Testing for normality: a user's (cautionary) guide.

The normality assumption postulates that empirical data derives from a normal (Gaussian) population. It is a pillar of inferential statistics that enables the theorization of probability functions and the computation of p-values thereof. The breach of this assumption may not impose a formal mathematical constraint on the computation of inferential outputs (e.g., p-values) but may make them inoperable and possibly lead to unethical waste of laboratory animals. Various methods, including statistical tests and qualitative visual examination, can reveal incompatibility with normality and the choice of a procedure should not be trivialized. The following minireview will provide a brief overview of diagrammatical methods and statistical tests commonly employed to evaluate congruence with normality. Special attention will be given to the potential pitfalls associated with their application. Normality is an unachievable ideal that practically never accurately describes natural variables, and detrimental consequences of non-normality may be safeguarded by using large samples. Therefore, the very concept of preliminary normality testing is also, arguably provocatively, questioned.

Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random.

BACKGROUND: Epidemiological and clinical studies often have missing data, frequently analysed using multiple imputation (MI). In general, MI estimates will be biased if data are missing not at random (MNAR). Bias due to data MNAR can be reduced by including other variables ("auxiliary variables") in imputation models, in addition to those required for the substantive analysis. Common advice is to take an inclusive approach to auxiliary variable selection (i.e. include all variables thought to be predictive of missingness and/or the missing values). There are no clear guidelines about the impact of this strategy when data may be MNAR. METHODS: We explore the impact of including an auxiliary variable predictive of missingness but, in truth, unrelated to the partially observed variable, when data are MNAR. We quantify, algebraically and by simulation, the magnitude of the additional bias of the MI estimator for the exposure coefficient (fitting either a linear or logistic regression model), when the (continuous or binary) partially observed variable is either the analysis outcome or the exposure. Here, "additional bias" refers to the difference in magnitude of the MI estimator when the imputation model includes (i) the auxiliary variable and the other analysis model variables; (ii) just the other analysis model variables, noting that both will be biased due to data MNAR. We illustrate the extent of this additional bias by re-analysing data from a birth cohort study. RESULTS: The additional bias can be relatively large when the outcome is partially observed and missingness is caused by the outcome itself, and even larger if missingness is caused by both the outcome and the exposure (when either the outcome or exposure is partially observed). CONCLUSIONS: When using MI, the naïve and commonly used strategy of including all available auxiliary variables should be avoided. We recommend including the variables most predictive of the partially observed variable as auxiliary variables, where these can be identified through consideration of the plausible casual diagrams and missingness mechanisms, as well as data exploration (noting that associations with the partially observed variable in the complete records may be distorted due to selection bias).

On network deconvolution for undirected graphs.

Network deconvolution (ND) is a method to reconstruct a direct-effect network describing direct (or conditional) effects (or associations) between any two nodes from a given network depicting total (or marginal) effects (or associations). Its key idea is that, in a directed graph, a total effect can be decomposed into the sum of a direct and an indirect effects, with the latter further decomposed as the sum of various products of direct effects. This yields a simple closed-form solution for the direct-effect network, facilitating its important applications to distinguish direct and indirect effects. Despite its application to undirected graphs, it is not well known why the method works, leaving it with skepticism. We first clarify the implicit linear model assumption underlying ND, then derive a surprisingly simple result on the equivalence between ND and use of precision matrices, offering insightful justification and interpretation for the application of ND to undirected graphs. We also establish a formal result to characterize the effect of scaling a total-effect graph. Finally, leveraging large-scale genome-wide association study data, we show a novel application of ND to contrast marginal versus conditional genetic correlations between body height and risk of coronary artery disease; the results align with an inferred causal directed graph using ND. We conclude that ND is a promising approach with its easy and wide applicability to both directed and undirected graphs.

Group sequential testing of a treatment effect using a surrogate marker.

The identification of surrogate markers is motivated by their potential to make decisions sooner about a treatment effect. However, few methods have been developed to actually use a surrogate marker to test for a treatment effect in a future study. Most existing methods consider combining surrogate marker and primary outcome information to test for a treatment effect, rely on fully parametric methods where strict parametric assumptions are made about the relationship between the surrogate and the outcome, and/or assume the surrogate marker is measured at only a single time point. Recent work has proposed a nonparametric test for a treatment effect using only surrogate marker information measured at a single time point by borrowing information learned from a prior study where both the surrogate and primary outcome were measured. In this paper, we utilize this nonparametric test and propose group sequential procedures that allow for early stopping of treatment effect testing in a setting where the surrogate marker is measured repeatedly over time. We derive the properties of the correlated surrogate-based nonparametric test statistics at multiple time points and compute stopping boundaries that allow for early stopping for a significant treatment effect, or for futility. We examine the performance of our proposed test using a simulation study and illustrate the method using data from two distinct AIDS clinical trials.

