Each month we scan and review the literature to identify new papers describing approaches to active monitoring, signal detection, and other epidemiologic and statistical methods.
Identification of the optimal treatment regimen in the presence of missing covariates. Huang Y, Zhou XH. Stat Med. 2019 Nov 27. doi: 10.1002/sim.8407. [Epub ahead of print] Read More
Exact sequential analysis for multiple weighted binomial end points. Silva IR, Gagne JJ, Najafzadeh M, Kulldorff M. Stat Med. 2019 Nov 25. doi: 10.1002/sim.8405. [Epub ahead of print] Read More
Estimating individualized treatment regimes from crossover designs. Nguyen CT, Luckett DJ, Kahkoska AR, Shearrer GE, Spruijt-Metz D, Davis JN, Kosorok MR. Biometrics. 2019 Nov 19. doi: 10.1111/biom.13186. [Epub ahead of print] Read More
Machine learning for causal inference in Biostatistics. Rose S, Rizopoulos D. Biostatistics. 2019 Nov 19. pii: kxz045. doi: 10.1093/biostatistics/kxz045. [Epub ahead of print] No abstract available. Read More
Regulatory oversight, causal inference, and safe and effective health care machine learning. Stern AD, Price WN. Biostatistics. 2019 Nov 19. pii: kxz044. doi: 10.1093/biostatistics/kxz044. [Epub ahead of print] Read More
Data linkage in pharmacoepidemiology: A call for rigorous evaluation and reporting. Pratt NL, Mack CD, Meyer AM, et al. Pharmacoepidemiol Drug Saf. 2019 Nov 17. doi: 10.1002/pds.4924. [Epub ahead of print] Read More
Corrections for measurement error due to delayed onset of illness for case-crossover designs. Coull BA, Lee S, McGee G, Manjourides J, Mittleman MA, Wellenius GA. Biometrics. 2019 Nov 15. doi: 10.1111/biom.13173. [Epub ahead of print] Read More
One-step targeted maximum likelihood estimation for time-to-event outcomes. Cai W, van der Laan MJ. Biometrics. 2019 Nov 15. doi: 10.1111/biom.13172. [Epub ahead of print] Read More
On the Relation Between G-formula and Inverse Probability Weighting Estimators for Generalizing Trial Results. Dahabreh IJ, Robertson SE, Hernán MA. Epidemiology. 2019 Nov;30(6):807-812. doi: 10.1097/EDE.0000000000001097. Read More
Avoiding pitfalls when combining multiple imputation and propensity scores. Granger E, Sergeant JC, Lunt M. Stat Med. 2019 Nov 20;38(26):5120-5132. doi: 10.1002/sim.8355. Epub 2019 Sep 11. Read More
Estimating individual treatment effects by gradient boosting trees. Sugasawa S, Noma H. Stat Med. 2019 Nov 20;38(26):5146-5159. doi: 10.1002/sim.8357. Epub 2019 Aug 28. Read More
Bayesian additive regression trees and the General BART model. Tan YV, Roy J. Stat Med. 2019 Nov 10;38(25):5048-5069. doi: 10.1002/sim.8347. Epub 2019 Aug 28. Read More
Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators.Bahamyirou A, Blais L, Forget A, Schnitzer ME. Stat Methods Med Res. 2019 Jun;28(6):1637-1650. doi: 10.1177/0962280218772065. Epub 2018 May 2. Read More
Data-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produce a separation of the two exposure groups in terms of propensity score densities which can lead to biased estimates of the treatment effect. To motivate the problem, we evaluated the Targeted Minimum Loss-based Estimation procedure using a simulation scenario to estimate the average treatment effect. We highlight the divergence in estimates obtained when using parametric and data-adaptive methods to estimate the propensity score. We then adapted an existing diagnostic tool based on a bootstrap resampling of the subjects and simulation of the outcome data in order to show that the estimation using data-adaptive methods for the propensity score in this study may lead to large bias and poor coverage. The adapted bootstrap procedure is able to identify this instability and can be used as a diagnostic tool.
