment efficacy. However, in the longer term, CBT yielded more favorable results.

Effects of behavioural exercise therapy on the effectiveness of multidisciplinary rehabilitation for chronic non-specific low back pain: a randomised controlled trial

Background

The long-term effects of behavioural medical rehabilitation (BMR), as a type of multidisciplinary rehabilitation, in the treatment of chronic non-specific low back pain (CLBP) have been shown. However, the specific effects of behavioural exercise therapy (BET) compared to standard exercise therapy (SET) within BMR are not well understood. The aim of the study was to assess the effectiveness of BMR + BET compared to BMR + SET in individuals with CLBP in a two-armed, pre-registered, multicentre, parallel, randomised controlled trial (RCT).

Methods

A total of 351 adults with CLBP in two rehabilitation centres were online randomised based on an ‘urn randomisation’ algorithm to either BMR + SET (n = 175) or BMR + BET (n = 176). Participants in both study groups were non-blinded and received BMR, consisting of an multidisciplinary admission, a psychosocial assessment, multidisciplinary case management, psychological treatment, health education and social counselling. The intervention group (BMR + BET) received a manualised, biopsychosocial BET within BMR. The aim of BET was to develop self-management strategies in coping with CLBP. The control group (BMR + SET) received biomedical SET within BMR with the aim to improve mainly physical fitness. Therapists in both study groups were not blinded. The BMR lasted on average 27 days, and both exercise programmes had a mean duration of 26 h. The primary outcome was functional ability at 12 months. Secondary outcomes were e.g. pain, avoidance-endurance, pain management and physical activity. The analysis was by intention-to-treat, blinded to the study group, and used a linear mixed model.

Results

There were no between-group differences observed in function at the end of the BMR (mean difference, 0.08; 95% CI − 2.82 to 2.99; p = 0.955), at 6 months (mean difference, − 1.80; 95% CI; − 5.57 to 1.97; p = 0.349) and at 12 months (mean difference, − 1.33; 95% CI − 5.57 to 2.92; p = 0.540). Both study groups improved in the primary outcome and most secondary outcomes at 12 months with small to medium effect sizes.

Conclusion

BMR + BET was not more effective in improving function and other secondary outcomes in individuals with CLBP compared to BMR + SET.

Trial registration

Current controlled trials NCT01666639, 16/08/2012.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-021-04353-y.

Contamination: How much can an individually randomized trial tolerate?

Cluster randomization results in an increase in sample size compared to individual randomization, referred to as an efficiency loss. This efficiency loss is typically presented under an assumption of no contamination in the individually randomized trial. An alternative comparator is the sample size needed under individual randomization to detect the attenuated treatment effect due to contamination. A general framework is provided for determining the extent of contamination that can be tolerated in an individually randomized trial before a cluster randomized design yields a larger sample size. Results are presented for a variety of cluster trial designs including parallel arm, stepped-wedge and cluster crossover trials. Results reinforce what is expected: individually randomized trials can tolerate a surprisingly large amount of contamination before they become less efficient than cluster designs. We determine the point at which the contamination means an individual randomized design to detect an attenuated effect requires a larger sample size than cluster randomization under a nonattenuated effect. This critical rate is a simple function of the design effect for clustering and the design effect for multiple periods as well as design effects for stratification or repeated measures under individual randomization. These findings are important for pragmatic comparisons between a novel treatment and usual care as any bias due to contamination will only attenuate the true treatment effect. This is a bias that operates in a predictable direction. Yet, cluster randomized designs with post-randomization recruitment without blinding, are at high risk of bias due to the differential recruitment across treatment arms. This sort of bias operates in an unpredictable direction. Thus, with knowledge that cluster randomized trials are generally at a greater risk of biases that can operate in a nonpredictable direction, results presented here suggest that even in situations where there is a risk of contamination, individual randomization might still be the design of choice.

Analysis of change in patient-reported outcome measures with floor and ceiling effects using the multilevel Tobit model: a simulation study and an example from a National Joint Register using body mass index and the Oxford Hip Score

OBJECTIVES

This study has three objectives. (1) Investigate the association between body mass index (BMI) and the efficacy of primary hip replacement using a patient-reported outcome measure (PROMs) with a measurement floor and ceiling, (2) Explore the performance of different estimation methods to estimate change in PROMs score following surgery using a simulation study and real word data where data has measurement floors and ceilings and (3) Lastly, develop guidance for practising researchers on the analysis of PROMs in the presence of floor and ceiling effects.

DESIGN

Simulation study and prospective national medical device register.

SETTING

National Register of Joint Replacement and Medical Devices.

METHODS

Using a Monte Carlo simulation study and data from a national joint replacement register (162 513 patients with pre- and post-surgery PROMs), we investigate simple approaches for the analysis of outcomes with floor and ceiling effects that are measured at two occasions: linear and Tobit regression (baseline adjusted analysis of covariance, change-score analysis, post-score analysis) in addition to linear and multilevel Tobit models.

PRIMARY OUTCOME

The primary outcome of interest is change in PROMs from pre-surgery to 6 months post-surgery.

RESULTS

Analysis of data with floor and ceiling effects with models that fail to account for these features induce substantial bias. Single-level Tobit models only correct for floor or ceiling effects when the exposure of interest is not associated with the baseline score. In observational data scenarios, only multilevel Tobit models are capable of providing unbiased inferences.