Testing for Sufficient Follow-Up in Censored Survival Data by Using Extremes.

In survival analysis, it often happens that some individuals, referred to as cured individuals, never experience the event of interest. When analyzing time-to-event data with a cure fraction, it is crucial to check the assumption of "sufficient follow-up," which means that the right extreme of the censoring time distribution is larger than that of the survival time distribution for the noncured individuals. However, the available methods to test this assumption are limited in the literature. In this article, we study the problem of testing whether follow-up is sufficient for light-tailed distributions and develop a simple novel test. The proposed test statistic compares an estimator of the noncure proportion under sufficient follow-up to one without the assumption of sufficient follow-up. A bootstrap procedure is employed to approximate the critical values of the test. We also carry out extensive simulations to evaluate the finite sample performance of the test and illustrate the practical use with applications to leukemia and breast cancer data sets.

Core Concepts in Pharmacoepidemiology: Quantitative Bias Analysis.

Pharmacoepidemiological studies provide important information on the safety and effectiveness of medications, but the validity of study findings can be threatened by residual bias. Ideally, biases would be minimized through appropriate study design and statistical analysis methods. However, residual biases can remain, for example, due to unmeasured confounders, measurement error, or selection into the study. A group of sensitivity analysis methods, termed quantitative bias analyses, are available to assess, quantitatively and transparently, the robustness of study results to these residual biases. These approaches include methods to quantify how the estimated effect would be altered under specified assumptions about the potential bias, and methods to calculate bounds on effect estimates. This article introduces quantitative bias analyses for unmeasured confounding, misclassification, and selection bias, with a focus on their relevance and application to pharmacoepidemiological studies.

Improving analysis of cognitive outcomes in cardiovascular trials using different statistical approaches.

BACKGROUND: The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest. METHODS: Alternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit). RESULTS: The beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches. CONCLUSION: When analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure. TRIALS REGISTRATION: ORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014.

Meta-análisis: Intervalos de confianza e Intervalos de predicción / Meta-analysis: Confidence intervals and Prediction intervals

En los informes meta-analíticos se suelen reportar varios tipos de intervalos, hecho que ha generado cierta confusión a la hora de interpretarlos. Los intervalos de confianza reflejan la incertidumbre relacionada con un número, el tamaño del efecto medio paramétrico. Los intervalos de predicción reflejan el tamaño paramétrico probable en cualquier estudio de la misma clase que los incluidos en un meta-análisis. Su interpretación y aplicaciones son diferentes. En este artículo explicamos su diferente naturaleza y cómo se pueden utilizar para responder preguntas específicas. Se incluyen ejemplos numéricos, así como su cálculo con el paquete metafor en R.(AU)

Several types of intervals are usually employed in meta-analysis, a fact that has generated some confusion when interpreting them. Confidence intervals reflect the uncertainty related to a single number, the parametric mean effect size. Prediction intervals reflect the probable parametric effect size in any study of the same class as those included in a meta-analysis. Its interpretation and applications are different. In this article we explain in de-tail their different nature and how they can be used to answer specific ques-tions. Numerical examples are included, as well as their computation with the metafor Rpackage.(AU)

Discussion on "LEAP: the latent exchangeability prior for borrowing information from historical data" by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim.

This discussion provides commentary on the paper by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim entitled "LEAP: the latent exchangeability prior for borrowing information from historical data". The authors propose a novel method to bridge the incorporation of supplemental information into a study while also identifying potentially exchangeable subgroups to better facilitate information sharing. In this discussion, we highlight the potential relationship with other Bayesian model averaging approaches, such as multisource exchangeability modeling, and provide a brief numeric case study to illustrate how the concepts behind latent exchangeability prior may also improve the performance of existing methods. The results provided by Alt et al. are exciting, and we believe that the method represents a meaningful approach to more efficient information sharing.

