Mastering variation: variance components and personalised medicine

Risk-benefit trade-offs and precision utilities in phase I-II clinical trials

BACKGROUND

Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life.

PURPOSE

While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups.

METHODS AND CONCLUSIONS

Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

Treatment effect heterogeneity of cognitive behavioral therapy for insomnia - A meta-analysis

Investigation of the heterogeneity of the treatment effect (HTE) might guide the optimization of cognitive behavioral therapy for insomnia (CBT-I). This study examined HTE in CBT-I thereby analyzing if treatment setting, control group, different CBT-I components, and patient characteristics drive HTE. Randomized controlled trials investigating CBT-I were included. Bayesian random effect meta-regressions were specified to examine variances between the intervention and control groups regarding post-treatment symptom severity. Subgroup analyses analyzing treatment setting and control groups and covariate analysis analyzing treatment components and patient characteristics were specified. No significant HTE in CBT-I was found for the overall data set, settings and control groups. The covariate analyses yielded significant results for baseline severity and the treatment component relaxation therapy. Thus, this study identified potential causes for HTE in CBT-I for the first time, showing that it might be worthwhile to further examine possibilities for precision medicine in CBT-I.

Immediate effects of Ramadan on objective time asleep in male youth football players from the Middle East: an interrupted time-series study

OBJECTIVE

To examine the abrupt effects of Ramadan onset on actigraphy-based time asleep in male youth Muslim football players.

METHODS

We adopted a quasi-experimental, interrupted time-series research design and tracked objective time asleep over a minimum of 12 consecutive nights in the two weeks prior to and immediately after Ramadan onset, respectively. Twenty-two, male academy student-athletes (chronological age range: 12.6 to 16.2 years) participated in the study (464 individual observations). Segmented generalized mixed-effects modelling estimated the effects of Ramadan onset on time asleep during the first period of night sleep only.

RESULTS

Ramadan onset led to an immediate mean reduction of 89 min (95% confidence interval [CI], 54 to 123 min) in time asleep during the first period of night sleep compared to pre-Ramadan sleep patterns. Model-adjusted estimated marginal means for time asleep were ~ 5.7 h (95%CI, 5.1 to 6.2 h) before and ~ 4.2 h (95%CI, 3.6 to 4.7 h) after Ramadan onset. Night sleep interruptions resulting in two or more fragmented periods accounted for 8% (95%CI, 2 to 21%) to 19% (95%, 11 to 29%) of sleep observations before and after Ramadan onset, respectively.

CONCLUSIONS

The onset of Ramadan determined an abrupt reduction in time asleep of ~ 1 h 30 min in the first period of a night cycle and contributed to additional problems of heterogeneous sleep fragmentation that can impact optimal school learning and youth athlete performance development processes.

Night-to-night variability of objective sleep outcomes in youth Middle Eastern football players

OBJECTIVE

To describe components of night-to-night variation in objective measures of sleep.

METHODS

We conducted a secondary data analysis of consecutive and chronologically ordered actigraphy-based measurements for time in bed (min), time asleep (min), and wake-after-sleep onset (min). This investigation examined 575 individual night-based measures available for a sub-sample of fifty-two, male youth Middle Eastern football players tracked over a 14-day surveillance period (chronological age range: 12.1 to 16 years). Distinct multivariable-adjusted generalized additive models included each objective sleep outcome measure as dependent variable and disaggregated components of variation for night measurement-by-sleep period interaction, week part (weekday or weekend), and study participant random effects from within-subject night-to-night sleep variation.

RESULTS

The within-subject standard deviation (SD) of ±98 min (95% confidence interval [CI], 92 to 104 min) for time in bed, ±87 min (95%CI, 82 to 93 min) for time asleep, and ±23 min (95%CI, 22 to 25 min) for wake-after-sleep-onset overwhelmed other sources of variability and accounted for ∼44% to 53% of the overall night-to-night variation. The night measurement-by-fragmented sleep period interaction SD was ±83 min (95%CI, 44 to 156 min) for time in bed, ±67 min (95%CI, 34 to 131 min) for time asleep, and ±15 min (95%CI, 7 to 32 min) for wake-after-sleep-onset that accounted for ∼22% to 32% of each sleep outcome measure overall variability.

CONCLUSIONS

Substantial random night-to-night within-subject variability poses additional challenges for strategies aiming to mitigate problems of insufficient and inconsistent sleep that are detrimental to school learning and youth athlete development processes.

Use of conditional estimates of effect in cancer epidemiology: An application to lung cancer treatment

BACKGROUND

In oncology clinical trials, there is the assumption that randomization sufficiently balances confounding covariates and therefore average treatment effects are usually reported. This paper explores the wider benefits provided by conditioning on covariates for reasons other than mitigation of confounding.

METHODS

We reanalyzed the data from primary randomized controlled trials listed in two meta-analyses to explore the significance of conditioning on smoking status in terms of the effect magnitude of treatment on progression free survival in non-small cell lung cancer.

RESULTS

The reanalysis revealed that conditioning on smoking status using sub-group analyses provided the closest empiric estimate of individual treatment effect based on smoking status and significantly reduced the heterogeneity of treatment effect observed across studies. In addition, smoking status was determined to be a modifier of the effect of treatment.

CONCLUSION

Conditioning on prognostic covariates in randomized trials in oncology helps generate the closest empiric estimates of individual treatment benefit, addresses heterogeneity due to varying covariate distributions across trials and facilitates future decision making as well as evidence synthesis. Conditioning using sub-group analyses also allows examination for effect modification in meta-analysis.

Advancing athletic assessment by integrating conventional methods with cutting-edge biomedical technologies for comprehensive performance, wellness, and longevity insights

In modern athlete assessment, the integration of conventional biochemical and ergophysiologic monitoring with innovative methods like telomere analysis, genotyping/phenotypic profiling, and metabolomics has the potential to offer a comprehensive understanding of athletes' performance and potential longevity. Telomeres provide insights into cellular functioning, aging, and adaptation and elucidate the effects of training on cellular health. Genotype/phenotype analysis explores genetic variations associated with athletic performance, injury predisposition, and recovery needs, enabling personalization of training plans and interventions. Metabolomics especially focusing on low-molecular weight metabolites, reveal metabolic pathways and responses to exercise. Biochemical tests assess key biomarkers related to energy metabolism, inflammation, and recovery. Essential elements depict the micronutrient status of the individual, which is critical for optimal performance. Echocardiography provides detailed monitoring of cardiac structure and function, while burnout testing evaluates psychological stress, fatigue, and readiness for optimal performance. By integrating this scientific testing battery, a multidimensional understanding of athlete health status can be achieved, leading to personalized interventions in training, nutrition, supplementation, injury prevention, and mental wellness support. This scientifically rigorous approach hereby presented holds significant potential for improving athletic performance and longevity through evidence-based, individualized interventions, contributing to advances in the field of sports performance optimization.