Emulating a trial of joint dynamic strategies: An application to monitoring and treatment of HIV-positive individuals. Caniglia EC, Robins JM, Cain LE, Sabin C, Logan R, Abgrall S, Mugavero MJ, Hernández-Díaz S, Meyer L, Seng R, Drozd DR,Seage Iii GR, Bonnet F, Le Marec F, Moore RD, Reiss P, van Sighem A, Mathews WC, Jarrín I, Alejos B, Deeks SG, Muga R, Boswell SL, Ferrer E, Eron JJ, Gill J, Pacheco A, Grinsztejn B, Napravnik S, Jose S, Phillips A, Justice A, Tate J, Bucher HC, Egger M, Furrer H, Miro JM, Casabona J, Porter K, Touloumi G, Crane H, Costagliola D, Saag M, Hernán MA. Stat Med. 2019 Jun 15;38(13):2428-2446. doi: 10.1002/sim.8120. Epub 2019 Mar 18. Read More
Decisions about when to start or switch a therapy often depend on the frequency with which individuals are monitored or tested. For example, the optimal time to switch antiretroviral therapy depends on the frequency with which HIV-positive individuals have HIV RNA measured. This paper describes an approach to use observational data for the comparison of joint monitoring and treatment strategies and applies the method to a clinically relevant question in HIV research: when can monitoring frequency be decreased and when should individuals switch from a first-line treatment regimen to a new regimen? We outline the target trial that would compare the dynamic strategies of interest and then describe how to emulate it using data from HIV-positive individuals included in the HIV-CAUSAL Collaboration and the Centers for AIDS Research Network of Integrated Clinical Systems. When, as in our example, few individuals follow the dynamic strategies of interest over long periods of follow-up, we describe how to leverage an additional assumption: no direct effect of monitoring on the outcome of interest. We compare our results with and without the "no direct effect" assumption. We found little differences on survival and AIDS-free survival between strategies where monitoring frequency was decreased at a CD4 threshold of 350 cells/μl compared with 500 cells/μl and where treatment was switched at an HIV-RNA threshold of 1000 copies/ml compared with 200 copies/ml. The "no direct effect" assumption resulted in efficiency improvements for the risk difference estimates ranging from an 7- to 53-fold increase in the effective sample size.
Bayesian estimation of the average treatment effect on the treated using inverse weighting. Capistrano ESM, Moodie EEM, Schmidt AM. Stat Med. 2019 Jun 15;38(13):2447-2466. doi: 10.1002/sim.8121. Epub 2019 Mar 11. Read More
We develop a Bayesian approach to estimate the average treatment effect on the treated in the presence of confounding. The approach builds on developments proposed by Saarela et al in the context of marginal structural models, using importance sampling weights to adjust for confounding and estimate a causal effect. The Bayesian bootstrap is adopted to approximate posterior distributions of interest and avoid the issue of feedback that arises in Bayesian causal estimation relying on a joint likelihood. We present results from simulation studies to estimate the average treatment effect on the treated, evaluating the impact of sample size and the strength of confounding on estimation. We illustrate our approach using the classic Right Heart Catheterization data set and find a negative causal effect of the exposure on 30-day survival, in accordance with previous analyses of these data. We also apply our approach to the data set of the National Center for Health Statistics Birth Data and obtain a negative effect of maternal smoking during pregnancy on birth weight.
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. J Clin Epidemiol. 2019 Jun;110:12-22. doi: 10.1016/j.jclinepi.2019.02.004. Epub 2019 Feb 11. Read More
OBJECTIVES: The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature. STUDY DESIGN AND SETTING: We conducted a Medline literature search (1/2016 to 8/2017) and extracted comparisons between LR and ML models for binary outcomes. RESULTS: We included 71 of 927 studies. The median sample size was 1,250 (range 72-3,994,872), with 19 predictors considered (range 5-563) and eight events per predictor (range 0.3-6,697). The most common ML methods were classification trees, random forests, artificial neural networks, and support vector machines. In 48 (68%) studies, we observed potential bias in the validation procedures. Sixty-four (90%) studies used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was not addressed in 56 (79%) studies. We identified 282 comparisons between an LR and ML model (AUC range, 0.52-0.99). For 145 comparisons at low risk of bias, the difference in logit(AUC) between LR and ML was 0.00 (95% confidence interval, -0.18 to 0.18). For 137 comparisons at high risk of bias, logit(AUC) was 0.34 (0.20-0.47) higher for ML. CONCLUSION: We found no evidence of superior performance of ML over LR. Improvements in methodology and reporting are needed for studies that compare modeling algorithms.