CONCLUSIONS

Inferences from pre- post-studies that fail to account for floor and ceiling effects may induce spurious associations with substantial risk of bias. Multilevel Tobit models indicate the efficacy of total hip replacement is independent of BMI. Restricting access to total hip replacement based on a patients BMI can not be supported by the data.

Body iron and lead status in early childhood and its effects on development and cognition: a longitudinal study from urban Vellore

OBJECTIVE

Early childhood factors can have persisting effects on development and cognition in children. We propose to explore the trends of Fe deficiency and Pb toxicity in early childhood and their association with child development at 2 years of age and cognition at 5 years.

DESIGN

Longitudinal birth cohort study.

SETTING

Urban slum, Vellore, India.

PARTICIPANTS

Children enrolled at birth were followed up regularly in the first 2 years with developmental and cognitive assessments at 2 and 5 years of age, respectively.

RESULTS

The birth cohort enrolled 251 children with 228 children followed up at 2 years and 212 at 5 years of age. Fe deficiency (ID) was highest at 15 months of age and improved subsequently at 24 months. Blood Pb levels (BLL) remained high at all age groups with an increasing trend with age; 97 % at 36 months having high BLL. Persistent high mean BLL at 15 and 24 months had negative association with both cognition and expressive language raw scores of 24 months, while high mean BLL at 15, 24 and 36 months had no significant association with any of the domains of cognition at 5 years of age. Early childhood cumulative body Fe status at 7, 15 and 24 months did not show any association with child development at 2 years, but was associated with verbal, performance and processing speed components of cognition at 5 years.

CONCLUSIONS

Optimising body Fe status and limiting Pb exposure in early childhood can augment child development and school entry cognition.

Comparison of a Novel Cadaver Model (Fix for Life) With the Formalin-Fixed Cadaver and Manikin Model for Suitability and Realism in Airway Management Training

BACKGROUND

Manikins are widely used in airway management training; however, simulation of realism and interpatient variability remains a challenge. We investigated whether cadavers embalmed with the novel Fix for Life (F4L) embalmment method are a suitable and realistic model for teaching 3 basic airway skills: facemask ventilation, tracheal intubation, and laryngeal mask insertion compared to a manikin (SimMan 3G) and formalin-fixed cadavers.

METHODS

Thirty anesthesiologists and experienced residents ("operators") were instructed to perform the 3 airway techniques in 10 F4L, 10 formalin-fixed cadavers, and 1 manikin. The order of the model type was randomized per operator. Primary outcomes were the operators' ranking of each model type as a teaching model (total rank), ranking of the model types per technique, and an operator's average verbal rating score for suitability and realism of learning the technique on the model. Secondary outcomes were the percentages of successfully performed procedures per technique and per model (success rates in completing the respective airway maneuvers). For each of the airway techniques, the Friedman analysis of variance was used to compare the 3 models on mean operator ranking and mean verbal rating scores.

RESULTS

Twenty-seven of 30 operators (90%) performed all airway techniques on all of the available models, whereas 3 operators performed the majority but not all of the airway maneuvers on all models for logistical reasons. The total number of attempts for each technique was 30 on the manikin, 292 in the F4L, and 282 on the formalin-fixed cadavers. The operators' median total ranking of each model type as a teaching model was 1 for F4L, 2 for the manikin and, 3 for the formalin-fixed cadavers (P < .001). F4L was considered the best model for mask ventilation (P = .029) and had a higher mean verbal rating score for realism in laryngeal mask airway insertion (P = .043). The F4L and manikin did not differ significantly in other scores for suitability and realism. The formalin-fixed cadaver was ranked last and received lowest scores in all procedures (all P < .001). Success rates of the procedures were highest in the manikin.

CONCLUSIONS

F4L cadavers were ranked highest for mask ventilation and were considered the most realistic model for training laryngeal mask insertion. Formalin-fixed cadavers are inappropriate for airway management training.

Repeated Measures Designs and Analysis of Longitudinal Data: If at First You Do Not Succeed-Try, Try Again

Anesthesia, critical care, perioperative, and pain research often involves study designs in which the same outcome variable is repeatedly measured or observed over time on the same patients. Such repeatedly measured data are referred to as longitudinal data, and longitudinal study designs are commonly used to investigate changes in an outcome over time and to compare these changes among treatment groups. From a statistical perspective, longitudinal studies usually increase the precision of estimated treatment effects, thus increasing the power to detect such effects. Commonly used statistical techniques mostly assume independence of the observations or measurements. However, values repeatedly measured in the same individual will usually be more similar to each other than values of different individuals and ignoring the correlation between repeated measurements may lead to biased estimates as well as invalid P values and confidence intervals. Therefore, appropriate analysis of repeated-measures data requires specific statistical techniques. This tutorial reviews 3 classes of commonly used approaches for the analysis of longitudinal data. The first class uses summary statistics to condense the repeatedly measured information to a single number per subject, thus basically eliminating within-subject repeated measurements and allowing for a straightforward comparison of groups using standard statistical hypothesis tests. The second class is historically popular and comprises the repeated-measures analysis of variance type of analyses. However, strong assumptions that are seldom met in practice and low flexibility limit the usefulness of this approach. The third class comprises modern and flexible regression-based techniques that can be generalized to accommodate a wide range of outcome data including continuous, categorical, and count data. Such methods can be further divided into so-called “population-average statistical models” that focus on the specification of the mean response of the outcome estimated by generalized estimating equations, and “subject-specific models” that allow a full specification of the distribution of the outcome by using random effects to capture within-subject correlations. The choice as to which approach to choose partly depends on the aim of the research and the desired interpretation of the estimated effects (population-average versus subject-specific interpretation). This tutorial discusses aspects of the theoretical background for each technique, and with specific examples of studies published in Anesthesia & Analgesia, demonstrates how these techniques are used in practice.