Are There Interindividual Responses of Cardiovascular Disease Risk Markers to Acute Exercise? A Replicate Crossover Trial

PURPOSE

Using a replicated crossover design, we quantified the response heterogeneity of postprandial cardiovascular disease risk marker responses to acute exercise.

METHODS

Twenty men (mean (SD) age, 26 (6) yr; body mass index, 23.9 (2.4) kg·m -2 ) completed four 2-d conditions (two control, two exercise) in randomized orders. On days 1 and 2, participants rested and consumed two high-fat meals over 9 h. Participants ran for 60 min (61 (7)% of peak oxygen uptake) on day 1 (6.5 to 7.5 h) of both exercise conditions. Time-averaged total area under the curve (TAUC) for triacylglycerol, glucose, and insulin were calculated from 11 venous blood samples on day 2. Arterial stiffness and blood pressure responses were calculated from measurements at baseline on day 1 and at 2.5 h on day 2. Consistency of individual differences was explored by correlating the two replicates of control-adjusted exercise responses for each outcome. Within-participant covariate-adjusted linear mixed models quantified participant-by-condition interactions and individual response SDs.

RESULTS

Acute exercise reduced mean TAUC-triacylglycerol (-0.27 mmol·L -1 ·h; Cohen's d = 0.29, P = 0.017) and TAUC-insulin (-25 pmol·L -1 ·h; Cohen's d = 0.35, P = 0.022) versus control, but led to negligible changes in TAUC-glucose and the vascular outcomes (Cohen's d ≤ 0.36, P ≥ 0.106). Small-to-moderate, but nonsignificant, correlations were observed between the two response replicates ( r = -0.42 to 0.15, P ≥ 0.066). We did not detect any individual response heterogeneity. All participant-by-condition interactions were P ≥ 0.137, and all individual response SDs were small with wide 95% confidence intervals overlapping zero.

CONCLUSIONS

Large trial-to-trial within-subject variability inhibited detection of consistent interindividual variability in postprandial metabolic and vascular responses to acute exercise.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research

It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.

Personalized redox biology: Designs and concepts

Personalized interventions are regarded as a next-generation approach in almost all fields of biomedicine, such as clinical medicine, exercise, nutrition and pharmacology. At the same time, an increasing body of evidence indicates that redox processes regulate, at least in part, multiple aspects of human physiology and pathology. As a result, the idea of applying personalized redox treatments to improve their efficacy has gained popularity among researchers in recent years. The aim of the present primer-style review was to highlight some crucial yet underappreciated methodological, statistical, and interpretative concepts within the redox biology literature, while also providing a physiology-oriented perspective on personalized redox biology. The topics addressed are: (i) the critical issue of investigating the potential existence of inter-individual variability; (ii) the importance of distinguishing a genuine and consistent response of a subject from a chance finding; (iii) the challenge of accurately quantifying the effect of a redox treatment when dealing with 'extreme' groups due to mathematical coupling and regression to the mean; and (iv) research designs and analyses that have been implemented in other fields, and can be reframed and exploited in a redox biology context.

Why Humble Farmers May in Fact Grow Bigger Potatoes: A Call for Street-Smart Decision-Making in Sport

BACKGROUND

The main task of applied sport science is to inform decision-making in sports practice, that is, enabling practitioners to compare the expectable outcomes of different options (e.g. training programs).

MAIN BODY

The "evidence" provided may range from group averages to multivariable prediction models. By contrast, many decisions are still largely based on the subjective, experience-based judgement of athletes and coaches. While for the research scientist this may seem "unscientific" and even "irrational", it is important to realize the different perspectives: science values novelty, universal validity, methodological rigor, and contributions towards long-term advancement. Practitioners are judged by the performance outcomes of contemporary, specific athletes. This makes out-of-sample predictive accuracy and robustness decisive requirements for useful decision support. At this point, researchers must concede that under the framework conditions of sport (small samples, multifactorial outcomes etc.) near certainty is unattainable, even with cutting-edge methods that might theoretically enable near-perfect accuracy. Rather, the sport ecosystem favors simpler rules, learning by experience, human judgement, and integration across different sources of knowledge. In other words, the focus of practitioners on experience and human judgement, complemented-but not superseded-by scientific evidence is probably street-smart after all. A major downside of this human-driven approach is the lack of science-grade evaluation and transparency. However, methods are available to merge the assets of data- and human-driven strategies and mitigate biases.

SHORT CONCLUSION

This work presents the challenges of learning, forecasting and decision-making in sport as well as specific opportunities for turning the prevailing "evidence vs. eminence" contrast into a synergy.

Interpreting Randomized Controlled Trials

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers

BACKGROUND

Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention "works" and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation.

METHODS

In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software.

RESULTS

Drawing on Rubin's potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset.

DISCUSSION

Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable "weak spots" of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care.

Inter-individual variability in redox and performance responses after antioxidant supplementation: A randomized double blind crossover study

AIM

We aimed to investigate the inter-individual variability in redox and physiological responses of antioxidant-deficient subjects after antioxidant supplementation.

METHODS

Two hundred individuals were sorted by plasma vitamin C levels. A low vitamin C group (n = 22) and a control group (n = 22) were compared in terms of oxidative stress and performance. Subsequently, the low vitamin C group received for 30 days vitamin C (1 g) or placebo, in randomized, double-blind, crossover fashion, and the effects were examined through a mixed-effects model, while individual responses were calculated.

RESULTS

The low vitamin C group exhibited lower vitamin C (-25 μmol/L; 95%CI[-31.7, -18.3]; p < 0.001), higher F2 -isoprostanes (+17.1 pg/mL; 95%CI[6.5, 27.7]; p = 0.002), impaired VO2max (-8.2 mL/kg/min; 95%CI[-12.8, -3.6]; p < 0.001) and lower isometric peak torque (-41.5 Nm; 95%CI[-61.8, -21.2]; p < 0.001) compared to the control group. Regarding antioxidant supplementation, a significant treatment effect was found in vitamin C (+11.6 μmol/L; 95%CI[6.8, 17.1], p < 0.001), F2 -isoprostanes (-13.7 pg/mL; 95%CI[-18.9, -8.4], p < 0.001), VO2max (+5.4 mL/kg/min; 95%CI[2.7, 8.2], p = 0.001) and isometric peak torque (+18.7; 95%CI[11.8, 25.7 Nm], p < 0.001). The standard deviation for individual responses (SDir) was greater than the smallest worthwhile change (SWC) for all variables indicating meaningful inter-individual variability. When a minimal clinically important difference (MCID) was set, inter-individual variability remained for VO2max , but not for isometric peak torque.

