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1.
Am J Gastroenterol ; 2021 Jan 28.
Artículo en Inglés | MEDLINE | ID: mdl-33560648

RESUMEN

INTRODUCTION: Famotidine has been posited as a potential treatment for coronavirus disease 2019 (COVID-19). We compared the incidence of COVID-19 outcomes (i.e., death and death or intensive services use) among hospitalized famotidine users vs proton pump inhibitors (PPIs) users, hydroxychloroquine users, or famotidine nonusers separately. METHODS: We constructed a retrospective cohort study using data from COVID-19 Premier Hospital electronic health records. The study population was COVID-19 hospitalized patients aged 18 years or older. Famotidine, PPI, and hydroxychloroquine exposure groups were defined as patients dispensed any medication containing 1 of the 3 drugs on the day of admission. The famotidine nonuser group was derived from the same source population with no history of exposure to any drug with famotidine as an active ingredient before or on the day of admission. Time at risk was defined based on the intention-to-treat principle starting 1 day after admission to 30 days after admission. For each study comparison group, we fit a propensity score model through large-scale regularized logistic regression. The outcome was modeled using a survival model. RESULTS: We identified 2,193 users of PPI, 5,950 users of the hydroxychloroquine, 1,816 users of famotidine, and 26,820 nonfamotidine users. After propensity score stratification, the hazard ratios (HRs) for death were as follows: famotidine vs no famotidine HR 1.03 (0.89-1.18), vs PPIs: HR 1.14 (0.94-1.39), and vs hydroxychloroquine: 1.03 (0.85-1.24). Similar results were observed for the risk of death or intensive services use. DISCUSSION: We found no evidence of a reduced risk of COVID-19 outcomes among hospitalized COVID-19 patients who used famotidine compared with those who did not or compared with PPI or hydroxychloroquine users.

2.
Regul Toxicol Pharmacol ; 120: 104866, 2021 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-33454352

RESUMEN

Many observational studies explore the association between acetaminophen and cancer, but known limitations such as vulnerability to channeling, protopathic bias, and uncontrolled confounding hamper the interpretability of results. To help understand the potential magnitude of bias, we identify key design choices in these observational studies and specify 10 study design variants that represent different combinations of these design choices. We evaluate these variants by applying them to 37 negative controls - outcome presumed not to be caused by acetaminophen - as well as 4 cancer outcomes in the Clinical Practice Research Datalink (CPRD) database. The estimated odds and hazards ratios for the negative controls show substantial bias in the evaluated design variants, with far fewer of the 95% confidence intervals containing 1 than the nominal 95% expected for negative controls. The effect-size estimates for the cancer outcomes are comparable to those observed for the negative controls. A comparison of exposed and unexposed reveals many differences at baseline for which most studies do not correct. We observe that the design choices made in many of the published observational studies can lead to substantial bias. Thus, caution in the interpretation of published studies of acetaminophen and cancer is recommended.

5.
Pharmacoepidemiol Drug Saf ; 29(11): 1382-1392, 2020 11.
Artículo en Inglés | MEDLINE | ID: mdl-32964514

RESUMEN

PURPOSE: Clinical trials compare outcomes among patients receiving study treatment with comparators drawn from the same source. These internal controls are missing in single arm trials and from long-term extensions (LTE) of trials including only the treatment arm. An external control group derived from a different setting is then required to assess safety or effectiveness. METHODS: We present examples of external control groups that demonstrate some of the issues that arise and make recommendations to address them through careful assessment of the data source fitness for use, design, and analysis steps. RESULTS: Inclusion and exclusion criteria and context that produce a trial population may result in trial patients with different clinical characteristics than are present in an external comparison group. If these differences affect the risk of outcomes, then a comparison of outcome occurrence will be confounded. Further, patients who continue into LTE may differ from those initially entering the trial due to treatment effects. Application of appropriate methods is needed to make valid inferences when such treatment or selection effects are present. Outcome measures in a trial may be ascertained and defined differently from what can be obtained in an external comparison group. Differences in sensitivity and specificity for identification or measurement of study outcomes leads to information bias that can also invalidate inferences. CONCLUSION: This review concentrates on threats to the valid use of external control groups both in the scenarios of single arm trials and LTE of randomized controlled trials, along with methodological approaches to mitigate them.

6.
J Am Med Inform Assoc ; 27(7): 1028-1036, 2020 Jul 01.
Artículo en Inglés | MEDLINE | ID: mdl-32626900

RESUMEN

OBJECTIVE: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites. MATERIALS AND METHODS: Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study and (2) a real-world use case study using 4 datasets from the Observational Health Data Sciences and Informatics network. RESULTS: On the one hand, our simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (ie, the pooled estimator). The relative bias was <0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 20% when the event rate is 1%. In the Observational Health Data Sciences and Informatics network application, the ODAC estimates have a relative bias <5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC. CONCLUSIONS: ODAC is a privacy-preserving and noniterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner.