Impact of DARC rs12075 Variants on Liver Fibrosis Progression in Patients with Chronic Hepatitis C: A Retrospective Study

: The Duffy antigen receptor for chemokines (DARC) rs12075 polymorphism regulates leukocyte trafficking and proinflammatory chemokine homeostasis. Hepatitis C virus (HCV)-mediated liver fibrosis is associated with an uncontrolled inflammatory response. In this study, we evaluate the association between the DARC rs12075 polymorphism and liver stiffness progression in HCV-infected patients. We carried out a retrospective cohort study (repeated measures design) in 208 noncirrhotic patients with chronic hepatitis C (CHC) who had at least two liver stiffness measurements (LSM) with a separation of at least 12 months. We used generalized linear models to analyze the association between DARC rs12075 polymorphism and outcome variables. During a follow-up of 46.6 months, the percentage of patients with stages of fibrosis F0/F1 decreased (p < 0.001), while LSM values and the percentage of patients with cirrhosis increased (p < 0.001). This pattern of changes was maintained in each of the groups of patients analyzed according to their rs12075 genotypes (AA or AG/GG). However, the variations in liver stiffness characteristics were lower in patients with the rs12075 AG/GG genotype (AG/GG versus AA). Thereby, in the adjusted analysis, patients with the rs12075 AG/GG genotype had a lower risk of an increased value of LSM2/LSM1 arithmetic mean ratio (AMR = 0.83; p = 0.001) and of an increase in LSM ≥ 5 kPa (odds ratio (OR) = 0.28; p = 0.009). Besides, patients with rs12075 AG/GG had a lower risk of cirrhosis progression (OR = 0.24; p = 0.009). No significant associations were found for an increase in LSM ≥ 10 kPa. We found an association between the DARC rs12075 single nucleotide polymorphism (SNP) and CHC progression. Specifically, patients with the DARC rs12075 AG/GG genotype had a lower risk of liver fibrosis progression and development of cirrhosis.

The Myeloid-Epithelial-Reproductive Tyrosine Kinase (MERTK) rs4374383 Polymorphism Predicts Progression of Liver Fibrosis in Hepatitis C Virus-Infected Patients: A Longitudinal Study

Background: The myeloid-epithelial-reproductive tyrosine kinase (MERTK) is involved in hepatic steatosis, inflammation, and liver fibrosis. Here we evaluated the association between the MERTK rs4374383 single nucleotide polymorphism (SNP) and liver fibrosis progression in hepatitis C virus (HCV)-infected patients. Methods: We performed a retrospective study (repeated measures design) in 208 patients who had liver stiffness measurement (LSM), which was assessed using transient elastography. No patient had cirrhosis at baseline (LSM ≥ 12.5 kPa). Results: At baseline, 53.8% were male, the median age was 47.1 years, 13.5% reported a high intake of alcohol, 10.1% were prior injection drug users, 85.3% were infected with HCV genotype 1, and 22.6% had previously failed antiviral therapy (pegylated-interferon-alpha/ribavirin). During a median follow-up of 46.6 months, 26 patients developed cirrhosis. The rs4374383 G carriers had a higher risk of increasing LSM (adjusted arithmetic mean ratio (aAMR) = 1.14; p = 0.006) and a higher likelihood of having an increase in LSM greater than 5 kPa (ΔLSM ≥ 5 kPa) (adjusted odds ratio (aOR) = 2.37; p = 0.029), and greater than 7 kPa (ΔLSM ≥ 7 kPa) (aOR = 3.24; p = 0.032), after controlling for confounding. The SNP’s association with cirrhosis progression was close to statistical significance (aOR = 2.18; p = 0.070). Conclusions: MERTK rs4374383 A carriers had a lower risk of liver fibrosis progression than G carriers, supporting the hypothesis that this SNP seems to have a critical role in the pathogenesis of liver disease in HCV-infected patients.

Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models

Simultaneous inference in longitudinal, repeated-measures, and multi-endpoint designs can be onerous, especially when trying to find a reasonable joint model from which the interesting effects and covariances are estimated. A novel statistical approach known as multiple marginal models greatly simplifies the modelling process: the core idea is to "marginalise" the problem and fit multiple small models to different portions of the data, and then estimate the overall covariance matrix in a subsequent, separate step. Using these estimates guarantees strong control of the family-wise error rate, however only asymptotically. In this paper, we show how to make the approach also applicable to small-sample data problems. Specifically, we discuss the computation of adjusted P values and simultaneous confidence bounds for comparisons of randomised treatment groups as well as for levels of a nonrandomised factor such as multiple endpoints, repeated measures, or a series of points in time or space. We illustrate the practical use of the method with a data example.

Sample size considerations for n-of-1 trials

N-of-1 trials are trials in which patients are treated with two or more treatments on multiple occasions. They can have many different purposes and can be analysed in different frameworks. In this note, five different criteria for planning sample sizes for n-of-1 trials are identified, and formulae and advice to address the associated tasks are provided. The basic design addressed is that of randomisation to treatment and control within cycles of pairs of episodes and the model assumed is that of a Normal-Normal mixture with variance components corresponding to within-cycle within-patient variation and treatment-by-patient interaction. The code to accomplish the tasks has been written in GenStat®, SAS® and R® and the application of the approaches is illustrated.

