Efficient Risk Set Sampling when a Time-dependent Exposure Is Present

2009 ◽  
Vol 48 (05) ◽  
pp. 438-443 ◽  
Author(s):  
J. Beyersmann ◽  
P. Gastmeier ◽  
M. Schumacher ◽  
M. Wolkewitz
Keyword(s):  
Simulation Study ◽  
Sampling Methods ◽  
Real Data ◽  
Outcome Data ◽  
Time Dependent ◽  
Study Endpoint ◽  
Time Dependency ◽  
Full Cohort ◽  
Covariate Information ◽  
The Impact

Summary Objectives: The impact of time-dependent exposures on the time until study endpoint may correctly be analyzed with data of a full cohort. Ignoring the time-dependent nature of these exposures leads to time-dependent bias. Matching for time to exposure is often applied to take the time-dependency into account, but prefixed sets of exposed and unexposed may still create bias. This approach is attractive since a subcohort would also save resources, especially when exposure and outcome data are only available in the full cohort but further covariate information is required. The first objective is to show to which extent matching for time to exposure yields biased results. Secondly, exposure density sampling is introduced and explored. Methods: To evaluate how both sampling methods perform, they are compared to the correct method as well as to the approach in which the time-dependent nature of the exposure is ignored. Real data of the SIR-3 study (Germany, 2000–2001) and a simulation study are used. Results: Simulations show that matching may reduce the time-dependent bias but still there is a bias. The matching bias decreases if fewer patients are exposed. Exposure density sampling yields unbiased results. Conclusions: Results from studies in which matching for time to exposure was applied are only tolerable for rare exposures. Whenever subcohorting is the intention in order to save resources, exposure density sampling should be preferred instead.

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

Inverse sampling regression for pooled data

2015 ◽  
Vol 26 (3) ◽  
pp. 1093-1109 ◽  
Author(s):  
Osval A Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Kent Eskridge ◽  
José Crossa
Keyword(s):  
Simulation Study ◽  
Low Cost ◽  
Group Testing ◽  
Real Data ◽  
Parameter Estimates ◽  
Inverse Sampling ◽  
Mixed Effect ◽  
Individual Level ◽  
Covariate Information ◽  
The Individual

Because pools are tested instead of individuals in group testing, this technique is helpful for estimating prevalence in a population or for classifying a large number of individuals into two groups at a low cost. For this reason, group testing is a well-known means of saving costs and producing precise estimates. In this paper, we developed a mixed-effect group testing regression that is useful when the data-collecting process is performed using inverse sampling. This model allows including covariate information at the individual level to incorporate heterogeneity among individuals and identify which covariates are associated with positive individuals. We present an approach to fit this model using maximum likelihood and we performed a simulation study to evaluate the quality of the estimates. Based on the simulation study, we found that the proposed regression method for inverse sampling with group testing produces parameter estimates with low bias when the pre-specified number of positive pools (r) to stop the sampling process is at least 10 and the number of clusters in the sample is also at least 10. We performed an application with real data and we provide an NLMIXED code that researchers can use to implement this method.

scholarly journals Compound Bias due to Measurement Error When Comparing Regression Coefficients

2019 ◽  
Vol 80 (3) ◽  
pp. 548-577
Author(s):  
William M. Murrah
Keyword(s):  
Measurement Error ◽  
Multiple Regression ◽  
Simulation Study ◽  
Type I Error ◽  
Real Data ◽  
Error Rates ◽  
Regression Coefficients ◽  
Type I ◽  
Type I Error Rates ◽  
The Impact

Multiple regression is often used to compare the importance of two or more predictors. When the predictors being compared are measured with error, the estimated coefficients can be biased and Type I error rates can be inflated. This study explores the impact of measurement error on comparing predictors when one is measured with error, followed by a simulation study to help quantify the bias and Type I error rates for common research situations. Two methods used to adjust for measurement error are demonstrated using a real data example. This study adds to the literature documenting the impact of measurement error on regression modeling, identifying issues particular to the use of multiple regression for comparing predictors, and offers recommendations for researchers conducting such studies.

scholarly journals The impact of missing data on analyses of a time-dependent exposure in a longitudinal cohort: a simulation study

2013 ◽  
Vol 10 (1) ◽  
Author(s):  
Amalia Karahalios ◽  
Laura Baglietto ◽  
Katherine J Lee ◽  
Dallas R English ◽  
John B Carlin ◽  
...  
Keyword(s):  
Missing Data ◽  
Simulation Study ◽  
Time Dependent ◽  
Longitudinal Cohort ◽  
The Impact

scholarly journals Evaluation of Four Multiple Imputation Methods for Handling Missing Binary Outcome Data in the Presence of an Interaction between a Dummy and a Continuous Variable

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sara Javadi ◽  
Abbas Bahrampour ◽  
Mohammad Mehdi Saber ◽  
Behshid Garrusi ◽  
Mohammad Reza Baneshi
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Simulation Study ◽  
Recursive Partitioning ◽  
Real Data ◽  
Continuous Variable ◽  
Outcome Data ◽  
Classification And Regression Tree ◽  
Parameter Estimates ◽  
Imputation Model

