Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.

Estimating the natural indirect effect and the mediation proportion via the product method

Background The natural indirect effect (NIE) and mediation proportion (MP) are two measures of primary interest in mediation analysis. The standard approach for mediation analysis is through the product method, which involves a model for the outcome conditional on the mediator and exposure and another model describing the exposure–mediator relationship. The purpose of this article is to comprehensively develop and investigate the finite-sample performance of NIE and MP estimators via the product method. Methods With four common data types with a continuous/binary outcome and a continuous/binary mediator, we propose closed-form interval estimators for NIE and MP via the theory of multivariate delta method, and evaluate its empirical performance relative to the bootstrap approach. In addition, we have observed that the rare outcome assumption is frequently invoked to approximate the NIE and MP with a binary outcome, although this approximation may lead to non-negligible bias when the outcome is common. We therefore introduce the exact expressions for NIE and MP with a binary outcome without the rare outcome assumption and compare its performance with the approximate estimators. Results Simulation studies suggest that the proposed interval estimator provides satisfactory coverage when the sample size ≥500 for the scenarios with a continuous outcome and sample size ≥20,000 and number of cases ≥500 for the scenarios with a binary outcome. In the binary outcome scenarios, the approximate estimators based on the rare outcome assumption worked well when outcome prevalence less than 5% but could lead to substantial bias when the outcome is common; in contrast, the exact estimators always perform well under all outcome prevalences considered. Conclusions Under samples sizes commonly encountered in epidemiology and public health research, the proposed interval estimator is valid for constructing confidence interval. For a binary outcome, the exact estimator without the rare outcome assumption is more robust and stable to estimate NIE and MP. An R package is developed to implement the methods for point and variance estimation discussed in this paper.

On the appropriate interpretation of evidence: the example of anti-vascular endothelial growth factor for diabetic macular edema

Background: Different network meta-analyses (NMAs) on the same topic result in differences in findings. In this review we investigated network meta-analyses comparing ranibizumab with aflibercept for diabetic macular edema in the hope of illuminating why the differences in findings occurred.Findings: For the binary outcome of best corrected visual acuity, different reviews all agreed on their being no clear difference between the two treatments; while continuous outcomes all favour aflibercept over ranibizumab. We discussed four points of particular concern that are illustrated by five similar NMAs, including: network differences, PICO differences, different data from the same measures of effect, differences in what is truly significant.Conclusions: Closer inspection of each of these reviews shows how the methods, including the searches and analyses all differ but the findings, although presented differently and sometimes interpreted differently, were similar.

Statistical Mediation Analysis for Models with a Binary Mediator and a Binary Outcome: the Differences Between Causal and Traditional Mediation Analysis

Mediation analysis is an important statistical method in prevention research, as it can be used to determine effective intervention components. Traditional mediation analysis defines direct and indirect effects in terms of linear regression coefficients. It is unclear how these traditional effects are estimated in settings with binary variables. An important recent methodological advancement in the mediation analysis literature is the development of the causal mediation analysis framework. Causal mediation analysis defines causal effects as the difference between two potential outcomes. These definitions can be applied to any mediation model to estimate natural direct and indirect effects, including models with binary variables and an exposure–mediator interaction. This paper aims to clarify the similarities and differences between the causal and traditional effect estimates for mediation models with a binary mediator and a binary outcome. Causal and traditional mediation analyses were applied to an empirical example to demonstrate these similarities and differences. Causal and traditional mediation analysis provided similar controlled direct effect estimates, but different estimates of the natural direct effects, natural indirect effects, and total effect. Traditional mediation analysis methods do not generalize well to mediation models with binary variables, while the natural effect definitions can be applied to any mediation model. Causal mediation analysis is therefore the preferred method for the analysis of mediation models with binary variables.

Using real-world transaction data to identify money laundering: Leveraging traditional regression and machine learning techniques

Money laundering is a pervasive legal and economic problem that hides criminal activity. Identifying money laundering is a priority for both banks and governments, thus, machine learning algorithms have emerged as a possible strategy to detect suspicious financial activity within financial institutions. We used traditional regression and supervised machine learning techniques to identify bank customers at an increased risk of committing money laundering. Specifically, we assessed whether model performance differed across varying operationalizations of the outcome (e.g., multinomial vs. binary classification) and determined whether the inclusion of investigator-derived novel features (e.g., averages across existing features) could improve model performance. We received two proprietary datasets from Scotiabank, a large bank headquartered in Canada. The datasets included customer account information (N = 4,469) and customers’ monthly transaction histories (N = 2,827) from April 15, 2019 to April 15, 2020. We implemented traditional logistic regression, logistic regression with LASSO regularization (LASSO), K-nearest neighbours (KNN), and extreme gradient boosted models (XGBoost). Results indicated that traditional logistic regression with a binary outcome, conducted with investigator-derived novel features, performed the best with an F1 score of 0.79 and accuracy of 0.72. Models with a binary outcome had higher accuracy than the multinomial models, but the F1 scores yielded mixed results. For KNN and XGBoost, we observed little change or worsening performance after the introduction of the investigator-derived novel features. However, the investigator-derived novel features improved model performance for LASSO and traditional logistic regression. Our findings demonstrate that investigators should consider different operationalizations of the outcome, where possible, and include novel features derived from existing features to potentially improve the detection of customer at risk of committing money laundering.

