Statistical Power Of Clinical Trials Research Articles

Incorporating Data from Multiple Endpoints in the Analysis of Clinical Trials: Example from RSV Vaccines.

To meet regulatory approval, interventions must demonstrate efficacy against a primary outcome in randomized clinical trials. However, when there are multiple clinically relevant outcomes, selecting a single primary outcome is challenging. Incorporating data from multiple outcomes may increase statistical power in clinical trials. We examined methods for analyzing data on multiple endpoints, inspired by real-world trials of interventions against respiratory syncytial virus (RSV). We developed a novel permutation test representing a weighted average of individual outcome test statistics ( wavP ) to evaluate intervention efficacy in a multiple endpoint analysis. We compared the power and type I error rate of this approach to the Bonferroni correction ( bonfT ) and the minP permutation test. We evaluated the different approaches using simulated data from three hypothetical trials varying the intervention efficacy, correlation, and incidence of the outcomes, and data from a real-world RSV clinical trial. When the vaccine efficacy against different outcomes was similar, wavP yielded higher power than bonfT and minP ; in some scenarios the improvement in power was substantial. In settings where vaccine efficacy was notably larger against one endpoint compared with the others, all three methods had similar power. We developed an R package, PERmutation basEd ANalysis of mulTiple Endpoints (PERMEATE), to guide the selection of the most appropriate method for analyzing multiple endpoints in clinical trials. Analyzing multiple endpoints using a weighted permutation method can increase power, whereas controlling the type I error rate compared with established methods under conditions mirroring real-world RSV clinical trials.

Standards of Fluid Biomarker Collection and Pre-analytical Processes in Humans and Mice: Recommendations by the Ataxia Global Initiative Working Group on Biomarkers.

The Ataxia Global Initiative (AGI) aims to serve as a platform to facilitate clinical trial readiness for the hereditary ataxias. Clinical trials for these diseases have been hampered by the lack of objective measures to study disease onset, progression, and treatment efficacy. While these issues are not unique to the genetic ataxias, the relative rarity of these diseases makes the need for such measures even more pressing to achieve statistical power in clinical trials. In this report, we have described the efforts of the AGI fluid biomarker working group (WG) in developing uniform protocols for biomarker sampling and storage, both for human and preclinical studies in mice. By reducing collection variability, we anticipate reduced noise in downstream biomarker analysis that will improve statistical power and minimize the necessary sample size. The emphasis has been on defining and standardizing the sampling and pre-analytical work-up of minimal set of biological samples, specifically blood plasma and serum, keeping in mind the need for harmonization of collection and storage that can be achieved with relatively limited cost and resources. An optional package is detailed for those centers that have the resources and commitment for additional biofluids/sample processing and storage. Finally, we have delineated similar standardized protocols for mice that will be important for preclinical studies in the field.

Polygenic Effect on Tau Pathology Progression in Alzheimer's Disease.

Polygenic variation accounts for a substantial portion of the risk of Alzheimer's disease (AD), but its effect on the rate of fibrillar-tau accumulation as a key driver of dementia symptoms is unclear. We combined the to-date largest number of genetic risk variants of AD (n=85 lead single-nucleotide polymorphisms [SNPs]) from recent genome-wide association studies (GWAS) to generate a polygenic score (PGS). We assessed longitudinal tau-positron emission tomography (PET), amyloid-PET, and cognition in 231 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using the PGS, together with global amyloid-PET, we predicted the rate of tau-PET increases in Braak-stage regions-of-interest and cognitive decline. We also assessed PGS-risk enrichment effects on the required sample size in clinical trials targeting tau pathology. We found that a higher PGS was associated with higher rates of tau-PET accumulation, in particular at elevated amyloid-PET levels. The tau-PET increases mediated the association between PGS and faster cognitive decline. Risk enrichment through high PGS afforded sample size savings by 34%. Our results demonstrate that the PGS predicts faster tau progression and thus cognitive decline, showing utility to enhance statistical power in clinical trials. ANN NEUROL 2023;93:819-829.

Statistical power in clinical trials of interventions for mood, anxiety, and psychotic disorders.