7.
Ther Innov Regul Sci ; 54(6): 1477-1488, 2020 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-32514736

RESUMEN

In late 2018, the Food and Drug Administration (FDA) outlined a framework for evaluating the possible use of real-world evidence (RWE) to support regulatory decision-making. This framework was created to facilitate studies that would generate high-quality RWE, including pragmatic clinical trials (PCTs), which are randomized trials designed to inform clinical or policy decisions by assessing the real-world effectiveness of an intervention. There is general agreement among experts that the use of existing healthcare and patient-generated data holds promise for making randomized trials more efficient, less costly, and more generalizable. Yet the benefits of relying on real-world data sources must be weighed against difficulties with ensuring data integrity and completeness. Additionally, appropriately monitoring patient safety in randomized trials of new drugs using healthcare system data that might not be available in real time can be quite difficult. Recognizing that these and other concerns are critical to the development and acceptability of PCTs, a group of stakeholders from academia, industry, professional organizations, regulatory bodies, government agencies, and patient advocates discussed a path forward for PCT growth and sustainability at a think tank meeting entitled "Monitoring and Analyzing Data from Pragmatic Streamlined Randomized Clinical Trials," which took place in January 2019 (Washington, DC). The goals of this meeting were to: (1) evaluate study design and methodological options specific to PCTs that have the potential to yield high-quality evidence; (2) discuss best practices to ensure data quality in PCTs; and (3) identify appropriate methods for study monitoring. Proceedings from the think tank meeting are summarized in this manuscript.

8.
Drug Saf ; 43(9): 927-942, 2020 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-32500272

RESUMEN

INTRODUCTION: Observational studies estimating severe outcomes for paracetamol versus ibuprofen use have acknowledged the specific challenge of channeling bias. A previous study relying on negative controls suggested that using large-scale propensity score (LSPS) matching may mitigate bias better than models using limited lists of covariates. OBJECTIVE: The aim was to assess whether using LSPS matching would enable the evaluation of paracetamol, compared to ibuprofen, and increased risk of myocardial infarction, stroke, gastrointestinal (GI) bleeding, or acute renal failure. STUDY DESIGN AND SETTING: In a new-user cohort study, we used two propensity score model strategies for confounder controls. One replicated the approach of controlling for a hand-picked list. The second used LSPSs based on all available covariates for matching. Positive and negative controls assessed residual confounding and calibrated confidence intervals. The data source was the Clinical Practices Research Datalink (CPRD). RESULTS: A substantial proportion of negative controls were statistically significant after propensity score matching on the publication covariates, indicating considerable systematic error. LSPS adjustment was less biased, but residual error remained. The calibrated estimates resulted in very wide confidence intervals, indicating large uncertainty in effect estimates once residual error was incorporated. CONCLUSIONS: For paracetamol versus ibuprofen, when using LSPS methods in the CPRD, it is only possible to distinguish true effects if those effects are large (hazard ratio > 2). Due to their smaller hazard ratios, the outcomes under study cannot be differentiated from null effects (represented by negative controls) even if there were a true effect. Based on these data, we conclude that we are unable to determine whether paracetamol is associated with an increased risk of myocardial infarction, stroke, GI bleeding, and acute renal failure compared to ibuprofen, due to residual confounding.

9.
Curr Med Res Opin ; 36(7): 1117-1124, 2020 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-32338068

RESUMEN

Objective: Observational evidence suggests that patients with type 2 diabetes mellitus (T2DM) are at increased risk for acute pancreatitis (AP) versus those without T2DM. A small number of AP events were reported in clinical trials of the sodium glucose co-transporter 2 inhibitor canagliflozin, though no imbalances were observed between treatment groups. This observational study evaluated risk of AP among new users of canagliflozin compared with new users of six classes of other antihyperglycemic agents (AHAs).Methods: Three US claims databases were analyzed based on a prespecified protocol approved by the European Medicines Agency. Propensity score adjustment controlled for imbalances in baseline covariates. Cox regression models estimated the hazard ratio of AP with canagliflozin compared with other AHAs using on-treatment (primary) and intent-to-treat approaches. Sensitivity analyses assessed robustness of findings.Results: Across the three databases, there were between 12,023-80,986 new users of canagliflozin; the unadjusted incidence rates of AP (per 1000 person-years) were between 1.5-2.2 for canagliflozin and 1.1-6.6 for other AHAs. The risk of AP was generally similar for new users of canagliflozin compared with new users of glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, sulfonylureas, thiazolidinediones, insulin, and other AHAs, with no consistent between-treatment differences observed across databases. Intent-to-treat and sensitivity analysis findings were qualitatively consistent with on-treatment findings.Conclusions: In this large observational study, incidence rates of AP in patients with T2DM treated with canagliflozin or other AHAs were generally similar, with no evidence suggesting that canagliflozin is associated with increased risk of AP compared with other AHAs.