Efficacy of a prevention program for eating disorders in schools: a cluster-randomized controlled trial

BACKGROUND

Previous prevention programs in the school context have not addressed both genders, have been time-consuming, or have had deficits in the evaluation method. The aim of the present study was to evaluate the impact of a universal prevention program for female and male adolescents on eating disorder pathology and related risk factors.

METHODS

Between February 2012 and July 2014, 2515 students in 23 schools from 8th or 11th grade were assessed for eligibility in this longitudinal cluster-randomized controlled trial with a six months follow-up. Of those students, 2342 were cluster-randomized to the intervention condition which received a six school hours universal prevention program or to the no treatment control condition.

RESULTS

The complete case population comprised 724 students in the intervention (54.3% female, M = 14.3 years, SD = 1.61) and 728 in the control condition (57.0% female, M = 14.7 years, SD = 1.63). Random-effects analysis of covariance on the primary outcome showed no significant differences between the intervention and control groups in their eating disorder pathology change scores six months after the intervention. Regarding secondary outcomes, participants in the intervention group showed a greater increase in knowledge about eating disorders both after the intervention (p < .001, ES = 1.06) and six months later (p = .01, ES = 0.40). Greater reductions in anxiety severity were observed in the intervention group post-intervention (p = .02, ES = 0.22) which was not maintained at the six months follow-up. Results differed between participants from grade 8 and 11.

CONCLUSION

The present universal prevention program can be particularly recommended for adolescents from grade 11.

TRIAL REGISTRATION

ISRCTN 97989348.

Comparison of scores associating symptoms and rescue medication use for evaluating the efficacy of allergy immunotherapy in seasonal allergic rhinoconjunctivitis: results from five trials

BACKGROUND

Over the past decade, regulatory bodies and scientific societies recommended, as primary efficacy outcome, a score that reflects both symptom severity and use of rescue medication for clinical trials in allergy immunotherapy (AIT).

OBJECTIVE

We sought to compare the results obtained with two subject-specific scores, the Combined Score (CS) and the Adjusted Symptom Score (AdSS), for assessment of AIT in seasonal allergic rhinoconjunctivitis due to birch and grass pollen allergens.

METHODS

CS and AdSS were evaluated in subjects receiving a 300IR dose of allergen extract daily, by sublingual route, in four clinical trials with the 5-grass pollen tablet (NCT00367640, NCT00409409, NCT00955825 and NCT00418379) and one with the birch pollen solution (NCT01731249). The CS is derived from the Rhinoconjunctivitis Total Symptom Score (RTSS) and the Rescue Medication Score (RMS) giving equal weight to symptoms and medication use. The AdSS is a symptom score adjusting for rescue medication use. Efficacy end-points were analysed using an analysis of covariance linear model.

RESULTS

In all trials, despite the different constructs of the two scores, Combined Score or Adjusted Symptom Score were similarly reduced in the 300IR group compared to the placebo group. Treatment effect was consistently demonstrated with both scores, CS and AdSS, used as either daily scores or average of the daily scores over the pollen season. Minor differences with the same statistical conclusions were observed between the results, leading to the same interpretation.

CONCLUSIONS AND CLINICAL RELEVANCE

The two scores, combined and adjusted scores, for evaluation of clinical efficacy of AIT have led to similar results, with similar statistical conclusions and similar interpretation.

Understanding Variation in Sets of N-of-1 Trials

A recent paper in this journal by Chen and Chen has used computer simulations to examine a number of approaches to analysing sets of n-of-1 trials. We have examined such designs using a more theoretical approach based on considering the purpose of analysis and the structure as regards randomisation that the design uses. We show that different purposes require different analyses and that these in turn may produce quite different results. Our approach to incorporating the randomisation employed when the purpose is to test a null hypothesis of strict equality of the treatment makes use of Nelder’s theory of general balance. However, where the purpose is to make inferences about the effects for individual patients, we show that a mixed model is needed. There are strong parallels to the difference between fixed and random effects meta-analyses and these are discussed.

Statistical Power for Analyses of Changes in Randomized Controlled Trials

Randomized controlled trials (RCTs) are widely recommended as the most useful study design to generate reliable evidence and guidance to daily practices in medicine and dentistry. However, it is not well-known in dental research that different statistical methods of data analysis can yield substantial differences in study power. In this study, computer simulations are used to explore how using different univariate and multivariate statistical methods of analyzing change in continuous outcome variables affects study power, and the sample size required for RCTs. Results show that, in general, analysis of covariance (ANCOVA) yields greater power than other statistical methods in testing the superiority of one treatment over another, or in testing the equivalence between two treatments. Therefore, ANCOVA should be used in preference to change score or percentage change score to reduce type II error rates.

Patient reported outcomes as endpoints in medical research

This review covers a number of the many design and analytic issues associated with clinical trials that incorporate patient reported outcomes as primary or secondary endpoints. We use a clinical trial designed to evaluate a new therapy for the prevention of migraines to illustrate how endpoints are defined by the objectives of the study, the methods for handling longitudinal assessments with multiple scales or outcomes, and the methods of analysis in the presence of missing data.

Meta-analysis of longitudinal studies

Background Longitudinal studies typically report estimates of the effect of a treatment or exposure at various times during the course of follow-up. Meta-analyses of these studies must account for correlations between effect estimates from the same study.