Evaluating the use of bootstrapping in cohort studies conducted with 1:1 propensity score matching-A plasmode simulation study. Desai RJ, Wyss R, Abdia Y, Toh S, Johnson M, Lee H, Karami S, Major JM, Nguyen M, Wang SV, Franklin JM, Gagne JJ. Pharmacoepidemiol Drug Saf. 2019 Jun;28(6):879-886. doi: 10.1002/pds.4784. Epub 2019 Apr 24. Read More
PURPOSE: Bootstrapping can account for uncertainty in propensity score (PS) estimation and matching processes in 1:1 PS-matched cohort studies. While theory suggests that the classical bootstrap can fail to produce proper coverage, practical impact of this theoretical limitation in settings typical to pharmacoepidemiology is not well studied. METHODS: In a plasmode-based simulation study, we compared performance of the standard parametric approach, which ignores uncertainty in PS estimation and matching, with two bootstrapping methods. The first method only accounted for uncertainty introduced during the matching process (the observation resampling approach). The second method accounted for uncertainty introduced during both PS estimation and matching processes (the PS reestimation approach). Variance was estimated based on percentile and empirical standard errors, and treatment effect estimation was based on median and mean of the estimated treatment effects across 1000 bootstrap resamples. Two treatment prevalence scenarios (5% and 29%) across two treatment effect scenarios (hazard ratio of 1.0 and 2.0) were evaluated in 500 simulated cohorts of 10 000 patients each. RESULTS: We observed that 95% confidence intervals from the bootstrapping approaches but not the standard approach, resulted in inaccurate coverage rates (98%-100% for the observation resampling approach, 99%-100% for the PS reestimation approach, and 95%-96% for standard approach). Treatment effect estimation based on bootstrapping approaches resulted in lower bias than the standard approach (less than 1.4% vs 4.1%) at 5% treatment prevalence; however, the performance was equivalent at 29% treatment prevalence. CONCLUSION: Use of bootstrapping led to variance overestimation and inconsistent coverage, while coverage remained more consistent with parametric estimation.
Evaluation of Socioeconomic Status Indicators for Confounding Adjustment in Observational Studies of Medication Use. Gopalakrishnan C, Gagne JJ, Sarpatwari A, Dejene SZ, Dutcher SK, Levin R, Franklin JM, Schneeweiss S, Desai RJ. Clin Pharmacol Ther. 2019 Jun;105(6):1513-1521. doi: 10.1002/cpt.1348. Epub 2019 Feb 25. Read More
Methodologic research evaluating confounding due to socioeconomic status (SES) in observational studies of medications is limited. We identified 7,109 patients who initiated brand or generic atorvastatin from Medicare claims (2011-2013) linked to electronic medical records and census data. We created a propensity score (PS) containing only claims-based covariates and augmented it with additional claims-based proxies for SES, ZIP code, and block group level SES. Cox models with PS fine-stratification and weighting were used to compare rates of a cardiovascular end point and emergency department visits. Adjustment with only claims-based variables substantially improved balance on all SES variables compared with the unadjusted. Although inclusion of SES in PS models further improved balance on SES variables compared with models with claims-based covariates only, it did not materially change point estimates for either outcome. Inclusion of claims-based proxies may mitigate confounding by SES when aggregate-level SES information is unavailable.
On adaptive propensity score truncation in causal inference. Ju C, Schwab J, van der Laan MJ. Stat Methods Med Res. 2019 Jun;28(6):1741-1760. doi: 10.1177/0962280218774817. Epub 2018 Jul 11. Read More
The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Positivity-C-TMLE, based on the collaborative targeted maximum likelihood estimation (C-TMLE) methodology. We demonstrate the outstanding performance of our novel approach in a variety of simulations by comparing it with other commonly studied estimators. Results show that by adaptively truncating the estimated PS with a more targeted objective function, the Positivity-C-TMLE estimator achieves the best performance for both point estimation and confidence interval coverage among all estimators considered.