CONCLUSION

The proportion of response was generally high after supplementation (82.9%-95.3%); however, a few participants did not benefit from the treatment. This underlines the potential need for personalized nutritional interventions in an exercise physiology context.

Psychological and neurological predictors of acupuncture effect in patients with chronic pain: a randomized controlled neuroimaging trial

ABSTRACT

Chronic pain has been one of the leading causes of disability. Acupuncture is globally used in chronic pain management. However, the efficacy of acupuncture treatment varies across patients. Identifying individual factors and developing approaches that predict medical benefits may promise important scientific and clinical applications. Here, we investigated the psychological and neurological factors collected before treatment that would determine acupuncture efficacy in knee osteoarthritis. In this neuroimaging-based randomized controlled trial, 52 patients completed a baseline assessment, 4-week acupuncture or sham-acupuncture treatment, and an assessment after treatment. The patients, magnetic resonance imaging operators, and outcome evaluators were blinded to treatment group assignment. First, we found that patients receiving acupuncture treatment showed larger pain intensity improvements compared with patients in the sham-acupuncture arm. Second, positive expectation, extraversion, and emotional attention were correlated with the magnitude of clinical improvements in the acupuncture group. Third, the identified neurological metrics encompassed striatal volumes, posterior cingulate cortex (PCC) cortical thickness, PCC/precuneus fractional amplitude of low-frequency fluctuation (fALFF), striatal fALFF, and graph-based small-worldness of the default mode network and striatum. Specifically, functional metrics predisposing patients to acupuncture improvement changed as a consequence of acupuncture treatment, whereas structural metrics remained stable. Furthermore, support vector machine models applied to the questionnaire and brain features could jointly predict acupuncture improvement with an accuracy of 81.48%. Besides, the correlations and models were not significant in the sham-acupuncture group. These results demonstrate the specific psychological, brain functional, and structural predictors of acupuncture improvement and may offer opportunities to aid clinical practices.

Brain Structure Measurements Predict Individualized Treatment Outcome of 12-Week Antipsychotic Monotherapies in First-episode Schizophrenia

BACKGROUND AND HYPOTHESIS

Early prediction of treatment response to antipsychotics in schizophrenia remains a challenge in clinical practice. This study aimed to investigate if brain morphometries including gray matter volume and cortical thickness could serve as potential predictive biomarkers in first-episode schizophrenia.

STUDY DESIGN

Sixty-eight drug-naïve first-episode patients underwent baseline structural MRI scans and were subsequently randomized to receive a single antipsychotic throughout the first 12 weeks. Assessments for symptoms and social functioning were conducted by eight "core symptoms" selected from the Positive and Negative Syndrome Scale (PANSS-8) and the Personal and Social performance scale (PSP) multiple times during follow-ups. Treatment outcome was evaluated as subject-specific slope coefficients for PANSS-8 and PSP scores using linear mixed model. LASSO regression model were conducted to examine the performance of baseline gray matter volume and cortical thickness in prediction of individualized treatment outcome.

STUDY RESULTS

The study showed that individual brain morphometries at baseline, especially the orbitofrontal, temporal and parietal cortex, pallidum and amygdala, significantly predicted 12-week treatment outcome of PANSS-8 (r[predicted vs observed] = 0.49, P = .001) and PSP (r[predicted vs observed] = 0.40, P = .003) in first-episode schizophrenia. Moreover, the gray matter volume performed better than cortical thickness in the prediction the symptom changes (P = .034), while cortical thickness outperformed gray matter volume in the prediction of outcome of social functioning (P = .029).

CONCLUSIONS

These findings provide initial evidence that brain morphometry have potential to be used as prognostic predictors for antipsychotic response in patients, encouraging the future investigation of the translational value of these measures in precision psychiatry.

Heterogeneity in Blood Pressure Response to 4 Antihypertensive Drugs: A Randomized Clinical Trial

Importance

Hypertension is the leading risk factor for premature death worldwide. Multiple blood pressure-lowering therapies are available but the potential for maximizing benefit by personalized targeting of drug classes is unknown.

Objective

To investigate and quantify the potential for targeting specific drugs to specific individuals to maximize blood pressure effects.

Design, Setting, and Participants

A randomized, double-blind, repeated crossover trial in men and women with grade 1 hypertension at low risk for cardiovascular events at an outpatient research clinic in Sweden. Mixed-effects models were used to assess the extent to which individuals responded better to one treatment than another and to estimate the additional blood pressure lowering achievable by personalized treatment.

Interventions

Each participant was scheduled for treatment in random order with 4 different classes of blood pressure-lowering drugs (lisinopril [angiotensin-converting enzyme inhibitor], candesartan [angiotensin-receptor blocker], hydrochlorothiazide [thiazide], and amlodipine [calcium channel blocker]), with repeated treatments for 2 classes.

Main Outcomes and Measures

Ambulatory daytime systolic blood pressure, measured at the end of each treatment period.

Results

There were 1468 completed treatment periods (median length, 56 days) recorded in 270 of the 280 randomized participants (54% men; mean age, 64 years). The blood pressure response to different treatments varied considerably between individuals (P < .001), specifically for the choices of lisinopril vs hydrochlorothiazide, lisinopril vs amlodipine, candesartan vs hydrochlorothiazide, and candesartan vs amlodipine. Large differences were excluded for the choices of lisinopril vs candesartan and hydrochlorothiazide vs amlodipine. On average, personalized treatment had the potential to provide an additional 4.4 mm Hg-lower systolic blood pressure.

Conclusions and Relevance

These data reveal substantial heterogeneity in blood pressure response to drug therapy for hypertension, findings that may have implications for personalized therapy.

Trial Registration

ClinicalTrials.gov Identifier: NCT02774460.

The Central Role of the Concept of Person in Psychological Science

The first article in the present issue of JPOR is written by two pioneers in person-oriented research, John Nesselroade and Peter Molenaar. Among other things they argue that the individual is the primary unit of analysis for studying behaviour, and that this is in line with a growing emphasis on personalized diagnoses and treatment regimens in medicine, which reflects a renewed emphasis on focusing on the individual person. As was argued by Julia Moeller in a previous article in JPOR, however, psychological science seems to “lag behind” in this respect, with a concomitant risk of a credibility loss. One problem is that psychology still lack a coherent theoretical paradigm that places the person at the center of the stage. Some promising theoretical work on the concept of person has been carried out by researchers such as Mark Bickhard and Peter Ossorio, and it is possible that the future will see an increased cross-fertilization between (1) the theoretical development of a comprehensive model of the person and (2) methodological developments that facilitate the study of developmental processes at the level of the individual person.