Segurança e eficácia da ablação de altíssima potência e curta duração versus a ablação de alta potência e curta duração em pacientes com fibrilação atrial: uma revisão sistemática e meta-análise atualizada / Safety and efficacy of very high-power short-duration ablation versus high-power short-duration ablation in patients with atrial fibrillation: an updated systematic review and meta-analysis

INTRODUÇÃO: A ablação da fibrilação atrial (FA) é essencialmente baseada no isolamento das veias pulmonares (PVI), no entanto, a recorrência da FA devido à reconexão da VP é um desafio. O uso da técnica de alta potência de curta duração (HPSD; > 40 W) mostrou-se mais eficaz e seguro, proporcionando lesões uniformes. Recentemente, foi introduzida a técnica de muito alta potência de curta duração (vHPSD) (90 W, 4 s), no entanto, sua eficácia e segurança ainda são controversas quando comparadas à técnica de alta potência. OBJETIVO: Realizamos uma revisão sistemática e meta-análise para comparar a técnica de altíssima potência e curta duração (vHPSD; 70-90 W) e a técnica de alta potência e curta duração (HPSD; > 40 W) em pacientes com FA. MÉTODOS: Pesquisamos no PubMed, Embase e Cochrane Central. Os desfechos foram a recorrência da FA e as complicações. Um modelo de efeitos aleatórios foi usado para calcular as razões de chances (ORs) com intervalos de confiança (IC) de 95%. A análise estatística foi realizada utilizando o programa R (versão 4.3.2). A heterogeneidade foi avaliada com estatísticas I2. RESULTADOS: Incluímos pacientes de 7 estudos. Destes, 1 foi derivado de RCT e 6 foram estudos observacionais. Em comparação com a alta potência de curta duração, a técnica de altíssima potência e curta duração (vHPSD; 70-90 W) não demonstrou uma variação estatisticamente significativa nas taxas de recorrência da FA (OR 0,91; IC 95% 0,50 - 1,67; p=0,769; I2 = 48%) e complicações associadas (OR 1,04; IC 95% 0,38 - 2,86; p=0,933; I2 = 0%). CONCLUSÃO: Os dados desta metaanálise revelam que não houve diferença significativa na recorrência da FA e nas complicações entre a técnica de muito alta potência de curta duração e a técnica de alta potência de curta duração.

The decreased left ventricular ejection fraction in patients using trastuzumab deruxtecan for her2-positive breast cancer: a systematic review and meta-analysis

Breast Cancer (BC) is one of the most common cancers diagnosed in population femmale and it has several subtypes, one of them being theexpressing human epidermal growth factor receptor 2 positive (HER2 +), one of the treatments for HER2+ breast cancer consists of chemotherapy plus trastuzumab deruxtecan. Several clinical trials have shown the effectiveness and safety of trastuzumabe deruxtecano in cancer patients, however, several Adverse Events (AEs) have been described and the decrease in left ventricular ejection has been singled out for more prominent analysis. Objective: We conducted a systematic review and meta-analysis to investigate the cardiovascular effects of Trastuzumab Deruxtecano and whether it can influence the appearance of reduced left ventricular ejection fraction.. METHODS: We performed a systematic search in Embase, PubMed and Cochrane databases for randomized controlled trials (RCTs) showed a decrease in left ventricular ejection fraction in patients using trastuzumab deruxtecan against Her-2-positive breast cancer compared to patients to used another's treatments against this disease. Mean difference (MD) with 95% confidence intervals (CI) were calculated using a random effects model. The heterogeneity was examined in the I2 statistic. P-values > 0.05 were considered statistically significant. The statistical analysis was carried out using R software version 4.2.3. RESULTS: A total of 3 RCTs were included, with a total of 1656 patients evaluated, 928 patients randomized to the use of Trastuzumab Deruxtecan and 728 patients to the use of other treatments according to medical choice, follow-up ranged from 10 to 38 months. There was a visible in the decrease in left ventricular ejection fraction, with a higher incidence in the group that used trastuzumab compared to the placebo group (RR: 5.73%; 95% CI 1.51 - 21.78; I2 33% ; P= 0.010466). Another important point is the discontinuation of treatment due to grade 2 adverse events, classified as reduced LVEF, where a higher incidence is seen in the group that used Trastuzumab Deruxtecan compared to the placebo group (RR 2.11%; 95% CI 1.54 - 2.89; P = 0.000003),7. CONCLUSION: In this meta-analysis, Trastuzumab Deruxtecan showed a relationship with a decrease in left ventricular ejection fraction, displaying the need for more studies to evaluate the cardiotoxicity of trastuzumab and its effects as a whole on the cardiovascular system.