Evaluating classification accuracy for modern learning approaches. Li J, Gao M, D'Agostino R. Stat Med. 2019 Jun 15;38(13):2477-2503. doi: 10.1002/sim.8103. Epub 2019 Jan 30. Read More
Deep learning neural network models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are novel and attractive artificial intelligence computing tools. However, evaluation of the performance of these methods is not readily available for practitioners yet. We provide a tutorial for evaluating classification accuracy for various state-of-the-art learning approaches, including familiar shallow and deep learning methods. For qualitative response variables with more than two categories, many traditional accuracy measures such as sensitivity, specificity, and area under the receiver operating characteristic curve are not applicable and we have to consider their extensions properly. In this paper, a few important statistical concepts for multicategory classification accuracy are reviewed and their utilities for various learning algorithms are demonstrated with real medical examples. We offer problem-based R code to illustrate how to perform these statistical computations step by step. We expect that such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics.
A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding. McMaster C, Liew D, Keith C, Aminian P, Frauman A. Drug Saf. 2019 Jun;42(6):721-725. doi: 10.1007/s40264-018-00794-y. Read More
INTRODUCTION: Adverse drug reaction (ADR) detection in hospitals is heavily reliant on spontaneous reporting by clinical staff, with studies in the literature pointing to high rates of underreporting . International Classification of Diseases, 10th Revision (ICD-10) codes have been used in epidemiological studies of ADRs and offer the potential for automated ADR detection systems. OBJECTIVE: The aim of this study was to develop an automated ADR detection system based on ICD-10 codes, using machine-learning algorithms to improve accuracy and efficiency. METHODS: For a 12-month period from December 2016 to November 2017, every inpatient episode receiving an ICD-10 code in the range Y40.0-Y59.9 (ADR code) was flagged for review as a potential ADR. Each flagged admission was assessed by an expert pharmacist and, if needed, reviewed at regular ADR committee meetings. For each report, a determination was made about ADR probability and severity. The dataset was randomly split into training and test sets. A machine-learning model using the random forest algorithm was developed on the training set to discriminate between true and false ADR reports. The model was then applied to the test set to assess accuracy using the area under the receiver operating characteristic (AUC). RESULTS: In the study period, 2917 Y40.0-Y59.9 codes were applied to admissions, resulting in 245 ADR reports after review. These 245 reports accounted for 44.5% of all ADR reporting in our hospital in the study period. A random forest model built on the training set was able to discriminate between true and false reports on the test set with an AUC of 0.803. CONCLUSIONS: Automated ADR detection using ICD-10 coding significantly improved ADR detection in the study period, with improved discrimination between true and false reports by applying a machine-learning model.
Composite interaction tree for simultaneous learning of optimal individualized treatment rules and subgroups. Qiu X, Wang Y. Stat Med. 2019 Jun 30;38(14):2632-2651. doi: 10.1002/sim.8105. Epub 2019 Mar 19. Read More
Treatment response heterogeneity has long been observed in patients affected by chronic diseases. Administering an individualized treatment rule (ITR) offers an opportunity to tailor treatment strategies according to patient-specific characteristics. Overly complex machine learning methods for estimating ITRs may produce treatment rules that have higher benefit but lack transparency and interpretability. In clinical practices, it is desirable to derive a simple and interpretable ITR while maintaining certain optimality that leads to improved benefit in subgroups of patients, if not on the overall sample. In this work, we propose a tree-based robust learning method to estimate optimal piecewise linear ITRs and identify subgroups of patients with a large benefit. We achieve these goals by simultaneously identifying qualitative and quantitative interactions through a tree model, referred to as the composite interaction tree (CITree). We show that it has improved performance compared to existing methods on both overall sample and subgroups via extensive simulation studies. Lastly, we fit CITree to Research Evaluating the Value of Augmenting Medication with Psychotherapy trial for treating patients with major depressive disorders, where we identified both qualitative and quantitative interactions and subgroups of patients with a large benefit.