Is it worth it to personalize the treatment of PTSD? - A variance-ratio meta-analysis and estimation of treatment effect heterogeneity in RCTs of PTSD

Several evidence-based treatments for posttraumatic stress disorder (PTSD) are recommended by international guidelines (e.g., APA, NICE). While their average effects are in general high, non-response rates indicate differential treatment effects. Here, we used a large database of RCTs on psychotherapy for PTSD to determine a reliable estimate of this heterogeneity in treatment effects (HTE) by applying Bayesian variance ratio meta-analysis. In total, 66 studies with a total of 8803 patients were included in our study. HTE was found for all psychological treatments, with varying degrees of certainty, only slight differences between psychological treatments, and active control groups yielding a smaller variance ratio compared to waiting list control groups. Across all psychological treatment and control group types, the estimate for the intercept was 0.12, indicating a 12% higher variance of posttreatment values in the intervention groups after controlling for differences in treatment outcomes. This study is the first to determine the maximum increase in treatment effects of psychological treatments for PTSD by personalization. The results indicate that there is comparatively high heterogeneity in treatment effects across all psychological treatment and control groups, which in turn allow personalizing psychological treatments by using treatment selection approaches.

Measuring Individual Benefits of Medical Treatments Using Longitudinal Hospital Data with Non-Ignorable Missing Responses Caused by Patient Discharge: Application to the Study of Benefits of Pain Management Post Spinal Fusion

Electronic health records (EHR) provide valuable resources for longitudinal studies and understanding risk factors associated with poor clinical outcomes. However, they may not contain complete follow-ups, and the missing data may not be at random since hospital discharge may depend in part on expected but unrecorded clinical outcomes that occur after patient discharge. These non-ignorable missing data requires appropriate analysis methods. Here, we are interested in measuring and analyzing individual treatment benefits of medical treatments in patients recorded in EHR databases. We present a method for predicting individual benefits that handles non-ignorable missingness due to hospital discharge. The longitudinal clinical outcome of interest is modeled simultaneously with the hospital length of stay using a joint mixed-effects model, and individual benefits are predicted through a frequentist approach: the empirical Bayesian approach. We illustrate our approach by assessing individual pain management benefits to patients who underwent spinal fusion surgery. By calculating sample percentiles of empirical Bayes predictors of individual benefits, we examine the evolution of individual benefits over time. We additionally compare these percentiles with percentiles calculated with a Monte Carlo approach. We showed that empirical Bayes predictors of individual benefits do not only allow examining benefits in specific patients but also reflect overall population trends reliably.

The Big Data Paradox in Clinical Practice

The big data paradox is a real-world phenomenon whereby as the number of patients enrolled in a study increases, the probability that the confidence intervals from that study will include the truth decreases. This occurs in both observational and experimental studies, including randomized clinical trials, and should always be considered when clinicians are interpreting research data. Furthermore, as data quantity continues to increase in today's era of big data, the paradox is becoming more pernicious. Herein, I consider three mechanisms that underlie this paradox, as well as three potential strategies to mitigate it: (1) improving data quality; (2) anticipating and modeling patient heterogeneity; (3) including the systematic error, not just the variance, in the estimation of error intervals.

Walking and resting blood pressure: An inter-individual response difference meta-analysis of randomized controlled trials

While walking is associated with reductions in resting SBP and DBP, a lack of true IIRD exists, suggesting that factors other than training response variation (random variation, physiological responses associated with behavioral changes that are not the result of walking) are responsible for the observed variation in resting SBP and DBP.

Anthocyanin-Rich Supplementation: Emerging Evidence of Strong Potential for Sport and Exercise Nutrition

Dark-colored fruits, especially berries, have abundant presence of the polyphenol anthocyanin which have been show to provide health benefits. Studies with the berry blackcurrant have provided notable observations with application for athletes and physically active individuals. Alterations in exercise-induced substrate oxidation, exercise performance of repeated high-intensity running and cycling time-trial and cardiovascular function at rest and during exercise were observed with intake of New Zealand blackcurrant. The dynamic plasma bioavailability of the blackcurrant anthocyanins and the anthocyanin-derived metabolites must have changed cell function to provide meaningful in-vivo physiological effects. This perspective will reflect on the research studies for obtaining the applied in-vivo effects by intake of anthocyanin-rich supplementation, the issue of individual responses, and the emerging strong potential of anthocyanins for sport and exercise nutrition. Future work with repeated intake of known amount and type of anthocyanins, gut microbiota handling of anthocyanins, and coinciding measurements of plasma anthocyanin and anthocyanin-derived metabolites and in-vivo cell function will be required to inform our understanding for the unique potential of anthocyanins as a nutritional ergogenic aid for delivering meaningful effects for a wide range of athletes and physically active individuals.

What Is the Numerical Nature of Pain Relief?

Pain relief, or a decrease in self-reported pain intensity, is frequently the primary outcome of pain clinical trials. Investigators commonly report pain relief in one of two ways: using raw units (additive) or using percentage units (multiplicative). However, additive and multiplicative scales have different assumptions and are incompatible with one another. In this work, we describe the assumptions and corollaries of additive and multiplicative models of pain relief to illuminate the issue from statistical and clinical perspectives. First, we explain the math underlying each model and illustrate these points using simulations, for which readers are assumed to have an understanding of linear regression. Next, we connect this math to clinical interpretations, stressing the importance of statistical models that accurately represent the underlying data; for example, how using percent pain relief can mislead clinicians if the data are actually additive. These theoretical discussions are supported by empirical data from four longitudinal studies of patients with subacute and chronic pain. Finally, we discuss self-reported pain intensity as a measurement construct, including its philosophical limitations and how clinical pain differs from acute pain measured during psychophysics experiments. This work has broad implications for clinical pain research, ranging from statistical modeling of trial data to the use of minimal clinically important differences and patient-clinician communication.

Personalized medicine for rare neurogenetic disorders: can we make it happen?

Rare neurogenetic disorders are collectively common, affecting 3% of the population, and often manifest with complex multiorgan comorbidity. With advances in genetic, -omics, and computational analysis, more children can be diagnosed and at an earlier age. Innovations in translational research facilitate the identification of treatment targets and development of disease-modifying drugs such as gene therapy, nutraceuticals, and drug repurposing. This increasingly allows targeted therapy to prevent the often devastating manifestations of rare neurogenetic disorders. In this perspective, successes in diagnosis, prevention, and treatment are discussed with a focus on inherited disorders of metabolism. Barriers for the identification, development, and implementation of rare disease-specific therapies are discussed. New methodologies, care networks, and collaborative frameworks are proposed to optimize the potential of personalized genomic medicine to decrease morbidity and improve lives of these vulnerable patients.

Risk factors relate to the variability of health outcomes as well as the mean: A GAMLSS tutorial

Background:

Risk factors or interventions may affect the variability as well as the mean of health outcomes. Understanding this can aid aetiological understanding and public health translation, in that interventions which shift the outcome mean and reduce variability are typically preferable to those which affect only the mean. However, most commonly used statistical tools do not test for differences in variability. Tools that do have few epidemiological applications to date, and fewer applications still have attempted to explain their resulting findings. We thus provide a tutorial for investigating this using GAMLSS (Generalised Additive Models for Location, Scale and Shape).