Previous research has suggested that statistical power is suboptimal in many biomedical disciplines, but it is unclear whether power is better in trials for particular interventions, disorders, or outcome types. We therefore performed a detailed examination of power in trials of psychotherapy, pharmacotherapy, and complementary and alternative medicine (CAM) for mood, anxiety, and psychotic disorders. We extracted data from the Cochrane Database of Systematic Reviews (Mental Health). We focused on continuous efficacy outcomes and estimated power to detect predetermined effect sizes (standardized mean difference [SMD] = 0.20-0.80, primary SMD = 0.40) and meta-analytic effect sizes (ESMA). We performed meta-regression to estimate the influence of including underpowered studies in meta-analyses. We included 256 reviews with 10 686 meta-analyses and 47 384 studies. Statistical power for continuous efficacy outcomes was very low across intervention and disorder types (overall median [IQR] power for SMD = 0.40: 0.32 [0.19-0.54]; for ESMA: 0.23 [0.09-0.58]), only reaching conventionally acceptable levels (80%) for SMD = 0.80. Median power to detect the ESMA was higher in treatment-as-usual (TAU)/waitlist-controlled (0.49-0.63) or placebo-controlled (0.12-0.38) trials than in trials comparing active treatments (0.07-0.13). Adequately-powered studies produced smaller effect sizes than underpowered studies (B = -0.06, p ⩽ 0.001). Power to detect both predetermined and meta-analytic effect sizes in psychiatric trials was low across all interventions and disorders examined. Consistent with the presence of reporting bias, underpowered studies produced larger effect sizes than adequately-powered studies. These results emphasize the need to increase sample sizes and to reduce reporting bias against studies reporting null results to improve the reliability of the published literature.

Predictive model incorporating polygenic risk score for Alzheimer's disease predicts MMSE decline in APOE4 carriers and noncarriers.

Heterogeneity in the progression of cognitive impairment in later life is especially challenging to predict in pre-symptomatic and prodromal AD populations and can negatively impact the efficiency and statistical power of clinical trials. Genetic risk estimation as the earliest measurable contributor to AD is one of the most promising solutions. Polygenic risk scores (PRS) calculated based on multiple common genetic variants identified in large genome-wide association studies (GWAS) take the comprehensive genetic effect into consideration beyond APOE genotype. Here we present the PRS model results comparing prediction for decline among APOE4 carriers and non-carriers among pre-symptomatic individuals. A PRS was calculated using GWAS summary statistics for clinical AD diagnosis. The samples used in modeling were amyloid-PET positive confirmed individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Logistic regression models assessed the association between PRS and MMSE decline covarying for age, sex, education, APOE-ε4, and baseline MMSE score. Feature importance rank was estimated by the absolute value of the corresponding coefficients of all predictors in the model. The PRS model outcompeted the base model for MMSE decline, which included age, sex, and education as predictors, and exhibited the best performance (AUC = 0.82). Additionally, the PRS model revealed the highest classification performance in both APOE4 carriers (AUC = 0.81) and noncarriers (AUC=0.82) of the pre-symptomatic individuals. Meanwhile, the feature importance analysis showed that PRS is ranked as the second most important feature in both subgroups. The proposed model including PRS explains the heterogeneity of cognitive decline above and beyond the APOE4 allele and increases the model performance over traditional methods to predict cognitive decline in individuals at risk for AD. Utilization of additional genomic factors beyond APOE in PRS models could enhance clinical trial recruitment and stratification strategies for trial analyses, such that high polygenic risk APOE3 carriers are selected for probable cognitive decline, in addition to APOE4 carriers.

Leveraging machine learning predictive biomarkers to augment the statistical power of clinical trials with baseline magnetic resonance imaging.

A key factor in designing randomized clinical trials is the sample size required to achieve a particular level of power to detect the benefit of a treatment. Sample size calculations depend upon the expected benefits of a treatment (effect size), the accuracy of measurement of the primary outcome, and the level of power specified by the investigators. In this study, we show that radiomic models, which leverage complex brain MRI patterns and machine learning, can be utilized in clinical trials with protocols that incorporate baseline MR imaging to significantly increase statistical power to detect treatment effects. Akin to the historical control paradigm, we propose to utilize a radiomic prediction model to generate a pseudo-control sample for each individual in the trial of interest. Because the variability of expected outcome across patients can mask our ability to detect treatment effects, we can increase the power to detect a treatment effect in a clinical trial by reducing that variability through using radiomic predictors as surrogates. We illustrate this method with simulations based on data from two cohorts in different neurologic diseases, Alzheimer’s disease and glioblastoma multiforme. We present sample size requirements across a range of effect sizes using conventional analysis and models that include a radiomic predictor. For our Alzheimer’s disease cohort, at an effect size of 0.35, total sample size requirements for 80% power declined from 246 to 212 for the endpoint cognitive decline. For our glioblastoma multiforme cohort, at an effect size of 1.65 with the endpoint survival time, total sample size requirements declined from 128 to 74. This methodology can decrease the required sample sizes by as much as 50%, depending on the strength of the radiomic predictor. The power of this method grows with increased accuracy of radiomic prediction, and furthermore, this method is most helpful when treatment effect sizes are small. Neuroimaging biomarkers are a powerful and increasingly common suite of tools that are, in many cases, highly predictive of disease outcomes. Here, we explore the possibility of using MRI-based radiomic biomarkers for the purpose of improving statistical power in clinical trials in the contexts of brain cancer and prodromal Alzheimer’s disease. These methods can be applied to a broad range of neurologic diseases using a broad range of predictors of outcome to make clinical trials more efficient.