Purpose To describe and contrast alternative approaches to handling correlations inherent to longitudinal effect estimates in meta-analyses.

Methods. Linear mixed-effects models can account for correlations in a number of ways. We considered three alternatives: including study-specific random-effects, correlated time-specific random-effects or a general multivariate specification that also allows correlated within-study residuals. Data from a review of studies of the effect of deep-brain stimulation (DBS) in patients with Parkinson's disease are used to illustrate the application of these models. Results are contrasted with those from a naïve meta-analysis in which the correlations are ignored.

Results The data included 46 studies that yielded 82 estimates of the effect of DBS measured at 3, 6, 12 months or later after implantation of the stimulator. Models that accounted for correlations, particularly the full multivariate specification, provided better fit (lower AIC) and yielded slightly more precise effect estimates. This was in part due to a relatively extreme observation from a study that provided similar estimates at other times, which in the naïve approach exerts greater influence since it is treated as an independent observation.

Limitations Since the true values of the parameters are not known, it is impossible to confirm that estimates from the multivariate approach are necessarily more accurate.

Conclusion Standard meta-analytic models can be readily extended to account for correlations between effects in longitudinal studies. These models may provide better fit and possibly more precise summary effect estimates. Clinical Trials 2007; 4: 525—539. http://ctj.sagepub.com

Cerebrospinal fluid ratios with Aβ42 predict preclinical brain β-amyloid accumulation

Introduction

Biomarkers are urgently needed for the critical yet understudied preclinical stage of Alzheimer's disease (AD).

Methods

Cerebrospinal fluid (CSF) collection, [C-11]Pittsburgh compound B (PiB) amyloid imaging, and magnetic resonance imaging were acquired in 104 cognitively healthy adults enriched with risk for sporadic AD. Image-derived cerebral β-amyloid (Aβ) burden, measured concurrently and longitudinally, was regressed on CSF measures of Aβ, neural injury, and inflammation, as well as ratios with Aβ₄₂. Linear mixed-effects regression was used to model the effect of the CSF measures that predicted longitudinal brain amyloid accumulation on longitudinal cognitive decline, measured by memory test scores.

Results

At baseline, Aβ₄₂/Aβ₄₀ and all CSF ratios to Aβ₄₂ were associated with PiB binding in AD-vulnerable regions. Longitudinally, Aβ₄₂/Aβ₄₀ and ratios of total tau (t-tau), phosphorylated-tau (p-tau), neurofilament light protein, and monocyte chemoattractant protein-1 to Aβ₄₂ were associated with increased Aβ deposition over 2 years, predominantly in lateral parietal and temporal cortex. However, these CSF ratios were not significantly associated with cognitive decline, and the effect seems to be largely driven by Aβ₄₂ in the denominator.

Discussion

These results corroborate previous findings that t-tau/Aβ₄₂ and p-tau/Aβ₄₂ are the strongest candidate biomarkers during the preclinical time frame. They support a framework in which neural injury and amyloid deposition are likely occurring simultaneously. It may be that neurodegenerative processes influence progressive amyloid accumulation, even in the preclinical time frame. CSF biomarkers for nonspecific axonal injury and inflammation may provide more information at more advanced stages of the preclinical time course.

Policy and system change and community coalitions: outcomes from allies against asthma

Objectives. We assessed policy and system changes and health outcomes produced by the Allies Against Asthma program, a 5-year collaborative effort by 7 community coalitions to address childhood asthma. We also explored associations between community engagement and outcomes. Methods. We interviewed a sample of 1,477 parents of children with asthma in coalition target areas and comparison areas at baseline and 1 year to assess quality-of-life and symptom changes. An extensive tracking and documentation procedure and a survey of 284 participating individuals and organizations were used to ascertain policy and system changes and community engagement levels. Results. A total of 89 policy and system changes were achieved, ranging from changes in interinstitutional and intrainstitutional practices to statewide legislation. Allies children experienced fewer daytime (P = .008) and nighttime (P = .004) asthma symptoms than comparison children. In addition, Allies parents felt less helpless, frightened, and angry (P = .01) about their child's asthma. Type of community engagement was associated with number of policy and system changes. Conclusions. Community coalitions can successfully achieve asthma policy and system changes and improve health outcomes. Increased core and ongoing community stakeholder participation rather than a higher overall number of participants was associated with more change.

On model selections for repeated measurement data in clinical studies

Repeated measurement designs have been widely used in various randomized controlled trials for evaluating long-term intervention efficacies. For some clinical trials, the primary research question is how to compare two treatments at a fixed time, using a t-test. Although simple, robust, and convenient, this type of analysis fails to utilize a large amount of collected information. Alternatively, the mixed-effects model is commonly used for repeated measurement data. It models all available data jointly and allows explicit assessment of the overall treatment effects across the entire time spectrum. In this paper, we propose an analytic strategy for longitudinal clinical trial data where the mixed-effects model is coupled with a model selection scheme. The proposed test statistics not only make full use of all available data but also utilize the information from the optimal model deemed for the data. The performance of the proposed method under various setups, including different data missing mechanisms, is evaluated via extensive Monte Carlo simulations. Our numerical results demonstrate that the proposed analytic procedure is more powerful than the t-test when the primary interest is to test for the treatment effect at the last time point. Simulations also reveal that the proposed method outperforms the usual mixed-effects model for testing the overall treatment effects across time. In addition, the proposed framework is more robust and flexible in dealing with missing data compared with several competing methods. The utility of the proposed method is demonstrated by analyzing a clinical trial on the cognitive effect of testosterone in geriatric men with low baseline testosterone levels.