New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability

This article presents new sensitivity measures in reliability-oriented global sensitivity analysis. The obtained results show that the contrast and the newly proposed sensitivity measures (entropy and two others) effectively describe the influence of input random variables on the probability of failure Pf. The contrast sensitivity measure builds on Sobol, using the variance of the binary outcome as either a success (0) or a failure (1). In Bernoulli distribution, variance Pf(1 − Pf) and discrete entropy—Pfln(Pf) − (1 − Pf)ln(1 − Pf) are similar to dome functions. By replacing the variance with discrete entropy, a new alternative sensitivity measure is obtained, and then two additional new alternative measures are derived. It is shown that the desired property of all the measures is a dome shape; the rise is not important. Although the decomposition of sensitivity indices with alternative measures is not proven, the case studies suggest a rationale structure of all the indices in the sensitivity analysis of small Pf. The sensitivity ranking of input variables based on the total indices is approximately the same, but the proportions of the first-order and the higher-order indices are very different. Discrete entropy gives significantly higher proportions of first-order sensitivity indices than the other sensitivity measures, presenting entropy as an interesting new sensitivity measure of engineering reliability.

High dimensional Selection with Interactions for Binary Outcome (HDSI-BO) Algorithm in Classifying Height Indicators Through Social-life and Well-being Factors

Introduction: High dimensional Selection with Interactions for Binary Outcome (HDSI-BO) algorithm can incorporate interaction terms and combine with existing techniques for feature selection. Simulation studies have validated the ability of HDSI-BO to select true features and consequently, improve prediction accuracy compared to standard algorithms. Our goal is to assess the applicability of HDSI-BO in combining different techniques and measure its predictive performance in a real data study of predicting height indicators by social-life and well-being factors. Methods: HDSI-BO was combined with logistic regression, ridge regression, LASSO, adaptive LASSO, and elastic net. Two-way interaction terms were considered. Hyperparameters used in HDSI-BO were optimized through genetic algorithms with five-fold cross-validation. To measure the performance of feature selection, we fitted final models by logistic regression based on the sets of selected features and used the model’s AUC as a measure. 30 trials were repeated to generate a range of the number of selected features and a 95% confidence interval for AUC. Results: When combined with all of the above methods, HDSI-BO methods achieved higher final AUC values both in terms of mean and confidence interval. In addition, HDSI-BO methods effectively narrowed down the sets of selected features and interaction terms compared with standard methods. Conclusion: The HDSI-BO algorithm combines well with multiple standard methods and has comparable or better predictive performance compared with the standard methods. The computational and time complexity of HDSI-BO is higher but still acceptable. Considering AUC as the single metric cannot comprehensively measure the feature selection performance. More effective metrics of performance should be explored for future work.

Adjusting for time of infection or positive test when estimating the risk of a post-infection outcome in an epidemic

When comparing the risk of a post-infection binary outcome, e.g. hospitalisation, for two variants of an infectious pathogen, it is important to adjust for calendar time of infection to avoid the confounding that would occur if the relative incidence of the two variants and the variant-specific risks of the outcome both change over time. Infection time is typically unknown and time of positive test used instead. Likewise, time of positive test may be used instead of infection time when assessing how the risk of the binary outcome changes over calendar time. Here we show that if mean time from infection to positive test is correlated with the outcome, the risk conditional on positive test time depends on whether incidence of infection is increasing or decreasing over calendar time. This complicates interpretation of risk ratios adjusted for positive test time. We also propose a simple sensitivity analysis that indicates how these risk ratios may differ from the risk ratios adjusted for infection time.

Statistical Development and Validation of Clinical Prediction Models

Summary Clinical prediction models in anesthesia and surgery research have many clinical applications including preoperative risk stratification with implications for clinical utility in decision-making, resource utilization, and costs. It is imperative that predictive algorithms and multivariable models are validated in a suitable and comprehensive way in order to establish the robustness of the model in terms of accuracy, predictive ability, reliability, and generalizability. The purpose of this article is to educate anesthesia researchers at an introductory level on important statistical concepts involved with development and validation of multivariable prediction models for a binary outcome. Methods covered include assessments of discrimination and calibration through internal and external validation. An anesthesia research publication is examined to illustrate the process and presentation of multivariable prediction model development and validation for a binary outcome. Properly assessing the statistical and clinical validity of a multivariable prediction model is essential for reassuring the generalizability and reproducibility of the published tool.