Methods:

The 1970 British birth cohort study was used, with body mass index (BMI; N = 6007) and mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale; N = 7104) measured in midlife (42–46 years) as outcomes. We used GAMLSS to investigate how multiple risk factors (sex, childhood social class, and midlife physical inactivity) related to differences in health outcome mean and variability.

Results:

Risk factors were related to sizable differences in outcome variability—for example males had marginally higher mean BMI yet 28% lower variability; lower social class and physical inactivity were each associated with higher mean and higher variability (6.1% and 13.5% higher variability, respectively). For mental wellbeing, gender was not associated with the mean while males had lower variability (–3.9%); lower social class and physical inactivity were each associated with lower mean yet higher variability (7.2% and 10.9% higher variability, respectively).

Conclusions:

The results highlight how GAMLSS can be used to investigate how risk factors or interventions may influence the variability in health outcomes. This underutilised approach to the analysis of continuously distributed outcomes may have broader utility in epidemiologic, medical, and psychological sciences. A tutorial and replication syntax is provided online to facilitate this (https://osf.io/5tvz6/).

Funding:

DB is supported by the Economic and Social Research Council (grant number ES/M001660/1), The Academy of Medical Sciences / Wellcome Trust (“Springboard Health of the Public in 2040” award: HOP001/1025); DB and LW are supported by the Medical Research Council (MR/V002147/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Test-Retest Reliability and Consistency of HVPG and Impact on Trial Design: A Study in 289 Patients from 20 Randomized Controlled Trials

BACKGROUND AND AIMS

Portal hypertension (PH) is a major driver for cirrhosis complications. Portal pressure is estimated in practice by the HVPG. The assessment of HVPG changes has been used for drug development in PH. This study aimed at quantifying the test-retest reliability and consistency of HVPG in the specific context of randomized controlled trials (RCTs) for the treatment of PH in cirrhosis and its impact on power calculations for trial design.

APPROACH AND RESULTS

We conducted a search of published RCTs in patients with cirrhosis reporting individual patient-level data of HVPG at baseline and after an intervention, which included a placebo or an untreated control arm. Baseline and follow-up HVPGs in the control groups were extracted after digitizing the plots. We assessed reliability and consistency and the potential impact of study characteristics. We retrieved a total of 289 before and after HVPG measurements in the placebo/untreated groups from 20 RCTs. The time span between the two HVPG measurements ranged between 20 minutes and 730 days. Pre-/post-HVPG variability was lower in studies including only compensated patients; therefore, modeled sample size calculations for trials in compensated cirrhosis were lower than for decompensated cirrhosis. A higher proportion of alcohol-associated cirrhosis and unicentric trials was associated with lower differences between baseline and follow-up measurements. The smallest detectable difference in an individual was 26% and 30% in compensated and decompensated patients, respectively.

CONCLUSIONS

The test-retest reliability of HVPG is overall excellent. Within-individual variance was higher in studies including higher proportions of decompensated patients. These findings should be taken into account when performing power analysis for trials based on the effects on HVPG or when considering HVPG as a tool to guide therapy of PH.

Classification of Companion Diagnostics: A New Framework for Biomarker-Driven Patient Selection

Background

Modern personalized medicine strategies builds on therapy companion diagnostics to stratify patients into subgroups with differential benefit/risk. In general, stratification for drug response implies a treatment-by-subgroup interaction. This interaction is usually suggested by the drug’s mechanism of action and investigated in pharmacological research or in clinical studies. In these candidate genes or pathway approaches, either biological reasons for a differential benefit/risk or statistical interaction regarding a pharmacological or clinical endpoint or both may be given. For successful drug approval, demonstration of a positive benefit/risk balance in the intended patient population is required. This also applies to situations with biomarker-selected populations. However, further regulatory considerations relate to the usefulness and plausibility of the selected patients and benefit/risk extrapolations or alternative therapy options in biomarker-negative populations.

Methods

To facilitate the specification of regulatory requirements and support the design of clinical development programmes, a systematic classification of biomarker-drug pairs is needed, in particular with regard to the expected underlying molecular mechanism and the clinical evidence.

Results

A classification of five biomarker-drug categories is proposed related to increasing evidence on the biomarker’s predictive value in relation to a specific drug. We classified biomarkers into five ascending categories with increasing evidence on the predictive nature of the biomarker in relation to a specific drug according to the comparative pharmacological and clinical evidence.

Conclusions

The proposed classification will facilitate regulatory decision-making and support drug development with respect to biomarker-related subgrouping, both, during clinical programme and at the time of marketing authorization application, since the grade of evidence on the differential power of the biomarker can be considered as an indicator for the usefulness of a biomarker-related subgrouping.

Detecting Heterogeneity of Intervention Effects Using Analysis and Meta-analysis of Differences in Variance Between Trial Arms

BACKGROUND

Randomized controlled trials (RCTs) with continuous outcomes usually only examine mean differences in response between trial arms. If the intervention has heterogeneous effects, then outcome variances will also differ between arms. Power of an individual trial to assess heterogeneity is lower than the power to detect the same size of main effect.

METHODS

We describe several methods for assessing differences in variance in trial arms and apply them to a single trial with individual patient data and to meta-analyses using summary data. Where individual data are available, we use regression-based methods to examine the effects of covariates on variation. We present an additional method to meta-analyze differences in variances with summary data.

RESULTS

In the single trial, there was agreement between methods, and the difference in variance was largely due to differences in prevalence of depression at baseline. In two meta-analyses, most individual trials did not show strong evidence of a difference in variance between arms, with wide confidence intervals. However, both meta-analyses showed evidence of greater variance in the control arm, and in one example, this was perhaps because mean outcome in the control arm was higher.

CONCLUSIONS

Using meta-analysis, we overcame low power of individual trials to examine differences in variance using meta-analysis. Evidence of differences in variance should be followed up to identify potential effect modifiers and explore other possible causes such as varying compliance.

