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INTRODUCTION: Alzheimer's disease (AD) is a neurological disorder with variability in pathology and clinical progression. AD patients may differ in individual‐level benefit from amyloid beta removal therapy. METHODS: Random forest models were applied to the EMERGE trial to create an individual‐level treatment response (ITR) score which represents individual‐level benefit of high‐dose aducanumab relative to the placebo. This ITR score was used to test the existence of heterogeneity in treatment effect (HTE). RESULTS: We found statistical evidence of HTE in the Clinical Dementia Rating–Sum of Boxes (CDR‐SB;P = 0.034). The observed CDR‐SB benefit was 0.79 points greater in the group with the top 25% of ITR score compared to the remaining 75% (P = 0.020). Of note, the highest treatment responders had lower hippocampal volume, higher plasma phosphorylated tau 181 and a shorter duration of clinical AD at baseline. DISCUSSION: This ITR analysis provides a proof of concept for precision medicine in future AD research and drug development. Highlights: Emerging trials have shown a population‐level benefit from amyloid beta (Aβ) removal in slowing cognitive decline in early Alzheimer's disease (AD).This work demonstrates significant heterogeneity of individual‐level treatment effect of aducanumab in early AD.The greatest clinical responders to Aβ removal therapy have a pattern of more severe neurodegenerative process. [ABSTRACT FROM AUTHOR]

Analyses of distributed data networks of rare diseases are constrained by legitimate privacy and ethical concerns. Analytical centers (e.g. research institutions) are thus confronted with the challenging task of obtaining data from recruiting sites that are often unable or unwilling to share personal records of participants. For time-to-event data, recently popularized disclosure techniques with privacy guarantees (e.g., Differentially Private Generative Adversarial Networks) are generally computationally expensive or inaccessible to applied researchers. To perform the widely used Cox proportional hazards regression, we propose an easy-to-implement privacy-preserving data analysis technique by pooling (i.e. aggregating) individual records of covariates at recruiting sites under the nested case-control sampling framework before sharing the pooled nested case-control subcohort. We show that the pooled hazard ratio estimators, under the pooled nested case-control subsamples from the contributing sites, are maximum likelihood estimators and provide consistent estimates of the individual level full cohort HRs. Furthermore, a sampling technique for generating pseudo-event times for individual subjects that constitute the pooled nested case-control subsamples is proposed. Our method is demonstrated using extensive simulations and analysis of the National Lung Screening Trial data. The utility of our proposed approach is compared to the gold standard (full cohort) and synthetic data generated using classification and regression trees. The proposed pooling technique performs to near-optimal levels comparable to full cohort analysis or synthetic data; the efficiency improves in rare event settings when more controls are matched on during nested case-control subcohort sampling. [ABSTRACT FROM AUTHOR]

Distributed data networks (DDNs) with horizontally partitioned datasets are viable resources for multicenter research studies and pharmacosurveillance. Within DDNs, maintaining confidentiality and limiting the disclosure of sensitive information is critical. Consequently, data sharing between partners within the same network is either restricted or completely prohibited during statistical modeling. Current privacy-preserving methods for logistic regression span two extreme paradigms: meta-analysis (MA), which combines estimates based on partner-specific estimates, is convenient for the analytical center (AC) but requires separate implementations of the analysis by each data node; while distributed regression (DR), which provides overall estimates based on partner-specific data summaries, produces rigorous solutions but is an iterative process that is both time and resource consuming. A practical middle ground that combines the convenience of MA and the rigor of DR is lacking. We propose a likelihood-based approach for logistic regression modeling that combines the rigor of DR and the convenience of MA. The two-stage approach has an equivalent estimation performance as DR but foregoes its multiple iterative steps through an MA update step, and is therefore more user-friendly. The approach uses only aggregate-level covariates to estimate a starting pooled effect estimate and within-node data summaries for a single-shot update of the pooled estimate without requiring individual covariate values at the AC. We call the approach hybrid Pooled Logistic Regression (hPoLoR) and show that it conveniently provides accurate and efficient estimates of the standard individual-level log odds ratios and standard errors without revealing personal data. Hence hPoLoR provides a rigorous yet convenient and application-friendly alternative to MA and DR. The method is demonstrated through extensive simulations and application to the JCUSH data. [ABSTRACT FROM AUTHOR]

Background: The performance of risk prediction models is often characterized in terms of discrimination and calibration. The receiver-operating characteristic (ROC) curve is widely used for evaluating model discrimination. However, when comparing ROC curves across different samples, the effect of case mix makes the interpretation of discrepancies difficult. Further, compared with model discrimination, evaluating model calibration has not received the same level of attention. Current methods for examining model calibration require specification of smoothing or grouping factors. Methods: We introduce the "model-based" ROC curve (mROC) to assess model calibration and the effect of case mix during external validation. The mROC curve is the ROC curve that should be observed if the prediction model is calibrated in the external population. We show that calibration-in-the-large and the equivalence of mROC and ROC curves are together sufficient conditions for the model to be calibrated. Based on this, we propose a novel statistical test for calibration that, unlike current methods, does not require any subjective specification of smoothing or grouping factors. Results: Through a stylized example, we demonstrate how mROC separates the effect of case mix and model miscalibration when externally validating a risk prediction model. We present the results of simulation studies that confirm the properties of the new calibration test. A case study on predicting the risk of acute exacerbations of chronic obstructive pulmonary disease puts the developments in a practical context. R code for the implementation of this method is provided. Conclusion: mROC can easily be constructed and used to interpret the effect of case mix and calibration on the ROC plot. Given the popularity of ROC curves among applied investigators, this framework can further promote assessment of model calibration. Highlights: Compared with examining model discrimination, examining model calibration has not received the same level of attention among investigators who develop or examine risk prediction models. This article introduces the model-based ROC (mROC) curve as the basis for graphical and statistical examination of model calibration on the ROC plot. This article introduces a formal statistical test based on mROC for examining model calibration that does not require arbitrary smoothing or grouping factors. Investigators who develop or validate risk prediction models can now also use the popular ROC plot for examining model calibration, as a critical but often neglected component in predictive analytics. [ABSTRACT FROM AUTHOR]

For a continuous time-to-event outcome and an expensive-to-measure exposure, we develop a pooling design and propose a likelihood-based approach to estimate the hazard ratios (HRs) of a Cox proportional hazards (PH) model. Our proposed approach fits a PH model based on pooled exposures with individually observed time-to-event outcomes. The design and estimation exploits the equivalence of the conditional logistic likelihood functions arising from a matched case-control study and the partial likelihood function of a riskset-matched, nested case-control (NCC) subset of a cohort study. To create the pools, we first focus on an NCC subcohort. Pools are formed at random while keeping the matching intact. Pool-level exposure and confounders are then evaluated and used in the likelihood to estimate the HR and the standard error of the estimates. The estimators are MLEs, provide consistent estimates of the individual-level HRs, and are asymptotically normal. Our simulation results indicate that the pooled estimates are comparable to the estimates obtained from the NCC subcohort. The units of analysis for the pooled design are the pools and not the individual participants. Hence the effective sample size is reduced. Therefore, the variance of the HR estimate increases with increasing poolsize. However, this variance inflation in small samples can be offset by including more matched controls per case within the NCC subcohort. An application is demonstrated with the Second Manifestations of ARTerial disease (SMART) study. [ABSTRACT FROM AUTHOR]