Mathematical modeling of area under the curve assessment criteria to quantify the antioxidant and pro-oxidant capacity: Coffee extracts as a case study

The development of a convenient mathematical application for testing the antioxidant and pro-oxidant capacity is essential in order to investigate potential sources of new agents and processes. In this regard, authors use the standardized values of the area under the curve of a kinetic profile of a dose-response agent, as a way to bypass the complex process of analyzing the kinetic variations of agents. In general, linear approaches are used, but such patterns frequently lead to unreliable results and misinterpretations, making it extremely difficult to compare the results from different assays. In this work, we have demonstrated the non-linearity of the dose-response area under the curve assessment criteria by means of simulations. A simple non-linear dose-response model was developed to describe the accurate response. As a case study, experimental data of extracts of unroasted coffee beans from five different country-climate locations for the two most common coffee varieties (Robusta and Arabica) were obtained using the β-carotene and crocin bleaching in vitro assays. Their antioxidant capacity was analyzed in detail and compared with commercial standards. The results show that the antioxidant capacity was greater than some of the commercial standards in terms of its maximum capacity, while when the analyses are based on rate parameters, the coffee extracts show between 6 and 40 times lower values than the standard antioxidants. In addition, to illustrate the advantages of using the standardized area units and the mathematical model developed, other more complex scenarios were recreated. We believe that the model application developed provides a simple alternative to summarize meaningful parameters that characterize the response, facilitates rigorous comparisons among the effects of different compounds and experimental approaches and helps to comprehend multi-variable scenarios.

ANCA serotype and histopathological classification for the prediction of renal outcome in ANCA-associated glomerulonephritis

BACKGROUND

The phenotype of renal involvement in anti-neutrophil cytoplasmic antibodies (ANCA) vasculitis has a major influence on survival, and histological subgrouping of diagnostic renal biopsies has been proposed to aid in the prediction of renal outcome. We aimed to validate this histological subgrouping and to investigate the additional value of ANCA serotype in the prediction of renal outcome.

METHODS

Data were retrospectively collected from the time of diagnosis by systematic review of medical records from 136 patients with renal biopsies recruited to cohorts from the UK and Spain, over 15 years. The end point, renal survival, was the composite of end-stage renal disease (ESRD) or death from any cause. The occurrence of ESRD, Stage 4 Kidney Disease Outcomes Quality Initiative-Chronic Kidney Disease, was assessed separately, in order to establish a severity index risk of chronic kidney disease.

RESULTS

Renal survival at 5 years was 96% in the focal, 86% in the crescentic, 81% in the mixed and 61% in the sclerotic subgroups (P = 0.03). Myeloperoxidase (MPO)-ANCA was associated with more severe disease when compared with PR3-ANCA, as demonstrated by a lower frequency of focal and higher frequency of sclerotic subgroups, by more advanced interstitial fibrotic change and by lower glomerular filtration rate at diagnosis and worse renal function at 1 and 2 years.

CONCLUSIONS

We have confirmed the predictive value for renal survival of the ANCA vasculitis histology classification in a multi-centre study. We found a worse renal outcome in patients with tubulointerstitial fibrosis and atrophy. MPO-ANCA positive patients had a worse renal prognosis due to more severe glomerular injury. These results contribute to patient stratification in renal vasculitis for therapeutic, epidemiological and basic research.

Randomized, controlled, double-blind, cross-over trial assessing treatment preference for pazopanib versus sunitinib in patients with metastatic renal cell carcinoma: PISCES Study

PURPOSE

Patient-reported outcomes may help inform treatment choice in advanced/metastatic renal cell carcinoma (RCC), particularly between approved targeted therapies with similar efficacy. This double-blind cross-over study evaluated patient preference for pazopanib or sunitinib and the influence of health-related quality of life (HRQoL) and safety factors on their stated preference.

PATIENTS AND METHODS

Patients with metastatic RCC were randomly assigned to pazopanib 800 mg per day for 10 weeks, a 2-week washout, and then sunitinib 50 mg per day (4 weeks on, 2 weeks off, 4 weeks on) for 10 weeks, or the reverse sequence. The primary end point, patient preference for a specific treatment, was assessed by questionnaire at the end of the two treatment periods. Other end points and analyses included reasons for preference, physician preference, safety, and HRQoL.

RESULTS

Of 169 randomly assigned patients, 114 met the following prespecified modified intent-to-treat criteria for the primary analysis: exposure to both treatments, no disease progression before cross over, and completion of the preference questionnaire. Significantly more patients preferred pazopanib (70%) over sunitinib (22%); 8% expressed no preference (P < .001). All preplanned sensitivity analyses, including the intent-to-treat population, statistically favored pazopanib. Less fatigue and better overall quality of life were the main reasons for preferring pazopanib, with less diarrhea being the most cited reason for preferring sunitinib. Physicians also preferred pazopanib (61%) over sunitinib (22%); 17% expressed no preference. Adverse events were consistent with each drug's known profile. Pazopanib was superior to sunitinib in HRQoL measures evaluating fatigue, hand/foot soreness, and mouth/throat soreness.

CONCLUSION

This innovative cross-over trial demonstrated a significant patient preference for pazopanib over sunitinib, with HRQoL and safety as key influencing factors.