Examining Side Effect Variability of Antipsychotic Treatment in Schizophrenia Spectrum Disorders: A Meta-analysis of Variance

Side effects of antipsychotic drugs play a key role in nonadherence of treatment in schizophrenia spectrum disorders (SSD). While clinical observations suggest that side effect variability between patients may be considerable, statistical evidence is required to confirm this. Here, we hypothesized to find larger side effect variability under treatment compared with control. We included double-blind, placebo-controlled, randomized controlled trials (RCTs) of adults with a diagnosis of SSD treated with 1 out of 14 antipsychotics. Standard deviations of the pre-post treatment differences of weight gain, prolactin levels, and corrected QT (QTc) times were extracted. The outcome measure was the variability ratio of treatment to control for individual antipsychotic drugs and the overall variability ratio of treatment to control across RCTs. Individual variability ratios were weighted by the inverse-variance method and entered into a random-effects model. We included N = 16 578 patients for weight gain, N = 16 633 patients for prolactin levels, and N = 10 384 patients for QTc time. Variability ratios (VR) were significantly increased for weight gain (VR = 1.08; 95% CI: 1.02-1.14; P = .004) and prolactin levels (VR = 1.38; 95% CI: 1.17-1.62; P < .001) but did not reach significance for QTc time (VR = 1.05; 95% CI: 0.98-1.12; P = 0.135). We found marked differences between individual antipsychotics and increased variability in side effects in patients under treatment with antipsychotics suggesting that subgroups of patients or individual patients may benefit from treatment allocation through stratified or personalized medicine.

A permutation test for assessing the presence of individual differences in treatment effects

An important goal of personalized medicine is to identify heterogeneity in treatment effects and then use that heterogeneity to target the intervention to those most likely to benefit. Heterogeneity is assessed using the predicted individual treatment effects framework, and a permutation test is proposed to establish if significant heterogeneity is present given the covariates and predictive model or algorithm used for predicted individual treatment effects. We first show evidence for heterogeneity in the effects of treatment across an illustrative example data set. We then use simulations with two different predictive methods (linear regression model and Random Forests) to show that the permutation test has adequate type-I error control. Next, we use an example dataset as the basis for simulations to demonstrate the ability of the permutation test to find heterogeneity in treatment effects for a predicted individual treatment effects estimate as a function of both effect size and sample size. We find that the proposed test has good power for detecting heterogeneity in treatment effects when the heterogeneity was due primarily to a single predictor, or when it was spread across the predictors. Power was found to be greater for predictions from a linear model than from random forests. This non-parametric permutation test can be used to test for significant differences across individuals in predicted individual treatment effects obtained with a given set of covariates using any predictive method with no additional assumptions.

Application of Transcranial Direct Current Stimulation in Psychiatry

Transcranial direct current stimulation (tDCS) is a neuromodulation technique, which noninvasively alters cortical excitability via weak polarizing currents between two electrodes placed on the scalp. Since it is comparably easy to handle, cheap to use and relatively well tolerated, tDCS has gained increasing interest in recent years. Based on well-known behavioral effects, a number of clinical studies have been performed in populations including patients with major depressive disorder followed by schizophrenia and substance use disorders, in sum with heterogeneous results with respect to efficacy. Nevertheless, the potential of tDCS must not be underestimated since it could be further improved by systematically investigating the various stimulation parameters to eventually increase clinical efficacy. The present article briefly explains the underlying physiology of tDCS, summarizes typical stimulation protocols and then reviews clinical efficacy for various psychiatric disorders as well as prevalent adverse effects. Future developments include combined and more complex interactions of tDCS with pharmacological or psychotherapeutic interventions. In particular, using computational models to individualize stimulation protocols, considering state dependency and applying closed-loop technologies will pave the way for tDCS-based personalized interventions as well as the development of home treatment settings promoting the role of tDCS as an effective treatment option for patients with mental health problems.

Using population crossover trials to improve the decision process regarding treatment individualization in N-of-1 trials

Healthcare researchers are showing renewed interest in the utilization of N-of-1 clinical trials for the individualization of pharmacological treatments. Here, we propose a frequentist approach to conducting treatment individualization in N-of-1 trials that we call "partial empirical Bayes." We infer the most beneficial treatment for the patient from combining the information provided by a previously conducted population crossover trial with individual patient data. We propose a method for estimating an optimal number of treatment cycles and investigate the statistical conditions under which N-of-1 trials are more beneficial than traditional clinical approaches. We represent the patient population with a random-coefficients linear model and calculate estimators of posttreatment individual disease severities. We show the estimators' consistency under the most common N-of-1 designs and examine their prediction errors and performance with small numbers of patient's responses. We demonstrate by simulating new patients that our approach is equivalent or superior to both the common clinical practice of recommending the on-average best treatment for all patients and the common individualization method that simply compares average responses to the tested treatments. We conclude that some situations exist in which individualization with N-of-1 trials is highly beneficial while other situations exist in which individualization may be unfruitful.

Lighter-Load Exercise Produces Greater Acute- and Prolonged-Fatigue in Exercised and Non-Exercised Limbs

Purpose: The present study compared the fatigue and perceptual responses to volume-load matched heavier- and lighter- load resistance exercise to momentary failure in both a local/exercised, and non-local/non-exercised limb. Methods: Eleven resistance-trained men undertook unilateral maximal voluntary contraction (MVC) testing for knee extension prior to and immediately, 24 hr- and 48 hr- post heavier (80% MVC) and lighter  (40% MVC) load dynamic unilateral knee extension exercise. Only the dominant leg of each participant was exercised to momentary failure using heavier and lighter loads, and perceptions of discomfort were measured immediately upon exercise cessation. Results: Point estimates and confidence intervals suggested that immediately post-exercise there was greater fatigue in both the exercised and non-exercised legs for the lighter- load condition. At 24 hr the exercised leg under the heavier-load condition had recovered to pre-exercise strength; however, the exercised leg under lighter- load condition had still not fully recovered by 48 hr. For the non-exercised leg, only the lighter-load condition induced fatigue; however, recovery had occurred by 48 hr. Median discomfort ratings were statistically significantly different (Z = -2.232, p = .026) between lighter and heavier loads (10 [IQR = 0] and 8 [IQR = 3], respectively). Conclusions: This study suggests that lighter-load resistance exercise induces greater fatigue in both the exercised- and non-exercised limbs, compared to heavier-load resistance exercise. These findings may have implications for exercise frequency as it may be possible to engage in heavier-load resistance exercise more frequently than a volume-load matched protocol using lighter loads.Abbreviations CI: Confidence intervals: ES: Effect size: MVC: Maximum voluntary contraction; Nm:Newton meters; RM: Repetition maximum; SD: Standard deviation; SI: Strength index.

Directions for Exercise Treatment Response Heterogeneity and Individual Response Research

Treatment response heterogeneity and individual responses following exercise training are topics of interest for personalized medicine. Proposed methods to determine the contribution of exercise to the magnitude of treatment response heterogeneity and categorizing participants have expanded and evolved. Setting clear research objectives and having a comprehensive understanding of the strengths and weaknesses of the available methods are vital to ensure the correct study design and analytical approach are used. Doing so will ensure contributions to the field are conducted as rigorously as possible. Nonetheless, concerns have emerged regarding the ability to truly isolate the impact of exercise training, and the nature of individual responses in relation to mean group changes. The purpose of this review is threefold. First, the strengths and limitations associated with current methods for quantifying the contribution of exercise to observed treatment response heterogeneity will be discussed. Second, current methods used to categorize participants based on their response to exercise will be outlined, as well as proposed mechanisms for factors that contribute to response variation. Finally, this review will provide an overview of some current issues at the forefront of individual response research.