An Implementation and Visualization of the Tree-Based Scan Statistic for Safety Event Monitoring in Longitudinal Electronic Health Data. Schachterle SE, Hurley S, Liu Q, Petronis KR, Bate A. Drug Saf. 2019 Jun;42(6):727-741. doi: 10.1007/s40264-018-00784-0. Read More
INTRODUCTION: Longitudinal electronic healthcare data hold great potential for drug safety surveillance. The tree-based scan statistic (TBSS), as implemented by the TreeScan® software, allows for hypothesis-free signal detection in longitudinal data by grouping safety events according to branching, hierarchical data coding systems, and then identifying signals of disproportionate recording (SDRs) among the singular events or event groups. OBJECTIVE: The objective of this analysis was to identify and visualize SDRs with the TBSS in historical data from patients using two antifungal drugs, itraconazole or terbinafine. By examining patients who used either itraconazole or terbinafine, we provide a conceptual replication of a previous TBSS analyses by varying methodological choices and using a data source that had not been previously used with the TBSS, i.e., the Optum Clinformatics™ claims database. With this analysis, we aimed to test a parsimonious design that could be the basis of a broadly applicable method for multiple drug and safety event pairs. METHODS: The TBSS analysis was used to examine incident events and any itraconazole or terbinafine use among US-based patients from 2002 through 2007. Event frequencies before and after the first day of drug exposure were compared over 14- and 56-day periods of observation in a Bernoulli model with a self-controlled design. Safety events were classified into a hierarchical tree structure using the Clinical Classifications Software (CCS) which mapped International Classification of Diseases, 9th Revision (ICD-9) codes to 879 diagnostic groups. Using the TBSS, the log likelihood ratio of observed versus expected events in all groups along the CCS hierarchy were compared, and groups of events that occurred at disproportionally high frequencies were identified as potential SDRs; p-values for the potential SDRs were estimated with Monte-Carlo permutation based methods. Output from TreeScan® was visualized and plotted as a network which followed the CCS tree structure. RESULTS: Terbinafine use (n = 223,968) was associated with SDRs for diseases of the circulatory system (14- and 56-day p = 0.001) and heart (14-day p = 0.026 and 56-day p = 0.001) as well as coronary atherosclerosis and other heart disease (14-day p = 0.003 and 56-day p = 0.004). For itraconazole use (n = 36,025), the TBSS identified SDRs for coronary atherosclerosis and other heart disease (p = 0.002) and complications of an implanted or grafted device (14-day p = 0.001 and 56-day p < 0.05). Use of both drugs was associated with SDRs for diseases of the digestive system at 14 days (p < 0.05) and this SDR had been observed among terbinafine users in a previous TBSS analysis with a different data source. The TreeScan® visualization facilitated the identification of the atherosclerosis and other heart disease SDRs as well as highlighting the consistency of the SDR for diseases of the digestive system across drugs and data sources. CONCLUSION: With the TBSS, we identified potential SDRs related to the circulatory system that may reflect the cardiac risk that was described in the itraconazole product label. SDRs for diseases of the digestive system among terbinafine users were also reported in a previous signal detection analysis, although other SDRs from the previous publications were not replicated. The TBSS visualizations aided in the understanding and interpretation of the TBSS output, including the comparisons to the previous publications. In this conceptual replication, differences in the results observed in our analysis and the previous analyses could be attributable to variation in modeling and design choices as well as factors that were intrinsic to the underlying data sources. The broad consistency, but far from perfect concordance, of our results with the known safety profile of these antifungals including the risks from the itraconazole product label supports the rationale for continued investigations of signal detection methods across differing data sources and populations.
A Bayesian nonparametric causal inference model for synthesizing randomized clinical trial and real-world evidence. Wang C, Rosner GL. Stat Med. 2019 Jun 30;38(14):2573-2588. doi: 10.1002/sim.8134. Epub 2019 Mar 18. Read More
With the wide availability of various real-world data (RWD), there is an increasing interest in synthesizing information from both randomized clinical trials and RWD for health-care decision makings. The task of addressing study-specific heterogeneities is one of the most difficult challenges in synthesizing data from disparate sources. Bayesian hierarchical models with nonparametric extension provide a powerful and convenient platform that formalizes the information borrowing strength across the sources. In this paper, we propose a propensity score-based Bayesian nonparametric Dirichlet process mixture model that summarizes subject-level information from randomized and registry studies to draw inference on the causal treatment effect. Simulation studies are conducted to evaluate the model performance under different scenarios. In addition, we demonstrate the proposed method using data from a clinical study on angiotensin converting enzyme inhibitor for treating congestive heart failure.