Cardio-protective effects of statins in patients with breast cancer undergoing anthracycline-based chemotherapy: a metaanalysis of randomized controlled trials

BACKGROUND: Contemporary understanding characterizes cardiotoxicity as a reduction in left ventricular ejection fraction (LVEF) by at least 10%, resulting in a final value below 53% in successive assessments. Nevertheless, breast cancer therapy can impact the cardiovascular system through various avenues. Cardiotoxicity is a known side effect of anthracycline chemotherapy, and the effectiveness of concomitant statin use in mitigating this risk is still unclear. PURPOSE: We aimed to evaluate the potential cardioprotective effects of statin exposure during anthracycline treatment. Our hypothesis posited that patients receiving statins during their treatment would experience a lesser decline in left ventricular ejection fraction (LVEF), lower levels of cholesterol and a reduced occurrence of cardiotoxicity compared to those not exposed to statins. METHODS: We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing statin versus placebo in patients undergoing anthracycline therapy. We searched PubMed, Embase and Cochrane for eligible trials. Mean differences (MDs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Heterogeneity was examined with I2 statistics. P values of < 0.05 were considered statistically significant. Statistical analysis were performed using R software version 4.2.3. RESULTS: A total of 4 RCTs comprising 580 patients were included, of whom 281 were randomized to statins and 299 to placebo. The follow up period ranged from 2.5 to 24 months, with participant ages varying between 36 to 68.9 in the intervention group and 37.9 to 72 in the control group. Compared with placebo, statins were significantly associated with a higher left ventricular ejection fraction (MD 2.57%; 95% CI 1.05-4.08; p<0.001; I2=0%), reduction in left ventricular systolic end-volume (MD -4.5 mL; 95% CI -7.57 to -1.44; p<0.004; I2=0%) and diastolic end-volume (MD -6.08 mL; 95% CI -11.27 to -0.9; p<0.021; I2=0%), with a low heterogeneity value. Statins also showed important reduction of total cholesterol (MD -46.28 mg/dL; 95% CI -71.3 to -21.25; p<0.001; I2=89%) and LDL-C (MD -39.45 mg/dL; 95% CI -52.27 to -26.64; p<0.001; I2=84%). CONCLUSIONS: In this metaanalysis of RCTs, the use of statins showed a correlation with improved cardiovascular parameters, indicating their effectiveness in minimizing cardiotoxicity in breast cancer patients undergoing anthracycline chemotherapy

Eficácia e segurança da nova geração de Mitraclip G4 versus gerações mais precoces para reparo transcateter borda a borda em pacientes com regurgitação mitral: uma revisão sistemática e metanálise / Efficacy and safety of new generation Mitraclip G4 versus earlier generations for transcatheter edge-to-edge repair in patients with mitral regurgitation: a systematic review and meta-analysis

INTRODUÇÃO: O reparo transcateter borda a borda utilizando o dispositivo MitraClip é uma ótima alternativa para tratar pacientes com insuficiência mitral e com alto risco cirúrgico. No entanto, a eficácia e segurança da nova geração de MitraClip versus as gerações anteriores não estão bem estabelecidas. OBJETIVO: Portanto, nosso objetivo foi realizar uma meta-análise explorando as gerações de MitraClip no reparo borda a borda da valva mitral por transcateter. MÉTODOS: Pesquisamos no PubMed, Embase e Cochrane Central estudos comparando dispositivos MitraClip de quarta geração com as gerações mais precoces em pacientes com regurgitação mitral (RM) tratados com reparo transcateter borda a borda. Os desfechos foram grau de RM, RM residual >2+, sucesso técnico, sucesso do dispositivo, número de clipes, NYHA III ou IV e mortalidade por todas as causas. A análise estatística foi realizada utilizando o programa R (versão 4.3.2). A heterogeneidade foi avaliada com estatística I2. RESULTADOS: Incluímos 2.123 pacientes de 6 estudos observacionais. Os resultados agrupados não revelaram diferença estatisticamente significativa no grau de RM ≤ 2+ após o procedimento (95,9% vs 95,7%; OR 1,05; IC 95% 0,68 - 1,62; p=0,839; I2 =0%), grau de RM ≤ 1+ após procedimento (OR 0,97; IC 95% 0,71 - 1,32; p=0,857; I2 =42%), sucesso do dispositivo (OR 1,20; IC 95% 0,87 - 1,68; p=0,275; I2 =0%), sucesso técnico (OR 0,98; IC 95% 0,66 - 1,46; p=0,919; I2 =0%) entre grupos de quarta geração e gerações iniciais. Entretanto, a opção pelo MitraClip de quarta geração em pacientes com insuficiência mitral foi associada a um maior número de pacientes que necessitaram de apenas 1 clipe durante o procedimento (OR 2,06; IC 95% 1,23 - 3,46; p = 0,007; I2 = 0%). CONCLUSÃO: Os dados desta meta-análise revelaram que não houve diferença significativa na redução da regurgitação mitral, na taxa de pacientes com NYHA III e IV e na mortalidade por todas as causas entre as gerações de MitraClip. Embora o uso da quarta geração estivesse associado a uma menor necessidade de clipes durante o procedimento.