Causes and Consequences of Interindividual Response Variability: A Call to Apply a More Rigorous Research Design in Acute Exercise-Cognition Studies

The different responses of humans to an apparently equivalent stimulus are called interindividual response variability. This phenomenon has gained more and more attention in research in recent years. The research field of exercise-cognition has also taken up this topic, as shown by a growing number of studies published in the past decade. In this perspective article, we aim to prompt the progress of this research field by (i) discussing the causes and consequences of interindividual variability, (ii) critically examining published studies that have investigated interindividual variability of neurocognitive outcome parameters in response to acute physical exercises, and (iii) providing recommendations for future studies, based on our critical examination. The provided recommendations, which advocate for a more rigorous study design, are intended to help researchers in the field to design studies allowing them to draw robust conclusions. This, in turn, is very likely to foster the development of this research field and the practical application of the findings.

Prioritisation and design of clinical trials

Clinical trials require participation of numerous patients, enormous research resources and substantial public funding. Time-consuming trials lead to delayed implementation of beneficial interventions and to reduced benefit to patients. This manuscript discusses two methods for the allocation of research resources and reviews a framework for prioritisation and design of clinical trials. The traditional error-driven approach of clinical trial design controls for type I and II errors. However, controlling for those statistical errors has limited relevance to policy makers. Therefore, this error-driven approach can be inefficient, waste research resources and lead to research with limited impact on daily practice. The novel value-driven approach assesses the currently available evidence and focuses on designing clinical trials that directly inform policy and treatment decisions. Estimating the net value of collecting further information, prior to undertaking a trial, informs a decision maker whether a clinical or health policy decision can be made with current information or if collection of extra evidence is justified. Additionally, estimating the net value of new information guides study design, data collection choices, and sample size estimation. The value-driven approach ensures the efficient use of research resources, reduces unnecessary burden to trial participants, and accelerates implementation of beneficial healthcare interventions.

Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes

We argue that well-informed patient-specific decision-making may be carried out as three consecutive tasks: (1) estimating key parameters of a statistical model, (2) using prognostic information to convert these parameters into clinically interpretable values, and (3) specifying joint utility functions to quantify risk-benefit trade-offs between clinical outcomes. Using the management of metastatic clear cell renal cell carcinoma as our motivating example, we explain the role of prognostic covariates that characterize between-patient heterogeneity in clinical outcomes. We show that explicitly specifying the joint utility of clinical outcomes provides a coherent basis for patient-specific decision-making.

The Role of Hepatic Venous Pressure Gradient in the Management of Cirrhosis

Quantifying the degree of portal hypertension provides useful information to estimate prognosis and to evaluate new therapies for portal hypertension. This quantification is done in clinical practice with the measurement of the hepatic venous pressure gradient. This article addresses the applications of measuring portal pressure in cirrhosis, including the differential diagnosis of portal hypertension; estimation of prognosis in cirrhosis, including preoperative evaluation before hepatic and extrahepatic surgery; assessment of the response to drug therapy (mainly in the context of drug development); and assessing the regression of portal hypertension syndrome.

Contributions of treatment centre and patient characteristics to patient-reported experience of haemodialysis: a national cross-sectional study

OBJECTIVES

To examine the relative importance of patient and centre level factors in determining self-reported experience of care in patients with advanced kidney disease treated by maintenance haemodialysis (HD).

DESIGN

Analysis of data from a cross sectional national survey; the UK Renal Registry (UKRR) national Kidney patient-reported experience measure (PREM) survey (2018). Centre-level data were obtained from the UKRR report (2018).

SETTING

National survey of patients with advanced kidney disease receiving treatment with maintenance HD in UK renal centres in 2018.

PARTICIPANTS

The Kidney PREM was distributed to all UK renal centres by the UKRR in May 2018. Each centre invited patients receiving outpatient treatment for kidney disease to complete the PREM. These included patients with chronic kidney disease, those receiving dialysis-both HD and peritoneal dialysis, and those with a functioning kidney transplant. There were no formal inclusion/exclusion criteria.

MAIN OUTCOME MEASURES

The Kidney PREM has 38 questions in 13 subscales. Responses were captured using a 7-point Likert scale (never 1, always 7). The primary outcome of interest was the mean PREM score calculated across all questions. Multilevel modelling was used to determine the proportion of variation of the mean PREM score across centres due to patient-related and centre-related factors.

RESULTS

There were records for 8253 HD patients (61% men, 77% white) from 69 renal centres (9-710 patients per centre). There was significant variation in mean PREM score across centres (5.35-6.53). In the multivariable analysis there was some variation in relation to both patient- and centre-level factors but these contributed little to explaining the overall variation. However, multilevel modelling showed that the overwhelming proportion of the explained variance (45%) was explained by variation between centres (40%), only a small proportion of which is identified by measured factors. Only 5% of the variation was related to patient-level factors.

CONCLUSIONS

Centre rather than patient characteristics determine the experience of care of patients receiving HD. Further work is required to define the characteristics of the treating centre which determine patient experience.

An efficient new assay for measuring zebrafish anxiety: Tall tanks that better characterize between-individual differences

BACKGROUND

Zebrafish (Danio rerio) are increasingly being used to model anxiety. A common behavioral assay employed for assessing anxiety-like behaviors in zebrafish is the "novel tank test". We hypothesized that using deeper tanks in this test would result in greater between-individual variation in behavioral responses and a more 'repeatable' assay.

NEW METHODS

After mapping the literature and identifying common behavioral parameters used in analysis, we performed novel tank anxiety tests in both custom-designed 'tall' tanks with increased depth and 'short' trapezoidal tanks. We compared the repeatability of the behavioral parameters between tall and short tanks and also investigated sex differences.

RESULTS

Overall, regardless of tank depth, almost all behavioral parameters associated with anxiety in zebrafish were significantly repeatable (R = 0.24 to 0.60). Importantly, our tall tanks better captured between-individual differences, resulting in higher repeatability estimates (average repeatability tall tanks: R = 0.46; average repeatability short tanks: R = 0.36) and clearer sex differences.

CONCLUSIONS

Our assay using tall tanks has advantages over tests based on short tanks which underestimate repeatability. We argue that use of deeper tanks will improve the reliability of behavioral data across studies using novel tank tests for zebrafish. Our results also call for increased attention in designing the most appropriate assay in biomedical and behavioral sciences as current methods may lack the sensitivity to detect subtle, yet important, information, such as between-individual variation, an important component in assessing the reliability of behavioral data.