Current approaches to identify sections within clinical narratives from electronic health records: a systematic review. Pomares-Quimbaya A, Kreuzthaler M, Schulz S. BMC Med Res Methodol. 2019 Jul 18;19(1):155. doi: 10.1186/s12874-019-0792-y. Read More
Exposure density sampling: Dynamic matching with respect to a time-dependent exposure. Ohneberg K, Beyersmann J, Schumacher M. Stat Med. 2019 Jul 17. doi: 10.1002/sim.8305. [Epub ahead of print] Read More
A primer on quantitative bias analysis with positive predictive values in research using electronic health data. Newcomer SR, Xu S, Kulldorff M, Daley MF, Fireman B, Glanz JM. J Am Med Inform Assoc. 2019 Jul 31. pii: ocz094. doi: 10.1093/jamia/ocz094. [Epub ahead of print] Read More
Using Electronic Health Records to Derive Control Arms for Early Phase Single-Arm Lung Cancer Trials: Proof-of-Concept in Randomized Controlled Trials. Carrigan G, Whipple S, Capra WB, et al. Clin Pharmacol Ther. 2019 Jul 27. doi: 10.1002/cpt.1586. [Epub ahead of print] Read More
Mediational E-values: Approximate sensitivity analysis for unmeasured mediator-outcome confounding. Smith LH, VanderWeele TJ. Epidemiology. 2019 Jul 23. doi: 10.1097/EDE.0000000000001064. [Epub ahead of print] Read More
Bias-adjusted Kaplan-Meier survival curves for marginal treatment effect in observational studies. Wang X, Bai F, Pang H, George SL. J Biopharm Stat. 2019;29(4):592-605. doi: 10.1080/10543406.2019.1633659. Epub 2019 Jul 9. Read More
Targeted learning with daily EHR data. Sofrygin O, Zhu Z, Schmittdiel JA, et al. Stat Med. 2019 Jul 20;38(16):3073-3090. doi: 10.1002/sim.8164. Epub 2019 Apr 25. Read More
Adjusting for unmeasured confounding using validation data: Simplified two-stage calibration for survival and dichotomous outcomes. Hjellvik V, De Bruin ML, Samuelsen SO, et al. Stat Med. 2019 Jul 10;38(15):2719-2734. doi: 10.1002/sim.8131. Epub 2019 Mar 3. Read More
A Case-Crossover-Based Screening Approach to Identifying Clinically Relevant Drug-Drug Interactions in Electronic Healthcare Data. Bykov K, Schneeweiss S, Glynn RJ, Mittleman MA, Gagne JJ. Clin Pharmacol Ther. 2019 Jul;106(1):238-244. doi: 10.1002/cpt.1376. Epub 2019 Mar 18. Read More
Bridging observational studies and randomized experiments by embedding the former in the latter. Bind MC, Rubin DB. Stat Methods Med Res. 2019 Jul;28(7):1958-1978. doi: 10.1177/0962280217740609. Epub 2017 Nov 29. Read More
Causal inference with measurement error in outcomes: Bias analysis and estimation methods. Shu D, Yi GY. Stat Methods Med Res. 2019 Jul;28(7):2049-2068. doi: 10.1177/0962280217743777. Epub 2017 Dec 15. Read More
Estimation of average treatment effects among multiple treatment groups by using an ensemble approach. Yan X, Abdia Y, Datta S, et al. Stat Med. 2019 Jul 10;38(15):2828-2846. doi: 10.1002/sim.8146. Epub 2019 Apr 2. Read More Data linkages between patient-powered research networks and health plans: a foundation for collaborative research. Agiro A, Chen X, Eshete B, et al. J Am Med Inform Assoc. 2019 Jul 1;26(7):594-602. doi: 10.1093/jamia/ocz012. Read More
Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on survival or time-to-event outcomes. Austin PC. Stat Methods Med Res. 2019 Aug;28(8):2348-2367.doi:10.1177/0962280218776690. Epub 2018 Jun 5. Read More
Comparison of alternative approaches to trim subjects in the tails of the propensity score distribution. Glynn RJ, Lunt M, Rothman KJ, Poole C, et al. Pharmacoepidemiol Drug Saf. 2019 Aug 5. doi: 10.1002/pds.4846 [Epub ahead of print] Read More
Comparison of self-controlled designs for evaluating outcomes of drug-drug interactions: simulation study. Bykov K, Franklin JM, Li H, Gagne JJ. Epidemiology. 2019 Aug 12. doi:10.1097/EDE.0000000000001087. [Epub ahead of print] Read More