Multiple imputation by chained equations (MICE) is the most common method for imputing missing data. In the MICE algorithm, imputation can be performed using a variety of parametric and nonparametric methods. The default setting in the implementation of MICE is for imputation models to include variables as linear terms only with no interactions, but omission of interaction terms may lead to biased results. It is investigated, using simulated and real datasets, whether recursive partitioning creates appropriate variability between imputations and unbiased parameter estimates with appropriate confidence intervals. We compared four multiple imputation (MI) methods on a real and a simulated dataset. MI methods included using predictive mean matching with an interaction term in the imputation model in MICE (MICE-interaction), classification and regression tree (CART) for specifying the imputation model in MICE (MICE-CART), the implementation of random forest (RF) in MICE (MICE-RF), and MICE-Stratified method. We first selected secondary data and devised an experimental design that consisted of 40 scenarios (2 × 5 × 4), which differed by the rate of simulated missing data (10%, 20%, 30%, 40%, and 50%), the missing mechanism (MAR and MCAR), and imputation method (MICE-Interaction, MICE-CART, MICE-RF, and MICE-Stratified). First, we randomly drew 700 observations with replacement 300 times, and then the missing data were created. The evaluation was based on raw bias (RB) as well as five other measurements that were averaged over the repetitions. Next, in a simulation study, we generated data 1000 times with a sample size of 700. Then, we created missing data for each dataset once. For all scenarios, the same criteria were used as for real data to evaluate the performance of methods in the simulation study. It is concluded that, when there is an interaction effect between a dummy and a continuous predictor, substantial gains are possible by using recursive partitioning for imputation compared to parametric methods, and also, the MICE-Interaction method is always more efficient and convenient to preserve interaction effects than the other methods.

scholarly journals COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

Background: Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. Objective: This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. Methods: The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. Results: Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. Conclusions: We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent Reproduction Numbers (Rt), as well as we demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

The impact of statin therapy after surgical or endovascular treatment of cerebral aneurysms

2020 ◽  
Vol 133 (1) ◽  
pp. 182-189
Author(s):  
Tae-Jin Song ◽  
Seung-Hun Oh ◽  
Jinkwon Kim
Keyword(s):  
Statin Therapy ◽  
Coil Embolization ◽  
Primary Outcome ◽  
Cox Regression ◽  
Cerebral Aneurysms ◽  
Time Dependent ◽  
Lipid Lowering ◽  
Surgical Clipping ◽  
The Impact

OBJECTIVECerebral aneurysms represent the most common cause of spontaneous subarachnoid hemorrhage. Statins are lipid-lowering agents that may expert multiple pleiotropic vascular protective effects. The authors hypothesized that statin therapy after coil embolization or surgical clipping of cerebral aneurysms might improve clinical outcomes.METHODSThis was a retrospective cohort study using the National Health Insurance Service–National Sample Cohort Database in Korea. Patients who underwent coil embolization or surgical clipping for cerebral aneurysm between 2002 and 2013 were included. Based on prescription claims, the authors calculated the proportion of days covered (PDC) by statins during follow-up as a marker of statin therapy. The primary outcome was a composite of the development of stroke, myocardial infarction, and all-cause death. Multivariate time-dependent Cox regression analyses were performed.RESULTSA total of 1381 patients who underwent coil embolization (n = 542) or surgical clipping (n = 839) of cerebral aneurysms were included in this study. During the mean (± SD) follow-up period of 3.83 ± 3.35 years, 335 (24.3%) patients experienced the primary outcome. Adjustments were performed for sex, age (as a continuous variable), treatment modality, aneurysm rupture status (ruptured or unruptured aneurysm), hypertension, diabetes mellitus, household income level, and prior history of ischemic stroke or intracerebral hemorrhage as time-independent variables and statin therapy during follow-up as a time-dependent variable. Consistent statin therapy (PDC > 80%) was significantly associated with a lower risk of the primary outcome (adjusted hazard ratio 0.34, 95% CI 0.14–0.85).CONCLUSIONSConsistent statin therapy was significantly associated with better prognosis after coil embolization or surgical clipping of cerebral aneurysms.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

scholarly journals Cost-effectiveness of new MDR-TB regimens: study protocol for the TB-PRACTECAL economic evaluation substudy

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e036599
Author(s):  
Sedona Sweeney ◽  
Gabriela Gomez ◽  
Nichola Kitson ◽  
Animesh Sinha ◽  
Natalia Yatskevich ◽  
...  
Keyword(s):  
Economic Evaluation ◽  
Cost Effectiveness ◽  
Best Practice ◽  
Current Treatment ◽  
Outcome Data ◽  
London School ◽  
Main Trial ◽  
Ethical Approval ◽  
Mdr Tb ◽  
The Impact

IntroductionCurrent treatment regimens for multidrug-resistant tuberculosis (MDR-TB) are long, poorly tolerated and have poor outcomes. Furthermore, the costs of treating MDR-TB are much greater than those for treating drug-susceptible TB, both for health service and patient-incurred costs. Urgent action is needed to identify short, effective, tolerable and cheaper treatments for people with both quinolone-susceptible and quinolone-resistant MDR-TB. We present the protocol for an economic evaluation (PRACTECAL-EE substudy) alongside an ongoing clinical trial (TB-PRACTECAL) aiming to assess the costs to patients and providers of new regimens, as well as their cost-effectiveness and impact on participant poverty levels. This substudy is based on data from the three countries participating in the main trial.Methods and analysisPrimary cost data will be collected from the provider and patient perspectives, following economic best practice. We will estimate the probability that new MDR-TB regimens containing bedaquiline, pretomanid and linezolid are cost-effective from a societal perspective as compared with the standard of care for MDR-TB patients in Uzbekistan, South Africa and Belarus. Analysis uses a Markov model populated with primary cost and outcome data collected at each study site. We will also estimate the impact of new regimens on prevalence of catastrophic patient costs due to TB.Ethics and disseminationEthical approval has been obtained from the London School of Hygiene & Tropical Medicine and Médecins Sans Frontières. Local ethical approval will be sought in each study site. The results of the economic evaluation will be shared with the country health authorities and published in a peer-reviewed journal.Trial registration numberClinicalTrials.gov Registry (NCT04207112); Pre-results.

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