10.
J Am Med Inform Assoc ; 27(3): 376-385, 2020 03 01.
Artículo en Inglés | MEDLINE | ID: mdl-31816040

RESUMEN

OBJECTIVES: We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites. MATERIALS AND METHODS: ODAL effectively utilizes the information from the local site (where the patient-level data are accessible) and incorporates the first-order (ODAL1) and second-order (ODAL2) gradients of the likelihood function from other sites to construct an estimator without requiring iterative communication across sites or transferring patient-level data. We evaluated ODAL via extensive simulation studies and an application to a dataset from the University of Pennsylvania Health System. The estimation accuracy was evaluated by comparing it with the estimator based on the combined individual participant data or pooled data (ie, gold standard). RESULTS: Our simulation studies revealed that the relative estimation bias of ODAL1 compared with the pooled estimates was <3%, and the ratio of standard errors was <1.25 for all scenarios. ODAL2 achieved higher accuracy (with relative bias <0.1% and ratio of standard errors <1.05). In real data analysis, we investigated the associations of 100 medications with fetal loss during pregnancy. We found that ODAL1 provided estimates with relative bias <10% for 85% of medications, and ODAL2 has relative bias <10% for 99% of medications. For communication cost, ODAL1 requires transferring p numbers from each site to the local site and ODAL2 requires transferring (p×p+p) numbers from each site to the local site, where p is the number of parameters in the regression model. CONCLUSIONS: This study demonstrates that ODAL is privacy-preserving and communication-efficient with small bias and high statistical efficiency.

12.
Health Technol Assess ; 23(60): 1-88, 2019 10.
Artículo en Inglés | MEDLINE | ID: mdl-31661431

RESUMEN

BACKGROUND: The randomised controlled trial is widely considered to be the gold standard study for comparing the effectiveness of health interventions. Central to its design is a calculation of the number of participants needed (the sample size) for the trial. The sample size is typically calculated by specifying the magnitude of the difference in the primary outcome between the intervention effects for the population of interest. This difference is called the 'target difference' and should be appropriate for the principal estimand of interest and determined by the primary aim of the study. The target difference between treatments should be considered realistic and/or important by one or more key stakeholder groups. OBJECTIVE: The objective of the report is to provide practical help on the choice of target difference used in the sample size calculation for a randomised controlled trial for researchers and funder representatives. METHODS: The Difference ELicitation in TriAls2 (DELTA2) recommendations and advice were developed through a five-stage process, which included two literature reviews of existing funder guidance and recent methodological literature; a Delphi process to engage with a wider group of stakeholders; a 2-day workshop; and finalising the core document. RESULTS: Advice is provided for definitive trials (Phase III/IV studies). Methods for choosing the target difference are reviewed. To aid those new to the topic, and to encourage better practice, 10 recommendations are made regarding choosing the target difference and undertaking a sample size calculation. Recommended reporting items for trial proposal, protocols and results papers under the conventional approach are also provided. Case studies reflecting different trial designs and covering different conditions are provided. Alternative trial designs and methods for choosing the sample size are also briefly considered. CONCLUSIONS: Choosing an appropriate sample size is crucial if a study is to inform clinical practice. The number of patients recruited into the trial needs to be sufficient to answer the objectives; however, the number should not be higher than necessary to avoid unnecessary burden on patients and wasting precious resources. The choice of the target difference is a key part of this process under the conventional approach to sample size calculations. This document provides advice and recommendations to improve practice and reporting regarding this aspect of trial design. Future work could extend the work to address other less common approaches to the sample size calculations, particularly in terms of appropriate reporting items. FUNDING: Funded by the Medical Research Council (MRC) UK and the National Institute for Health Research as part of the MRC-National Institute for Health Research Methodology Research programme.


Asunto(s)
Ensayos Clínicos Controlados Aleatorios como Asunto , Tamaño de la Muestra , Investigación Biomédica , Ensayos Clínicos Fase III como Asunto , Ensayos Clínicos Fase IV como Asunto , Técnica Delfos , Educación , Humanos
15.
Pharmacoepidemiol Drug Saf ; 28(12): 1620-1628, 2019 12.
Artículo en Inglés | MEDLINE | ID: mdl-31456304