Statistical efficiency of patient data in randomized clinical trials of epilepsy treatments.

Previous research suggests that natural fluctuations in seizure rates within individuals have a quantifiable impact on therapeutic clinical trial outcomes. A trial simulator estimated the statistical power of clinical trials with a typical trial design with and without patients included who exhibited a range of means (1-15 seizures/mo) and standard deviations (1-15 seizures/mo) in their baseline seizure rates. Trial outcomes were evaluated using 50% responder rates, median percentage change, and time to prerandomization. Patients with higher seizure frequencies and lower standard deviations during their baseline contribute more to the statistical power regardless of the method used to evaluate the trial. Power varied from -20% to 30% depending on baseline seizure characteristics. Patient-specific characteristics can predict the contributions to the statistical power of clinical trials for epilepsy treatments. It may be possible to characterize this contribution with baseline data, leading to more efficient clinical trials.

Recruitment and retention in clinical trials in chronic kidney disease: report from national workshops with patients, caregivers and health professionals.

Slow recruitment and poor retention jeopardize the reliability and statistical power of clinical trials, delaying access to effective interventions and increasing costs, as commonly observed in nephrology trials. Involving patients in trial design, recruitment and retention is infrequent but potentially transformational. We conducted three workshops involving 105 patients/caregivers and 43 health professionals discussing patient recruitment and retention in clinical trials in chronic kidney disease. We identified four themes. 'Navigating the unknown'-patients described being unaware of the research question, confused by technical terms, sceptical about findings and feared the risk of harm. 'Wary of added burden'-patients voiced reluctance to attend additional appointments, were unsure of the commitment required or at times felt too unwell and without capacity to participate. 'Disillusioned and disconnected'-some patients felt they were taken for granted, particularly if they did not receive trial results. Participants believed there was no culture of trial participation in kidney disease and an overall lack of awareness about opportunities to participate. To improve recruitment and retention, participants addressed 'Building motivation and interest'. Investigators should establish research consciousness from the time of diagnosis, consider optimal timing for approaching patients, provide comprehensive information in an accessible manner, emphasize current and future relevance to them and their illness, involve trusted clinicians in recruitment and minimize the burden of trial participation. Participation in clinical trials was seen as an opportunity for people to give back to the health system and for future people in their predicament.

Statistical power of clinical trials increased while effect size remained stable: an empirical analysis of 136,212 clinical trials between 1975 and 2014

ObjectivesTo study the statistical power of randomized clinical trials and examine developments over time. Study Design and SettingWe analyzed the statistical power in 136,212 clinical trials between 1975 and 2014 extracted from meta-analyses from the Cochrane database of systematic reviews. We determined study power to detect standardized effect sizes, where power was based on the meta-analyzed effect size. Average power, effect size, and temporal patterns were examined for all meta-analyses and a subset of significant meta-analyses. ResultsThe number of trials with power ≥80% was low (7%) but increased over time: from 5% in 1975–1979 to 9% in 2010–2014. In significant meta-analyses, the proportion of trials with sufficient power increased from 9% to 15% in these years (median power increased from 16% to 23%). This increase was mainly due to increasing sample sizes, while effect sizes remained stable with a median Cohen's h of 0.09 (interquartile range 0.04–0.22) and a median Cohen's d of 0.20 (0.11–0.40). ConclusionThis study demonstrates that sufficient power in clinical trials is still problematic, although the situation is slowly improving. Our data encourage further efforts to increase statistical power in clinical trials to guarantee rigorous and reproducible evidence-based medicine.

Adequate statistical power in clinical trials is associated with the combination of a male first author and a female last author.