Models for the analysis of repeated continuous outcome measures in clinical trials

Repeated continuous outcome measures are common in clinical trials. In this tutorial style paper, using data collected from a trial evaluating an intervention for managing asthma and chronic obstructive pulmonary disease, we demonstrate ways of statistically analysing such data to answer frequently encountered clinical research questions. We illustrate the use of linear mixed effects modelling in doing so and discuss its advantages over several other commonly used approaches. The methods described in this paper can easily be carried out using standard statistical software.

Dose-finding design using mixed-effect proportional odds model for longitudinal graded toxicity data in phase I oncology clinical trials

Phase I oncology clinical trials are designed to identify the optimal dose that will be recommended for phase II trials. This dose is typically defined as the dose associated with a certain probability of severe toxicity during the first cycle of treatment, although toxicity is repeatedly measured over cycles on an ordinal scale. We propose a new adaptive dose-finding design using longitudinal measurements of ordinal toxic adverse events, with proportional odds mixed-effect models. Likelihood-based inference is implemented. The optimal dose is then the dose producing a target rate of severe toxicity per cycle. This model can also be used to identify cumulative or late toxicities. The performances of this approach were compared with those of the continual reassessment method in a simulation study. Operating characteristics were evaluated in terms of correct identification of the target dose, distribution of the doses allocated and power to detect trends in the risk of toxicities over time. This approach was also used to reanalyse data from a phase I oncology trial. Use of a proportional odds mixed-effect model appears to be feasible in phase I dose-finding trials, increases the ability of selecting the correct dose and provides a tool to detect cumulative effects.

Reduction in serum clusterin is a potential therapeutic biomarker in patients with castration-resistant prostate cancer treated with custirsen

Elevated levels of clusterin (CLU), a stress-induced and secreted cytoprotective chaperone, are associated with advanced tumor stage, metastasis, treatment resistance, and adverse outcome in several cancers. Custirsen, a second-generation antisense oligonucleotide, inhibits CLU production in tumor cells and reduces serum CLU levels. A Phase 2 study evaluated custirsen in combination with second-line chemotherapy in men with metastatic castration-resistant prostate cancer (mCRPC) who had progressed while on or within 6 months of first-line docetaxel-based chemotherapy. Exploratory analyses evaluated serum CLU levels during custirsen treatment and correlative clinical effects on prostate-specific antigen (PSA) response, overall survival, and any relationship between serum CLU and PSA. Men with mCRPC were treated with mitoxantrone/prednisone/custirsen (MPC, n = 22) or docetaxel retreatment/prednisone/custirsen (DPC plus DPC-Assigned, n = 45) in an open-label, multicenter study. Subject-specific profiles of PSA and serum CLU levels during treatment were characterized using statistical modeling to compute subject-specific summary measures; these measures were analyzed for relationship to survival using proportional hazard regression. Estimated individual serum CLU response profiles were scored as below or at/above the median level for the population through 100 days postrandomization. Median survival was longer for subjects scoring below the median serum CLU level compared with subjects at/above the median level, respectively (MPC: 15.1 months vs. 6.2 months; DPC-Pooled: 17.0 months vs. 12.1 months). Lowered serum CLU levels during custirsen treatment when in combination with either chemotherapy regimen were predictive of longer survival in mCRPC. These results support further evaluation of serum CLU as a therapeutic biomarker.

Effects of behavioural exercise therapy on the effectiveness of a multidisciplinary rehabilitation for chronic non-specific low back pain: study protocol for a randomised controlled trial

BACKGROUND

In Germany, a multidisciplinary rehabilitation named "behavioural medical rehabilitation" (BMR) is available for treatment of chronic low back pain (clbp). A central component of BMR is standard exercise therapy (SET), which is directed mainly to improve physical fitness. There is a need to address psychosocial factors within SET and therefore to improve behavior change with a focus on the development of self-management skills in dealing with clbp. Furthermore, short-term effectiveness of BMR with a SET has been proven, but the impact of a behavioural exercise therapy (BET) for improvement of the long-term effectiveness of BMR is unclear.

METHODS/DESIGN

To compare the effectiveness of two exercise programs with different approaches within BMR on the effects of BMR a prospective randomized controlled trial (RCT) in two rehabilitation centres will be performed. 214 patients aged 18-65 with clbp will be, based on an "urn randomisation"-algorithm, randomly assigned to a BMR with SET (function-oriented, n=107) and BMR with BET (behaviour-oriented, n=107). Both exercise programs have a mean duration of 26 hours in three weeks and are delivered by a limited number of not-blinded study therapists in closed groups with six to twelve patients who will be masked regarding study group. The main differences of BET lie in its detailed manualised program with a theory-based, goal-orientated combination of exercise, education and behavioural elements, active participation of patients and consideration of their individual preferences and previous experiences with exercise. The primary outcome is functional ability assessed with the Hannover Functional Ability Questionnaire directly before and after the rehabilitation program, as well as a six and twelve-month follow-up.

DISCUSSION

This RCT is designed to explore the effects of BET on the effectiveness of a BMR compared to a BMR with SET in the management of patients with clbp. Methodological challenges arise from conducting a RCT within routine health care as well as from ensuring high treatment integrity. Findings of this study might contribute to a better understanding of the mechanism of action of BMR and the special effects of BET and may be used to improve the quality of these interventions in routine care, therefore reducing the burden to patients with disabling clbp.

TRIAL REGISTRATION

Current controlled trials NCT01666639.