Data-adaptive longitudinal model selection in causal inference with collaborative targeted minimum loss-based estimation. Schnitzer ME, Sango J, Guerra SF, van der Laan MJ. Biometrics. 2019 Aug 9. doi: 10.1111/biom.13135. [Epub ahead of print] Read More
Estimating individual treatment effects by gradient boosting trees. Sugasawa S, Noma H. Stat Med. 2019 Aug 28. doi: 10.1002/sim.8357. [Epub ahead of print] Read More
How to obtain valid tests and confidence intervals after propensity score variable selection? Dukes O, Vansteelandt S. Stat Methods Med Res. 2019 Aug 6:962280219862005. doi:10.1177/0962280219862005. [Epub ahead of print] Read More
Inverse probability weighted Cox model in multi-site studies without sharing individual-level data. Shu D, Yoshida K, Fireman BH, Toh S. Stat Methods Med Res. 2019 Aug 26:962280219869742. doi: 10.1177/0962280219869742. [Epub ahead of print] Read More
Postmarket surveillance of arthroplasty device components using machine learning methods. Cafri G, Graves SE, Sedrakyan A, Fan J, et al. Pharmacoepidemiol Drug Saf. 2019 Aug 16. doi:10.1002/pds.4882. [Epub ahead of print] Read More
Subgroup balancing propensity score. Dong J, Zhang JL, Zeng S, Li F. Stat Methods Med Res. 2019 Aug 28:962280219870836. doi: 10.1177/0962280219870836. [Epub ahead of print] Read More
Time-to-event analysis when the event is defined on a finite time interval. Lee C, Lee SJ, Haneuse S. Stat Methods Med Res.2019 Aug 22:962280219869364.doi:10.1177/0962280219869364. [Epub ahead of print] Read More
Transparent Reporting on Research Using Unstructured Electronic Health Record Data to Generate 'Real World' Evidence of Comparative Effectiveness and Safety. Wang SV, Patterson OV, Gagne JJ, et al. Drug Saf. 2019 Aug 26. doi: 10.1007/s40264-019-00851-0. [Epub ahead of print] Read More
Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions. Schomaker M, Luque-Fernandez MA, Leroy V, Davies MA. Stat Med. 2019 Aug 22. doi: 10.1002/sim.8340. [Epub ahead of print] Read More
Estimation of high-dimensional propensity scores with multiple exposure levels. Eberg M, Platt RW, Reynier P, Filion KB. Pharmacoepidemiol Drug Saf. 2019 Sep 30. doi: 10.1002/pds.4890. [Epub ahead of print]. Read More
Use of Time-Dependent Propensity Scores to Adjust Hazard Ratio Estimates in Cohort Studies with Differential Depletion of Susceptibles. Wyss R, Gagne JJ, Zhao Y, et al. Epidemiology. 2019 Sep 26. doi: 10.1097/EDE.0000000000001107. [Epub ahead of print]. Read More
An example of how immortal time bias can reverse the results of an observational study. Airaksinen J, Pentti J, Suominen S, Vahtera J, Kivimäki M. Epidemiology. 2019 Sep 19. doi: 10.1097/EDE.0000000000001103. [Epub ahead of print]. Read More
Measuring Frailty in Administrative Claims Data: Comparative Performance of Four Claims-Based Frailty Measures in the United States Medicare Data. Kim DH, Patorno E, Pawar A, Lee H, Schneeweiss S, Glynn RJ. J Gerontol A Biol Sci Med Sci. 2019 Sep 30. pii: glz224. doi: 10.1093/gerona/glz224. [Epub ahead of print]. Read More
Adjusting for time-varying confounders in survival analysis using structural nested cumulative survival time models. Seaman S, Dukes O, Keogh R, Vansteelandt S. Biometrics. 2019 Sep 28. doi: 10.1111/biom.13158. [Epub ahead of print]. Read More
Comparing record linkage software programs and algorithms using real-world data. Karr AF, Taylor MT, West SL, et al. PLoS One. 2019 Sep 24;14(9):e0221459. doi: 10.1371/journal.pone.0221459. Read More
Nonrandomized real-world evidence to support regulatory decision-making: Process for a randomized trial replication project. Franklin JM, Pawar A, Martin D, et al. Clin Pharmacol Ther. 2019 Sep 21. doi: 10.1002/cpt.1633. [Epub ahead of print] Read More
Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies. Wang C, Li H, Chen WC, et al. J Biopharm Stat. 2019;29(5):731-748. doi: 10.1080/10543406.2019.1657133. Epub 2019 Sep 17. Read More
Avoiding pitfalls when combining multiple imputation and propensity scores. Granger E, Sergeant JC, Lunt M. Stat Med. 2019 Nov 20;38(26):5120-5132. doi: 10.1002/sim.8355. Epub 2019 Sep 11. Read More
Are confidence intervals better termed "uncertainty intervals"? Gelman A, Greenland S. BMJ. 2019 Sep 10;366:l5381. doi: 10.1136/bmj.l5381. Read More
Performance evaluation of regression splines for propensity score adjustment in post-market safety analysis with multiple treatments. Tian Y, Baro E, Zhang R. J Biopharm Stat. 2019;29(5):810-821. doi: 10.1080/10543406.2019.1657138. Epub 2019 Sep 10. Read More
Multiple imputation for systematically missing confounders within a distributed data drug safety network: A simulation study and real-world example. Secrest MH, Platt RW, Reynier P, et al. Pharmacoepidemiol Drug Saf. 2019 Sep 4. doi: 10.1002/pds.4876. [Epub ahead of print] Read More
Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning. Chen L, Gu Y, Ji X, Sun Z, Li H, Gao Y, Huang Y. J Am Med Inform Assoc. 2019 Oct 7. pii: ocz141. doi: 10.1093/jamia/ocz141. [Epub ahead of print] Read More
Using instrumental variables to estimate the attributable fraction. Dahlqwist E, Kutalik Z, Sjölander A. Stat Methods Med Res. 2019 Oct 23:962280219879175. doi: 10.1177/0962280219879175. [Epub ahead of print] Read More
Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners. Desai RJ, Franklin JM. BMJ. 2019 Oct 23;367:l5657. doi: 10.1136/bmj.l5657. No abstract available. Read More
Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort. Fang G, Annis IE, Elston-Lafata J, Cykert S. J Am Med Inform Assoc. 2019 Oct 1;26(10):977-988. doi: 10.1093/jamia/ocz036. Read More
Reproducing Protocol-based Studies Using Parameterizable Tools - Comparison of Analytic Approaches Used by Two Medical Product Surveillance Networks. Huang TY, Welch EC, Shinde MU, et al Clin Pharmacol Ther. 2019 Oct 20. doi: 10.1002/cpt.1698. [Epub ahead of print] Read More
Effect heterogeneity and variable selection for standardizing causal effects to a target population. Huitfeldt A, Swanson SA, Stensrud MJ, Suzuki E. Eur J Epidemiol. 2019 Oct 26. doi: 10.1007/s10654-019-00571-w. [Epub ahead of print] Read More
Estimating treatment effects with machine learning. McConnell KJ, Lindner S. Health Serv Res. 2019 Oct 10. doi: 10.1111/1475-6773.13212. [Epub ahead of print] Read More
An empirical assessment of immeasurable time bias in the setting of nested case-control studies: Statins and all-cause mortality among patients with heart failure. Oh IS, Filion KB, Jeong HE, Shin JY. Pharmacoepidemiol Drug Saf. 2019 Oct;28(10):1318-1327. doi: 10.1002/pds.4888. Epub 2019 Aug 20. Read More
Privacy-protecting multivariable-adjusted distributed regression analysis for multi-center pediatric study. Toh S, Rifas-Shiman SL, Lin PD, et al, PCORnet Antibiotics and Childhood Growth Study Group. Pediatr Res. 2019 Oct 2. doi: 10.1038/s41390-019-0596-0. [Epub ahead of print] Read More
Inverse probability weighting methods for Cox regression with right-truncated data. Vakulenko-Lagun B, Mandel M, Betensky RA. Biometrics. 2019 Oct 17. doi: 10.1111/biom.13162. [Epub ahead of print] Read More Hybrid clinical trials to generate real-world evidence: design considerations from a sponsor's perspective. Zhu M, Sridhar S, Hollingsworth R, et al. Contemp Clin Trials. 2019 Oct 24:105856. doi: 10.1016/j.cct.2019.105856. [Epub ahead of print] Read More