Clinical trials have a vital role in ensuring the safety and efficacy of new treatments and interventions in medicine. A key characteristic of a clinical trial is its statistical power. Here we investigate whether the statistical power of a trial is related to the gender of first and last authors on the paper reporting the results of the trial. Based on an analysis of 31,873 clinical trials published between 1974 and 2017, we find that adequate statistical power was most often present in clinical trials with a male first author and a female last author (20.6%, 95% confidence interval 19.4-21.8%), and that this figure was significantly higher than the percentage for other gender combinations (12.5-13.5%; P<0.0001). The absolute number of female authors in clinical trials gradually increased over time, with the percentage of female last authors rising from 20.7% (1975-85) to 28.5% (after 2005). Our results demonstrate the importance of gender diversity in research collaborations and emphasize the need to increase the number of women in senior positions in medicine.

Neuroimaging Identifies Patients Most Likely to Respond to a Restorative Stroke Therapy.

Patient heterogeneity reduces statistical power in clinical trials of restorative therapies. Valid predictors of treatment responsiveness are needed, and several have been studied with a focus on corticospinal tract (CST) injury. We studied performance of 4 such measures for predicting behavioral gains in response to motor training therapy. Patients with subacute-chronic hemiparetic stroke (n=47) received standardized arm motor therapy, and change in arm Fugl-Meyer score was calculated from baseline to 1 month post-therapy. Injury measures calculated from baseline magnetic resonance imaging included (1) percent CST overlap with stroke, (2) CST-related atrophy (cerebral peduncle area), (3) CST integrity (fractional anisotropy) in the cerebral peduncle, and (4) CST integrity in the posterior limb of internal capsule. Percent CST overlap with stroke, CST-related atrophy, and CST integrity did not correlate with one another, indicating that these 3 measures captured independent features of CST injury. Percent injury to CST significantly predicted treatment-related behavioral gains (r=-0.41; P=0.004). The other CST injury measures did not, neither did total infarct volume nor baseline behavioral deficits. When directly comparing patients with mild versus severe injury using the percent CST injury measure, the odds ratio was 15.0 (95% confidence interval, 1.54-147; P<0.005) for deriving clinically important treatment-related gains. Percent CST injury is useful for predicting motor gains in response to therapy in the setting of subacute-chronic stroke. This measure can be used as an entry criterion or a stratifying variable in restorative stroke trials to increase statistical power, reduce sample size, and reduce the cost of such trials.

Simulating Clinical Trials With and Without Intracranial EEG Data.

SummaryObjectiveIt is currently unknown whether knowledge of clinically silent (electrographic) seizures improves the statistical efficiency of clinical trials.MethodsUsing data obtained from 10 patients with chronically implanted subdural electrodes over an average of 1 year, a Monte Carlo bootstrapping simulation study was performed to estimate the statistical power of running a clinical trial based on (1) patient‐reported seizures with intracranial electroencephalogram (icEEG) confirmation, (2) all patient‐reported events, or (3) all icEEG‐confirmed seizures. A “drug” was modeled as having 10%, 20%, 30%, 40%, and 50% efficacy in 1,000 simulated trials each. Outcomes were represented as percentage of trials that achieved p < 0.05 using Fisher's exact test for 50% responder rates (RR50) and the Wilcoxon rank‐sum test for median percentage change (MPC).ResultsAt each simulated drug strength, the MPC method showed higher power than RR50. As drug strength increased, statistical power increased. For all cases except RR50 with drug of 10% efficacy, using patient‐reported events (with or without icEEG confirmation) was not as statistically powerful as using all available intracranially confirmed seizures (p < 0.001).SignificanceWith simulation, this study demonstrates that additional accuracy in seizure detection using chronically implanted icEEG improves statistical power of clinical trials. Newer invasive and noninvasive seizure detection devices may have the potential to provide greater statistical efficiency, accelerate drug discovery, and lower trial costs.