Avaliação prognóstica de níveis glicêmicos em pacientes com choque cardiogênico: uma revisão sistemática e metanálise / Prognostic assessment of glycemic levels in patients with cardiogenic shock: a systematic review and meta-analysis

INTRODUÇÃO: Embora a hiperglicemia tenha valor prognóstico nas doenças cardiovasculares, há poucos dados disponíveis sobre seu valor prognóstico para pacientes em choque cardiogênico. OBJETIVO: Conduzir uma revisão sistemática e metanálise para avaliar os níveis glicêmicos no momento da admissão hospitalar em pacientes com choque cardiogênico decorrente de diversas causas etiológicas. MÉTODOS: Realizamos uma revisão sistemática nas bases de dados Elsevier, PubMed e Cochrane de estudos que avaliassem o valor prognóstico de índices glicêmicos na admissão de pacientes com choque cardiogênico, sendo o desfecho primário mortalidade geral. Incluímos estudos que definiram como hiperglicemia valores de corte de 180 a 220 mg/dL. Para a análise estatística foi utilizado o RevMan 5.1.7, e a heterogeneidade foi avaliada usando estatísticas I². RESULTADOS: Um total de 7 dos 5.214 estudos foram incluídos, resultando 3.504 pacientes, com média de idade variando entre 60 e 74 anos, sendo 2.242 (64%) homens. Desses pacientes, cerca de 926 (26%) tinham o diagnóstico prévio de Diabetes Mellitus. Estes pacientes foram divididos em subgrupos conforme seu índice glicêmico na admissão, Normoglicemia (<180mg/dL) e Hiperglicemia (180-220 mg/dL). O grupo hiperglicêmico foi associado a maior mortalidade (OR 2,14; IC 95% 1,53 - 3,00; p<0.00001; I²= 53%; Figura 1). CONCLUSÃO: Nesta revisão sistemática e metanálise, valores de glicemia entre 180 a 220 mg/dL foram associados a maior risco de mortalidade em pacientes em choque cardiogênico.

Summary statistics knockoffs inference with family-wise error rate control.

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.

Saving lives with statistics.

Healthcare is awash with numbers, and figuring out what knowledge these numbers might hold is worthwhile in order to improve patient care. Numbers allow for objective mathematical analysis of the information at hand, but while mathematics is objective by design, our choice of mathematical approach in a given situation is not. In prehospital and critical care, numbers stem from a wide range of different sources and situations, be it experimental setups, observational data or data registries, and what constitutes a "good" statistical analysis can be unclear. A well-crafted statistical analysis can help us see things our eyes cannot, and find patterns where our brains come short, ultimately contributing to changing clinical practice and improving patient outcome. With increasingly more advanced research questions and research designs, traditional statistical approaches are often inadequate, and being able to properly merge statistical competence with clinical knowhow is essential in order to arrive at not only correct, but also valuable and usable research results. By marrying clinical knowhow with rigorous statistical analysis we can accelerate the field of prehospital and critical care.

Covariate-constrained randomization in cluster randomized 2 × 2 factorial trials: application to a diabetes prevention study.

