Performance of prior event rate ratio adjustment method in pharmacoepidemiology: a simulation study

2014 ◽  
Vol 24 (5) ◽  
pp. 468-477 ◽  
Author(s):  
Md Jamal Uddin ◽  
Rolf H. H. Groenwold ◽  
Tjeerd P. van Staa ◽  
Anthonius de Boer ◽  
Svetlana V. Belitser ◽  
...  
Keyword(s):  
Simulation Study ◽  
Rate Ratio ◽  
Event Rate ◽  
Adjustment Method ◽  
Prior Event

scholarly journals On the Causal Interpretation of Rate-Change Methods: The Prior Event Rate Ratio and Rate Difference

2020 ◽  
Vol 190 (1) ◽  
pp. 142-149
Author(s):  
Robertus van Aalst ◽  
Edward Thommes ◽  
Maarten Postma ◽  
Ayman Chit ◽  
Issa J Dahabreh
Keyword(s):  
Rate Ratio ◽  
Treatment Initiation ◽  
Event Rate ◽  
Rate Change ◽  
Causal Interpretation ◽  
Rate Difference ◽  
Study Results ◽  
Before And After ◽  
Prior Event ◽  
Additive Scale

Abstract A growing number of studies use data before and after treatment initiation in groups exposed to different treatment strategies to estimate “causal effects” using a ratio measure called the prior event rate ratio (PERR). Here, we offer a causal interpretation for PERR and its additive scale analog, the prior event rate difference (PERD). We show that causal interpretation of these measures requires untestable rate-change assumptions about the relationship between 1) the change of the counterfactual rate before and after treatment initiation in the treated group under hypothetical intervention to implement the control strategy; and 2) the change of the factual rate before and after treatment initiation in the control group. The rate-change assumption is on the multiplicative scale for PERR but on the additive scale for PERD; the 2 assumptions hold simultaneously under testable, but unlikely, conditions. Even if investigators can pick the most appropriate scale, the relevant rate-change assumption might not hold exactly, so we describe sensitivity analysis methods to examine how assumption violations of different magnitudes would affect study results. We illustrate the methods using data from a published study of proton pump inhibitors and pneumonia.

scholarly journals Prior event rate ratio adjustment produced estimates consistent with randomized trial: a diabetes case study

2020 ◽  
Vol 122 ◽  
pp. 78-86
Author(s):  
Lauren R. Rodgers ◽  
John M. Dennis ◽  
Beverley M. Shields ◽  
Luke Mounce ◽  
Ian Fisher ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Rate Ratio ◽  
Event Rate ◽  
Diabetes Case ◽  
Prior Event

scholarly journals Assessing the prior event rate ratio method via probabilistic bias analysis on a Bayesian network

2019 ◽  
Vol 39 (5) ◽  
pp. 639-659 ◽  
Author(s):  
Edward W. Thommes ◽  
Salaheddin M. Mahmud ◽  
Yinong Young‐Xu ◽  
Julia Thornton Snider ◽  
Robertus Aalst ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Rate Ratio ◽  
Event Rate ◽  
Ratio Method ◽  
Bias Analysis ◽  
Prior Event

scholarly journals Aspirin reduces cardiovascular events in patients with pneumonia: a prior event rate ratio analysis in a large primary care database

2020 ◽  
pp. 2002795
Author(s):  
Fergus Hamilton ◽  
David Arnold ◽  
William Henley ◽  
Rupert A. Payne
Keyword(s):  
Myocardial Infarction ◽  
Primary Care ◽  
Ischaemic Stroke ◽  
Primary Outcome ◽  
Event Rate ◽  
Ratio Analysis ◽  
Secondary Outcomes ◽  
Care Database ◽  
Prior Event ◽  
Reduced Risk