Power and Sample Size Calculations in Clinical Trials with Patient-Reported Outcomes under Equal and Unequal Group Sizes Based on Graded Response Model: A Simulation Study

ObjectivesTo provide a valid sample size strategy based on simulation and to evaluate the statistical power in clinical trials with patient-reported outcomes (PROs) based on a polytomous item response theory model—the graded response model (GRM)—and to compare this framework with the classical test theory (CTT) approach. MethodsOne thousand randomized clinical trials were simulated using PRO based on the GRM and under various combinations of the number of patients in each arm, the group allocation ratio, the number of items and categories, and group effects. The power and sample size estimated in the simulations were then compared with those computed using the CTT framework. ResultsThe results indicated that the impact of the most influential factors, including the number of patients, group allocation ratio, group effects, and the number of categories, on the power and sample size of the GRM-based and CTT-based approaches was similar. Nevertheless, the strong impact of the number of items on these issues distinguished the two approaches. ConclusionsIt is crucial to use an adapted sample size formula in a GRM-based analysis because the classical formula designed for the CTT-based approach does not consider the impact of the number of items, which could result in an inadequately sized study and a decrease in power. Thus, when clinicians design a randomized clinical trial with polytomous PRO endpoints using classical sample size formula as the base, they should be aware of the possibility of making an incorrect clinical decision.

Using re-randomisation to improve patient recruitment and increase statistical power in clinical trials

Patient recruitment is a major challenge for randomised trials. Reviews of publicly funded UK trials have found that 45 to 69% fail to recruit to target. This increases costs, delays results, and adversely impacts on the feasibility of conducting trials for conditions where there is a limited patient pool. For some conditions a patient requires treatment on multiple occasions. For example, patients with sickle cell disease require pain relief for each pain crisis, or women having fertility treatment undergo multiple treatment cycles until becoming pregnant. The current norm is for patients to be included for only one treatment episode. We propose a re-randomisation design, allowing patients to be randomised on multiple occasions, which could substantially increase the recruitment rate. We describe some properties of the re-randomisation design, such as the conditions required to obtain unbiased estimates of the treatment effect and control type I error rates, and offer advice on practical design and analysis issues. We show that this design can reduce the required number of patients compared with a parallel group design. It can be used in a wider variety of settings than a crossover design and may be more patient-centred as the number of treatment periods is determined by participants rather than the trial design. We also highlight situations where this design would be inappropriate.

Diagnosis and treatment of early renal disease in patients with type 2 diabetes mellitus: what are the clinical needs?

Renal disease is prevalent in patients with diabetes mellitus type 2. Aggressive metabolic control and lowering of systemic and/or intraglomerular blood pressure are effective interventions but not without side effects. Thus a better, early identification of patients at risk for incidence or progression to end-stage renal failure by the use of new, validated biomarkers is highly desirable. In the majority of patients, hypertension and hyperglycaemia are pathogenetically important pathways for the progression of renal disease. Nonetheless even aggressive therapy targeting these factors does not eliminate the risk of end-stage renal failure and experimental evidence suggests that many other pathways (e.g. tubulointerstitial hypoxia or inflammation etc.) also contribute. As their individual importance might vary from patient to patient, interventions which interfere are likely not to be therapeutically effective in all subjects. In this situation, an option to preserve the statistical power of clinical trials is to rely on biomarkers that reflect individual pathophysiology. In current clinical practice, albuminuria is the biomarker that has been best evaluated to guide stratified/personalized therapy but there is a clear need to expand our diagnostic abilities.

Systematic review of multivariable prognostic models for mild traumatic brain injury.

Prognostic models can guide clinical management and increase statistical power in clinical trials. The availability and adequacy of prognostic models for mild traumatic brain injury (MTBI) is uncertain. The present study aimed to (1) identify and evaluate multivariable prognostic models for MTBI, and (2) determine which pre-, peri-, and early post-injury variables have independent prognostic value in the context of multivariable models. An electronic search of MEDLINE, PsycINFO, PubMed, EMBASE, and CINAHL databases for English-language MTBI cohort studies from 1970-2013 was supplemented by Web of Science citation and hand searching. This search strategy identified 7789 articles after removing duplicates. Of 182 full-text articles reviewed, 26 met eligibility criteria including (1) prospective inception cohort design, (2) prognostic information collected within 1 month post-injury, and (3) 2+variables combined to predict clinical outcome (e.g., post-concussion syndrome) at least 1 month later. Independent reviewers extracted sample characteristics, study design features, clinical outcome variables, predictor selection methods, and prognostic model discrimination, calibration, and cross-validation. These data elements were synthesized qualitatively. The present review found no multivariable prognostic model that adequately predicts individual patient outcomes from MTBI. Suboptimal methodology limits their reproducibility and clinical usefulness. The most robust prognostic factors in the context of multivariable models were pre-injury mental health and early post-injury neuropsychological functioning. Women and adults with early post-injury anxiety also have worse prognoses. Relative to these factors, the severity of MTBI had little long-term prognostic value. Future prognostic studies should consider a broad range of biopsychosocial predictors in large inception cohorts.