Systematic Review of N-of-1 Studies in Rare Genetic Neurodevelopmental Disorders: The Power of 1

OBJECTIVE

To improve the use of N-of-1 studies in rare genetic neurodevelopmental disorders, we systematically reviewed the literature and formulated recommendations for future studies.

METHODS

The systematic review protocol was registered in the PROSPERO International Prospective Register of Systematic Reviews (CRD42020154720). EMBASE and MEDLINE were searched for relevant studies. Information was recorded on types of interventions, outcome measures, validity, strengths, and limitations using standard reporting guidelines and critical appraisal tools. Qualitative and descriptive analyses were performed.

RESULTS

Twelve studies met the N-of-1 inclusion criteria, including both single trials and series. Interventions were mainly directed to neuropsychiatric manifestations. Main strengths were the use of personalized and clinically relevant outcomes in most studies. Generalizability was compromised due to limited use of validated and generalizable outcome measures.

CONCLUSION

N-of-1 studies are sporadically reported in rare genetic neurodevelopmental disorders. Properly executed N-of-1 studies may provide a powerful alternative to larger randomized controlled trials in rare disorders and a much needed bridge between practice and science. We provide recommendations for future N-of-1 studies in rare genetic neurodevelopmental disorders, ultimately optimizing evidence-based and personalized care.

Performance Variability During Motor Learning of a New Balance Task in a Non-immersive Virtual Environment in Children With Hemiplegic Cerebral Palsy and Typically Developing Peers

Background: Motor impairments contribute to performance variability in children with cerebral palsy (CP) during motor skill learning. Non-immersive virtual environments (VEs) are popular interventions to promote motor learning in children with hemiplegic CP. Greater understanding of performance variability as compared to typically developing (TD) peers during motor learning in VEs may inform clinical decisions about practice dose and challenge progression. Purpose: (1) To quantify within-child (i.e., across different timepoints) and between-child (i.e., between children at the same timepoint) variability in motor skill acquisition, retention and transfer in a non-immersive VE in children with CP as compared to TD children; and (2) To explore the relationship between the amount of within-child variability during skill acquisition and learning outcomes. Methods: Secondary data analysis of 2 studies in which 13 children with hemiplegic CP and 67 TD children aged 7-14 years undertook repeated trials of a novel standing postural control task in acquisition, retention and transfer sessions. Changes in performance across trials and sessions in children with CP as compared to TD children and between younger (7-10 years) and older (11-14 years) children were assessed using mixed effects models. Raw scores were converted to z-scores to meet model distributional assumptions. Performance variability was quantified as the standard deviation of z-scores. Results: TD children outperformed children with CP and older children outperformed younger children at each session. Older children with CP had the least between-child variability in acquisition and the most in retention, while older TD children demonstrated the opposite pattern. Younger children with CP had consistently high between-child variability, with no difference between sessions. Within-child variability was highest in younger children, regardless of group. Within-child variability was more pronounced in TD children as compared to children with CP. The relationship between the amount of within-child variability in performance and performance outcome at acquisition, retention and transfer sessions was task-specific, with a positive correlation for 1 study and a negative correlation in the other. Conclusions: Findings, though preliminary and limited by small sample size, can inform subsequent research to explore VE-specific causes of performance variability, including differing movement execution requirements and individual characteristics such as motivation, attention and visuospatial abilities.

Advancing clinical response characterization to frontotemporal transcranial direct current stimulation with electric field distribution in patients with schizophrenia and auditory hallucinations: a pilot study

Transcranial direct current stimulation (tDCS) has been proposed as a therapeutic option for treatment-resistant auditory verbal hallucinations (AVH) in schizophrenia. In such cases, repeated sessions of tDCS are delivered with the anode over the left prefrontal cortex and the cathode over the left temporoparietal junction. Despite promising findings, the clinical response to tDCS is highly heterogeneous among patients. Here, we explored baseline differences between responders and nonresponders to frontotemporal tDCS using electric field modeling. We hypothesized that responders would display different tDCS-induced electric field strength in the brain areas involved in AVH compared to nonresponders.Using baseline structural MRI scans of 17 patients with schizophrenia and daily AVH who received 10 sessions of active frontotemporal tDCS, we constructed individual realistic whole brain models estimating electric field strength. Electric field maps were compared between responders (n = 6) and nonresponders to tDCS (n = 11) using an independent two-sample t test. Clinical response was defined as at least a 50% decrease of AVH 1 month after the last tDCS session.Results from the electric field map comparison showed that responders to tDCS displayed higher electric field strength in the left transverse temporal gyrus at baseline compared to nonresponders (T = 2.37; p = 0.016; 32 voxels).These preliminary findings suggested that the strength of the tDCS-induced electric field reaching the left transverse temporal gyrus could play an important role in the response to frontotemporal tDCS. In addition, this work suggests the interest of using electric field modeling to individualize tDCS and increase response rate.

Treatment effect variability in brain stimulation across psychiatric disorders: A meta-analysis of variance

Noninvasive brain stimulation methods such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are promising add-on treatments for a number of psychiatric conditions. Yet, some of the initial excitement is wearing off. Randomized controlled trials (RCT) have found inconsistent results. This inconsistency is suspected to be the consequence of variation in treatment effects and solvable by identifying responders in RCTs and individualizing treatment. However, is there enough evidence from RCTs that patients respond differently to treatment? This question can be addressed by comparing the variability in the active stimulation group with the variability in the sham group. We searched MEDLINE/PubMed and included all double-blinded, sham-controlled RCTs and crossover trials that used TMS or tDCS in adults with a unipolar or bipolar depression, bipolar disorder, schizophrenia spectrum disorder, or obsessive compulsive disorder. In accordance with the PRISMA guidelines to ensure data quality and validity, we extracted a measure of variability of the primary outcome. A total of 130 studies with 5748 patients were considered in the analysis. We calculated variance-weighted variability ratios for each comparison of active stimulation vs sham and entered them into a random-effects model. We hypothesized that treatment effect variability in TMS or tDCS would be reflected by increased variability after active compared with sham stimulation, or in other words, a variability ratio greater than one. Across diagnoses, we found only a minimal increase in variability after active stimulation compared with sham that did not reach statistical significance (variability ratio = 1.03; 95% CI, 0.97, 1.08, P = 0.358). In conclusion, this study found little evidence for treatment effect variability in brain stimulation, suggesting that the need for personalized or stratified medicine is still an open question.

The ACTTION Guide to Clinical Trials of Pain Treatments, part II: mitigating bias, maximizing value

Summaries of the articles included in part II of the ACTTION Guide to Clinical Trials of Pain Treatments are followed by brief overviews of methodologic considerations involving precision pain medicine, pragmatic clinical trials, real world evidence, and patient engagement in clinical trials.