BackgroundIschaemic stroke and myocardial infarction (MI) are common after pneumonia and are associated with long-term mortality. Aspirin may attenuate this risk and should be explored as a therapeutic option.MethodsWe extracted all patients with pneumonia, aged over 50, from the Clinical Practice Research Datalink (CPRD), a large UK primary care database, from inception until January 2019. We then performed a prior event rate ratio analysis (PERR) with propensity score matching, an approach that allows for control of measured and unmeasured confounding, with aspirin usage as the exposure, and ischaemic events as the outcome. The primary outcome was the combined outcome of ischaemic stroke and myocardial infarction. Secondary outcomes were ischaemic stroke and myocardial infarction individually. Relevant confounders were included in the analysis (smoking, comorbidities, age, gender).Findings48 743 patients were eligible for matching. 8099 of these were aspirin users who were matched to 8099 non-users. Aspirin users had a reduced risk of the primary outcome (adjusted hazard ratio, HR 0.64; 95% confidence interval 0.52–0.79) in the PERR analysis. For both secondary outcomes, aspirin use was also associated with a reduced risk HR 0.46 (0.30–0.72) and HR 0.70 (0.55–0.91) for myocardial infarction and stroke respectively).InterpretationThis study provides supporting evidence that aspirin use is associated with reduced ischaemic events after pneumonia in a primary care setting. This drug may have a future clinical role in preventing this important complication.

Use of Personal Continuous Glucose Monitoring Device Is Associated With Reduced Risk of Hypoglycemia in a 16-Week Clinical Trial of People With Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion

2020 ◽  
pp. 193229682095766
Author(s):  
Morten Hasselstrøm Jensen ◽  
Peter Vestergaard ◽  
Irl B. Hirsch ◽  
Ole Hejlesen
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Rate Ratio ◽  
Continuous Subcutaneous Insulin Infusion ◽  
Insulin Infusion ◽  
Event Rate ◽  
Glucose Monitoring ◽  
Subcutaneous Insulin

Aims: Continuous glucose monitoring (CGM) has the potential to promote diabetes self-management at home with a better glycemic control as outcome. Investigation of the effect of CGM has typically been carried out based on randomized controlled trials with prespecified CGM devices on CGM-naïve participants. The aim of this study was to investigate the effect on glycemic control in people using their personal CGM before and during the trial. Materials and Methods: Data from the Onset 5 trial of 472 people with type 1 diabetes using either their personal CGM ( n = 117) or no CGM ( n = 355) and continuous subcutaneous insulin infusion in a 16-week treatment period were extracted. Change from baseline in glycated hemoglobin A1c (HbA1c), number of hypoglycemic episodes, and CGM metrics at the end of treatment were analyzed with analysis of variance repeated-measures models. Results: Use of personal CGM compared with no CGM was associated with a reduction in risk of documented symptomatic hypoglycemia (event rate ratio: 0.82; 95% CI: 0.69-0.97) and asymptomatic hypoglycemia (event rate ratio: 0.72; 95% CI: 0.53-0.97), reduced time spent in hypoglycemia ( P = .0070), and less glycemic variability ( P = .0043) without a statistically significant increase in HbA1c ( P = .2028). Conclusions: Results indicate that use of personal CGM compared with no CGM in a population of type 1 diabetes is associated with a safer glycemic control without a statistically significantly deteriorated effect on HbA1c, which adds to the evidence about the real-world use of CGM, where device type is not prespecified, and users are not CGM naïve.

scholarly journals Learning from local to global - an efficient distributed algorithm for modeling time-to-event data

2020 ◽  
Author(s):  
Rui Duan ◽  
Chongliang Luo ◽  
Martijn J. Schuemie ◽  
Jiayi Tong ◽  
Jason C. Liang ◽  
...  
Keyword(s):  
Simulation Study ◽  
Likelihood Function ◽  
Event Rate ◽  
Meta Analysis ◽  
Weighted Average ◽  
Relative Bias ◽  
Time To Event ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

ABSTRACTObjectivesWe developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multi-center Cox proportional hazard model (ODAC) without sharing patient-level information across sites.MethodsUsing 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 four datasets from the Observational Health Data Sciences and Informatics (OHDSI) network.ResultsOur 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 (i.e., the pooled estimator). The relative bias was less than 0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the metaanalysis estimator, which was obtained by the inverse variance weighted average of the sitespecific estimates, had substantial bias when the event rate is less than 5%, with the relative bias reaching 20% when the event rate is 1%. In the OHDSI network application, the ODAC estimates have a relative bias less than 5% for 15 out of 16 log hazard ratios; while the meta-analysis estimates had substantially higher bias than ODAC.ConclusionsODAC is a privacy-preserving and non-iterative 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.

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