BACKGROUND: Cluster randomized trials (CRTs) are randomized trials where randomization takes place at an administrative level (e.g., hospitals, clinics, or schools) rather than at the individual level. When the number of available clusters is small, researchers may not be able to rely on simple randomization to achieve balance on cluster-level covariates across treatment conditions. If these cluster-level covariates are predictive of the outcome, covariate imbalance may distort treatment effects, threaten internal validity, lead to a loss of power, and increase the variability of treatment effects. Covariate-constrained randomization (CR) is a randomization strategy designed to reduce the risk of imbalance in cluster-level covariates when performing a CRT. Existing methods for CR have been developed and evaluated for two- and multi-arm CRTs but not for factorial CRTs. METHODS: Motivated by the BEGIN study-a CRT for weight loss among patients with pre-diabetes-we develop methods for performing CR in 2 × 2 factorial cluster randomized trials with a continuous outcome and continuous cluster-level covariates. We apply our methods to the BEGIN study and use simulation to assess the performance of CR versus simple randomization for estimating treatment effects by varying the number of clusters, the degree to which clusters are associated with the outcome, the distribution of cluster level covariates, the size of the constrained randomization space, and analysis strategies. RESULTS: Compared to simple randomization of clusters, CR in the factorial setting is effective at achieving balance across cluster-level covariates between treatment conditions and provides more precise inferences. When cluster-level covariates are included in the analyses model, CR also results in greater power to detect treatment effects, but power is low compared to unadjusted analyses when the number of clusters is small. CONCLUSIONS: CR should be used instead of simple randomization when performing factorial CRTs to avoid highly imbalanced designs and to obtain more precise inferences. Except when there are a small number of clusters, cluster-level covariates should be included in the analysis model to increase power and maintain coverage and type 1 error rates at their nominal levels.

Design considerations for Factorial Adaptive Multi-Arm Multi-Stage (FAST) clinical trials.

BACKGROUND: Multi-Arm, Multi-Stage (MAMS) clinical trial designs allow for multiple therapies to be compared across a spectrum of clinical trial phases. MAMS designs fall under several overarching design groups, including adaptive designs (AD) and multi-arm (MA) designs. Factorial clinical trials designs represent a combination of factorial and MAMS trial designs and can provide increased efficiency relative to fixed, traditional designs. We explore design choices associated with Factorial Adaptive Multi-Arm Multi-Stage (FAST) designs, which represent the combination of factorial and MAMS designs. METHODS: Simulation studies were conducted to assess the impact of the type of analyses, the timing of analyses, and the effect size observed across multiple outcomes on trial operating characteristics for a FAST design. Given multiple outcomes types assessed within the hypothetical trial, the primary analysis approach for each assessment varied depending on data type. RESULTS: The simulation studies demonstrate that the proposed class of FAST trial designs can offer a framework to potentially provide improvements relative to other trial designs, such as a MAMS or factorial trial. Further, we note that the design implementation decisions, such as the timing and type of analyses conducted throughout trial, can have a great impact on trial operating characteristics. CONCLUSIONS: Motivated by a trial currently under design, our work shows that the FAST category of trial can potentially offer benefits similar to both MAMS and factorial designs; however, the chosen design aspects which can be included in a FAST trial need to be thoroughly explored during the planning phase.

A Two-Step Framework for Validating Causal Effect Estimates.

BACKGROUND: Comparing causal effect estimates obtained using observational data to those obtained from the gold standard (i.e., randomized controlled trials [RCTs]) helps assess the validity of these estimates. However, comparisons are challenging due to differences between observational data and RCT generated data. The unknown treatment assignment mechanism in the observational data and varying sampling mechanisms between the RCT and the observational data can lead to confounding and sampling bias, respectively. AIMS: The objective of this study is to propose a two-step framework to validate causal effect estimates obtained from observational data by adjusting for both mechanisms. MATERIALS AND METHODS: An estimator of causal effects related to the two mechanisms is constructed. A two-step framework for comparing causal effect estimates is derived from the estimator. An R package RCTrep is developed to implement the framework in practice. RESULTS: A simulation study is conducted to show that using our framework observational data can produce causal effect estimates similar to those of an RCT. A real-world application of the framework to validate treatment effects of adjuvant chemotherapy obtained from registry data is demonstrated. CONCLUSION: This  study constructs a framework for comparing causal effect estimates between observational data and RCT data, facilitating the assessment of the validity of causal effect estimates obtained from observational data.