SP0067 How to Develop Meaningful Indices from Scratch (E.G. ASDAS)

Measuring and monitoring health outcomes is of utmost importance in rheumatology and in medicine in general. Yet quantifying health outcomes, such as disease activity, physical function and health related quality...

Abstract T P286: Comparison of Neuro-QOL Physical Function and modified Rankin Scale in Stroke

Introduction: The modified Rankin scale (mRS) is a reliable objective measure of disability and is widely applied in clinical trials. Health-related quality of life (QOL) measurements using Neuro-QOL provide validated measures of patient-reported outcomes. We compared changes in physical function QOL scores with transitions in mRS scores after ischemic stroke or transient ischemic attack (TIA). Methods: From a single-center prospective cohort study, we identified ischemic stroke and TIA patients admitted between August 2012 and April 2013 who survived to 3-month follow-up and underwent mRS and Neuro-QOL outcomes assessments at 1 and 3 months. The mRS was assessed by structured telephone interviews and Neuro-QOL scores using short forms of upper extremity (UE) and lower extremity (LE) function. We used Pearson's coefficient to compare mRS and Neuro-QOL scores and assessed mean changes in Neuro-QOL T-scores across mRS strata and between 1 and 3 months. Results: Three-hundred twelve patients met study criteria (mean age 65.2 years; initial NIHSS score 4; 77.2% ischemic stroke). Strong correlations were noted between mRS and physical function QOL scores at 3-months: mRS-LE: r = -0.74; mRS-UE: r = -0.74. UE and LE QOL scores were 6.1 and 7.1 points lower, respectively, in those with mRS of 1 compared with 0. Within individual patients, a 1 point decrease (improvement) in mRS was associated with increase in UE and LE QOL by 5.6 and 3.3 points while a 1 point increase (worsening) in mRS was associated with a decrease in UE and LE QOL by 4.0 and 7.5 points, respectively. By mRS, 7.5% of patients had worsening by at least 1 point between 1 and 3 months while the proportion with at least 1 standard deviation (10 points) decrease in UE or LE QOL scores was 12.1% and 14.4%, respectively. Conclusions: The mRS correlates well with patient-reported scores of physical function using Neuro-QOL. However, Neuro-QOL offers a complementary tool for assessment of neurological function and may help identify finer grades of functional change over time. Using a continuous measurement of function from the patient perspective may also be more valuable to patients and improve statistical power in clinical trials.

Pharmacogenomic Implications of Population Admixture: Brazil as a Model Case

The heterogeneous Brazilian population, with European, African and Amerindian ancestral roots is a model case for exploring the impact of population admixture on the frequency distribution of polymorphisms in pharmacogenes, and the design and interpretation of pharmacogenomics trials. Examples drawn from studies carried out by researchers of the Brazilian pharmacogenomics network, support the following conclusions: the distribution of polymorphisms varies across geographical regions and self-reported 'race/color' categories, and is best modeled as continuous functions of individual proportions of European and African ancestry; the differential frequency of polymorphisms impacts the calculations of sample sizes required for adequate statistical power in clinical trials performed in different segments of the Brazilian population; and extrapolation of pharmacogenomics data from well-defined ethnic groups to Brazilians is plagued with uncertainty. Data for warfarin and tacrolimus are reviewed to highlight the advantages and challenges of performing pharmacogenomic trials in Brazilians.

Advancing care for traumatic brain injury: findings from the IMPACT studies and perspectives on future research

Research in traumatic brain injury (TBI) is challenging for several reasons; in particular, the heterogeneity between patients regarding causes, pathophysiology, treatment, and outcome. Advances in basic science have failed to translate into successful clinical treatments, and the evidence underpinning guideline recommendations is weak. Because clinical research has been hampered by non-standardised data collection, restricted multidisciplinary collaboration, and the lack of sensitivity of classification and efficacy analyses, multidisciplinary collaborations are now being fostered. Approaches to deal with heterogeneity have been developed by the IMPACT study group. These approaches can increase statistical power in clinical trials by up to 50% and are also relevant to other heterogeneous neurological diseases, such as stroke and subarachnoid haemorrhage. Rather than trying to limit heterogeneity, we might also be able to exploit it by analysing differences in treatment and outcome between countries and centres in comparative effectiveness research. This approach has great potential to advance care in patients with TBI.