RESUMEN

PURPOSE: To compare the incidence of diabetic ketoacidosis (DKA) among patients with type 2 diabetes mellitus (T2DM) who were new users of sodium glucose co-transporter 2 inhibitors (SGLT2i) versus other classes of antihyperglycemic agents (AHAs). METHODS: Patients were identified from four large US claims databases using broad (all T2DM patients) and narrow (intended to exclude patients with type 1 diabetes or secondary diabetes misclassified as T2DM) definitions of T2DM. New users of SGLT2i and seven groups of comparator AHAs were matched (1:1) on exposure propensity scores to adjust for imbalances in baseline covariates. Cox proportional hazards regression models, conditioned on propensity score-matched pairs, were used to estimate hazard ratios (HRs) of DKA for new users of SGLT2i versus other AHAs. When I2 <40%, a combined HR across the four databases was estimated. RESULTS: Using the broad definition of T2DM, new users of SGLT2i had an increased risk of DKA versus sulfonylureas (HR [95% CI]: 1.53 [1.31-1.79]), DPP-4i (1.28 [1.11-1.47]), GLP-1 receptor agonists (1.34 [1.12-1.60]), metformin (1.31 [1.11-1.54]), and insulinotropic AHAs (1.38 [1.15-1.66]). Using the narrow definition of T2DM, new users of SGLT2i had an increased risk of DKA versus sulfonylureas (1.43 [1.01-2.01]). New users of SGLT2i had a lower risk of DKA versus insulin and a similar risk as thiazolidinediones, regardless of T2DM definition. CONCLUSIONS: Increased risk of DKA was observed for new users of SGLT2i versus several non-SGLT2i AHAs when T2DM was defined broadly. When T2DM was defined narrowly to exclude possible misclassified patients, an increased risk of DKA with SGLT2i was observed compared with sulfonylureas.


Asunto(s)
Diabetes Mellitus Tipo 2/tratamiento farmacológico , Cetoacidosis Diabética/epidemiología , Inhibidores del Cotransportador de Sodio-Glucosa 2/efectos adversos , Reclamos Administrativos en el Cuidado de la Salud/estadística & datos numéricos , Anciano , Glucemia , Bases de Datos Factuales/estadística & datos numéricos , Cetoacidosis Diabética/inducido químicamente , Femenino , Receptor del Péptido 1 Similar al Glucagón/antagonistas & inhibidores , Humanos , Incidencia , Insulina/efectos adversos , Masculino , Metformina/efectos adversos , Persona de Mediana Edad , Factores de Riesgo , Compuestos de Sulfonilurea/efectos adversos , Estados Unidos/epidemiología
16.
J Clin Epidemiol ; 115: 77-89, 2019 11.
Artículo en Inglés | MEDLINE | ID: mdl-31302205

RESUMEN

OBJECTIVES: Data Abstraction Assistant (DAA) is a software for linking items abstracted into a data collection form for a systematic review to their locations in a study report. We conducted a randomized cross-over trial that compared DAA-facilitated single-data abstraction plus verification ("DAA verification"), single data abstraction plus verification ("regular verification"), and independent dual data abstraction plus adjudication ("independent abstraction"). STUDY DESIGN AND SETTING: This study is an online randomized cross-over trial with 26 pairs of data abstractors. Each pair abstracted data from six articles, two per approach. Outcomes were the proportion of errors and time taken. RESULTS: Overall proportion of errors was 17% for DAA verification, 16% for regular verification, and 15% for independent abstraction. DAA verification was associated with higher odds of errors when compared with regular verification (adjusted odds ratio [OR] = 1.08; 95% confidence interval [CI]: 0.99-1.17) or independent abstraction (adjusted OR = 1.12; 95% CI: 1.03-1.22). For each article, DAA verification took 20 minutes (95% CI: 1-40) longer than regular verification, but 46 minutes (95% CI: 26 to 66) shorter than independent abstraction. CONCLUSION: Independent abstraction may only be necessary for complex data items. DAA provides an audit trail that is crucial for reproducible research.


Asunto(s)
Indización y Redacción de Resúmenes/métodos , Revisiones Sistemáticas como Asunto , Estudios Cruzados , Recolección de Datos , Humanos , Oportunidad Relativa , Distribución Aleatoria , Programas Informáticos , Adulto Joven
18.
Ann Intern Med ; 170(8): 571-572, 2019 04 16.
Artículo en Inglés | MEDLINE | ID: mdl-30909296
20.
Sci Data ; 5: 180268, 2018 11 27.
Artículo en Inglés | MEDLINE | ID: mdl-30480665

RESUMEN

The Yale University Open Data Access (YODA) Project has facilitated access to clinical trial data since 2013. The purpose of this article is to provide an overview of the Project, describe key decisions that were made when establishing data sharing policies, and suggest how our experience and the experiences of our first two data generator partners, Medtronic, Inc. and Johnson & Johnson, can be used to enhance other ongoing or future initiatives.


Asunto(s)
Ensayos Clínicos como Asunto , Difusión de la Información/métodos , Humanos
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