Growth after hematopoietic stem cell transplantation in children with acute myeloid leukemia

Previous studies have shown that hematopoietic stem cell transplantation (HSCT) may result in growth impairment. The purpose of this study was to evaluate the growth during 5 yr after HSCT and to determine factors that influence final adult height (FAH). We retrospectively reviewed the medical records of acute myeloid leukemia (AML) patients who received HSCT. Among a total of 37 eligible patients, we selected 24 patients who began puberty at 5 yr after HSCT (Group 1) and 19 patients who reached FAH without relapse (Group 2). In Group 1, with younger age at HSCT, sex, steroid treatment, hypogonadism and hypothyroidism were not significantly associated with growth impairment 5 yr after HSCT. History of radiotherapy (RT) significantly impaired the 5 yr growth after HSCT. Chronic graft-versus-host disease (cGVHD) only temporarily impaired growth after HSCT. In Group 2, with younger age at HSCT, steroid treatment and hypogonadism did not significantly reduce FAH. History of RT significantly reduced FAH. Growth impairment after HSCT may occur in AML patients, but in patients without a history of RT, growth impairment seemed to be temporary and was mitigated by catch-up growth.

Blood dioxin biomonitoring to assess local residents' exposure from a large urban remediation project

BACKGROUND

A total of 265000 m(3) of dioxin contaminated soil and sediments from past industrial activity was treated on site in an urban setting in Sydney, Australia. To respond to local community concerns about potential dioxin exposure from fugitive emissions a human biomonitoring study was undertaken.

OBJECTIVE

To determine whether local residents were exposed to significant amounts of dioxin from the remediation process.

METHODS

Blood samples were collected from local residents around the site and a representative metropolitan control group. They were pooled within age and sex strata and the change in dioxin concentrations over the remediation period and a summary of the mid point and post remediation dioxin concentrations were compared between groups. Information on dietary intake was collected to look for possible confounding.

RESULTS

The mean dioxin Toxic Equivalent concentrations (TeQ) decreased among both the local resident and control groups over the remediation period by 1.9 and 2.1 pg gm(-1) lipid respectively. Modelled blood concentrations adjusting for age and sex did not detect a significant difference between groups for changes in either TeQ or 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8 TCDD). The summary measure approach did however demonstrate that the 2,3,7,8 TCDD concentrations among the local resident group was approximately 0.7 pg g(-1) lipid higher compared to the control group post remediation. There were no significant changes in dietary intake sources of dioxin.

CONCLUSION

Biomonitoring demonstrated that local residents were not exposed to significant quantities of dioxin. Large scale remediation of dioxin contaminated land can be safely undertaken in an urban setting.

On summary measure analysis of linear trend repeated measures data: performance comparison with two competing methods

Background

The summary measure approach (SMA) is sometimes the only applicable tool for the analysis of repeated measurements in medical research, especially when the number of measurements is relatively large. This study aimed to describe techniques based on summary measures for the analysis of linear trend repeated measures data and then to compare performances of SMA, linear mixed model (LMM), and unstructured multivariate approach (UMA).

Methods

Practical guidelines based on the least squares regression slope and mean of response over time for each subject were provided to test time, group, and interaction effects. Through Monte Carlo simulation studies, the efficacy of SMA vs. LMM and traditional UMA, under different types of covariance structures, was illustrated. All the methods were also employed to analyze two real data examples.

Results

Based on the simulation and example results, it was found that the SMA completely dominated the traditional UMA and performed convincingly close to the best-fitting LMM in testing all the effects. However, the LMM was not often robust and led to non-sensible results when the covariance structure for errors was misspecified. The results emphasized discarding the UMA which often yielded extremely conservative inferences as to such data.

Conclusions

It was shown that summary measure is a simple, safe and powerful approach in which the loss of efficiency compared to the best-fitting LMM was generally negligible. The SMA is recommended as the first choice to reliably analyze the linear trend data with a moderate to large number of measurements and/or small to moderate sample sizes.

Extended key-factor/key-stage analysis for longitudinal data

Key-factor/key-stage analysis was originally a descriptive approach to analyze life tables. However, this method can be extended to analyze longitudinal data in pharmaceutical experiments. By dividing the variance into components, the extended key-factor/key-stage analysis indicates which factor is influential, and through which stage the factor generates its influence in determining the outcome of treatments. Such knowledge helps us in constructing a class of nonlinear longitudinal models that can be interpretable than linear models. Example SAS programs and R programs are provided for the calculation. Supplemental materials are available for this article. Go to the publisher's online edition of Journal of Biopharmaceutical Statistics to view the supplemental files.

Handling baselines in repeated measures analyses with missing data at random

In longitudinal clinical studies, after randomization at baseline, subjects are followed for a period of time for development of symptoms. A mixed model for repeated measures (MMRM) can be used to analyze data from such studies. Fitzmaurice et al. (2004) outlined five approaches for handling baseline responses in an MMRM analysis. They are: (1) Retain the baselines as part of the outcome vector and make no assumptions about group differences in the mean response at baseline. (2) Retain the baselines as part of the outcome vector and assume the group means are equal at baseline. (3) Subtract the baselines from all of the remaining post-baseline responses, and analyze the differences from baseline. (4) Use the baselines as a covariate in the analysis of the post-baseline responses, assuming homogeneous regression slopes. (5) Use the baselines as a covariate in the analysis of the post-baseline responses, allowing different regression slopes. In this paper, we evaluate these five approaches in the presence of data missing at random. We evaluate the approaches based on the bias of the estimate and the coverage accuracy of the confidence interval. The results suggest that strategies 2 and 5 are recommended.