BMC Medical Research Methodology

Network meta-analysis for survival outcome data often involves several studies only reported dichotomized outcomes (i.e., the numbers of events and sample sizes of individual arms). To avoid the reporting biases via eliminating these studies in the syntesis analyses, Woods et al. (2010; BMC Med Res Methodol 10:54) proposed a Bayesian approach to combine the survival and dichotomized outcome data using hierarchical models. However, the Bayesian methods require complicated computations involving t...

ObjectiveWe aimed to increase research and training capacity for the All of Us community through an R package designed to reduce barriers to entry to the Researcher Workbench. Materials and MethodsWe developed the open-source R package allofus, available on the R package repository CRAN. The package provides functions that address common challenges we encountered while working with All of Us Research Program data. We tested the package with standard R unit tests and in real research projects. ...

ObjectivesWhile randomized controlled trials (RCTs) are considered a standard for evidence on the efficacy of medical treatments, non-randomized real-world evidence (RWE) studies using data from health insurance claims or electronic health records can provide important complementary evidence. The use of RWE to inform decision-making has been questioned because of concerns regarding confounding in non-randomized studies and the use of secondary data. RCT-DUPLICATE was a demonstration project that...

Low trial generalizability is a concern. The Food and Drug Administration had guidance on broadening trial eligibility criteria to enroll underrepresented populations. However, investigators are hesitant to do so because of concerns over patient safety. There is a lack of methods to rationalize criteria design. In this study, we used data from a large research network to assess how adjustments of eligibility criteria can jointly affect generalizability and patient safety (i.e the number of serio...

ObjectiveTo empirically explore the level of agreement of the treatment hierarchies from different ranking metrics in network meta-analysis (NMA) and to investigate how network characteristics influence the agreement. DesignEmpirical evaluation from re-analysis of network meta-analyses. Data232 networks of four or more interventions from randomised controlled trials, published between 1999 and 2015. MethodsWe calculated treatment hierarchies from several ranking metrics: relative treatment ef...

BackgroundUnmeasured confounding is a persistent concern in observational studies. We can quantitatively assess the impact of unmeasured confounding using a quantitative bias analysis (QBA). A QBA specifies the relationship between the unmeasured confounder(s), U, and study data via its bias parameters. There are two broad classes of QBA methods: deterministic and probabilistic. We focus on a probabilistic QBA which incorporates external information about U via prior distribution(s) placed on t...

Estimating treatment effects from time-to-event data in observational studies requires careful adjustment for both confounding and informative censoring. While inverse probability of treatment weighting (IPTW) and inverse probability of censoring weighting (IPCW) have been used to address these sources of bias separately, their combined application remains underexplored, especially in high-dimensional, real-world datasets. In this paper, we benchmark IPTW, IPCW, and their combination to estimate...

BackgroundEstimating causal effects in observational health data is challenging due to confounding by indication. Traditional approaches such as inverse probability of treatment weighting (IPTW) rely on correct model specification, which is difficult in high-dimensional settings. We implemented an offset-based double machine learning (Offset-DML) practical framework for estimating binary treatment effects on the log-odds scale using logistic regression. MethodsWe have conducted a plasmode simul...

BackgroundRecent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. MethodsWe performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. ResultsThe approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognos...

PurposeSimulation studies are used in pharmacoepidemiology for evaluating inferential methods in a controlled setting, whereby a known data-generating mechanism allows evaluation of the performance of different approaches and assumptions. This study aimed to review simulation studies performed in pharmacoepidemiology. MethodsWe conducted a review of all papers published in the journal of Pharmacoepidemiology and Drug Safety (PDS) over the period 2017 to 2024. We extracted data on study characte...

ObjectiveFindings from family-based analyses, such as sibling comparisons, are often reported using only odds ratios or hazard ratios. We demonstrate how this can be improved upon by applying the marginalized between-within framework. Study Design and SettingWe provide an overview of sibling comparison methods and the marginalized between-within framework, which enables estimation of absolute risks and clinically relevant metrics while accounting for shared familial confounding. We illustrate t...

Randomized clinical trials (RCTs) are essential to guide medical practice; however, their generalizability to a given population is often uncertain. We developed a statistically informed Generative Adversarial Network (GAN) model, RCT-Twin-GAN, that leverages relationships between covariates and outcomes and generates a digital twin of an RCT (RCT-Twin) conditioned on covariate distributions from a second patient population. We used RCT-Twin-GAN to reproduce treatment effect outcomes of the Syst...

Adjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sample size requirements in time-to-event trials. We consider both parametric simulations and simulations derived from the TCGA cohort of hepatocellular carcinoma (HCC) patients to assess how sample size requirements are reduced with covariate adjustment. Simulation...

Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management s...

Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed rep...

In progressive diseases, like Alzheimers disease, treatments that slow progression should start early in the disease course to longer maintain higher levels of functioning. In corresponding clinical trials, the treatment effect is usually expressed in terms of mean differences on a clinical scale. Early in the disease course, however, treatment effects expressed on a clinical scale are often small but may nonetheless correspond to an important slowing of disease progression. This complicates the...

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding treatment effects on health-related outcomes using real-world data requires defining a causal parameter and imposing relevant identification assumptions to translate it into a statistical estimand. Semiparametric methods, like the targeted maximum likelihood estimator (TMLE), have been developed to construct asymptotically linear estimators of these parameters. To further establish the asymptotic efficiency of these estimators, two conditions must be met: 1)...

BackgroundRisk-based analyses are increasingly popular for understanding heterogeneous treatment effects (HTE) in clinical trials. For time-to-event analyses, the assumption that high-risk patients benefit most on the clinically important absolute scale when hazard ratios (HRs) are constant across risk strata might not hold. Absolute treatment effects can be measured as either the risk difference (RD) at a given time point or the difference in restricted mean survival time ({Delta}RMST) which al...

Multi-criteria decision analysis (MCDA) is a benefit-risk assessment tool that evaluates multiple competing benefit and risk endpoints simultaneously. MCDA has the potential to aid sponsors in making effective and informed go/no-go decisions for clinical development programs. MCDA involves assigning weights to benefit and risk endpoints based on their relative importance (i.e., utility weight) and using them to compute a single utility score that represents the overall benefit-risk profile of th...

BackgroundIn biostatistics, assessing the fragility of research findings is crucial for understanding their clinical significance. This study focuses on the fragility index, unit fragility index, and relative risk index as measures to evaluate statistical fragility. The relative risk index quantifies the deviation of observed findings from therapeutic equivalence. In contrast, the fragility indices assess the susceptibility of p-values to change significance with minor alterations in outcomes wi...