scholarly journals Multiple-Imputation Variance Estimation in Studies With Missing or Misclassified Inclusion Criteria

2020 ◽  
Vol 189 (12) ◽  
pp. 1628-1632
Author(s):  
Mark J Giganti ◽  
Bryan E Shepherd
Keyword(s):  
Multiple Imputation ◽  
Variance Estimation ◽  
Variance Estimator ◽  
Inclusion Criteria ◽  
Variance Estimators ◽  
Coverage Probabilities ◽  
Log Odds ◽  
Variance Estimate ◽  
High Level ◽  
Analysis Models

Abstract In observational studies using routinely collected data, a variable with a high level of missingness or misclassification may determine whether an observation is included in the analysis. In settings where inclusion criteria are assessed after imputation, the popular multiple-imputation variance estimator proposed by Rubin (“Rubin’s rules” (RR)) is biased due to incompatibility between imputation and analysis models. While alternative approaches exist, most analysts are not familiar with them. Using partially validated data from a human immunodeficiency virus cohort, we illustrate the calculation of an imputation variance estimator proposed by Robins and Wang (RW) in a scenario where the study exclusion criteria are based on a variable that must be imputed. In this motivating example, the corresponding imputation variance estimate for the log odds was 29% smaller using the RW estimator than using the RR estimator. We further compared these 2 variance estimators with a simulation study which showed that coverage probabilities of 95% confidence intervals based on the RR estimator were too high and became worse as more observations were imputed and more subjects were excluded from the analysis. The RW imputation variance estimator performed much better and should be employed when there is incompatibility between imputation and analysis models. We provide analysis code to aid future analysts in implementing this method.

scholarly journals Parametric bootstrap estimators for hybrid inference in forest inventories

2017 ◽  
Vol 91 (3) ◽  
pp. 354-365 ◽  
Author(s):  
Mathieu Fortin ◽  
Rubén Manso ◽  
Robert Schneider
Keyword(s):  
Multiple Imputation ◽  
Nonlinear Models ◽  
Parametric Bootstrap ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Resampling Methods ◽  
Forest Inventories ◽  
Point Estimator ◽  
Complex Models ◽  
High Level

Abstract In forestry, the variable of interest is not always directly available from forest inventories. Consequently, practitioners have to rely on models to obtain predictions of this variable of interest. This context leads to hybrid inference, which is based on both the probability design and the model. Unfortunately, the current analytical hybrid estimators for the variance of the point estimator are mainly based on linear or nonlinear models and their use is limited when the model reaches a high level of complexity. An alternative consists of using a variance estimator based on resampling methods (Rubin, D. B. (1987). Multiple imputation for nonresponse surveys. John Wiley & Sons, Hoboken, New Jersey, USA). However, it turns out that a parametric bootstrap (BS) estimator of the variance can be biased in contexts of hybrid inference. In this study, we designed and tested a corrected BS estimator for the variance of the point estimator, which can easily be implemented as long as all of the stochastic components of the model can be properly simulated. Like previous estimators, this corrected variance estimator also makes it possible to distinguish the contribution of the sampling and the model to the variance of the point estimator. The results of three simulation studies of increasing complexity showed no evidence of bias for this corrected variance estimator, which clearly outperformed the BS variance estimator used in previous studies. Since the implementation of this corrected variance estimator is not much more complicated, we recommend its use in contexts of hybrid inference based on complex models.

Variance estimation for volume when D2H is the covariate in regression

1984 ◽  
Vol 14 (6) ◽  
pp. 818-821 ◽  
Author(s):  
H. T. Schreuder ◽  
Jana Anderson
Keyword(s):  
Confidence Intervals ◽  
Loblolly Pine ◽  
Variance Estimation ◽  
Stat Assoc ◽  
Variance Estimators ◽  
Robust Variance ◽  
Variance Estimate

For two realistic (but not real) populations of loblolly pine, variance estimators for weighed regression estimates of total volume are compared. The traditional variance estimators and two robust variance estimators proposed by Royall and Cumberland (R. M. Royall and W. G. Cumberland. 1981. JASA, J. Am. Stat. Assoc. 76: 924–930) are found to be quite unreliable. In contrast, the jackknife variance estimate and confidence intervals based on it are found to be much more reliable.

scholarly journals A Note on the Effect of Data Clustering on the Multiple-Imputation Variance Estimator: A Theoretical Addendum to the Lewis et al. article in JOS 2014

2016 ◽  
Vol 32 (1) ◽  
pp. 147-164 ◽  
Author(s):  
Yulei He ◽  
Iris Shimizu ◽  
Susan Schappert ◽  
Jianmin Xu ◽  
Vladislav Beresovsky ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Variance Estimators ◽  
Design Effects ◽  
Study Results ◽  
Analytic Expressions ◽  
Theoretical Reasoning ◽  
Care Survey ◽  
Variance Estimate ◽  
Nationally Representative ◽  
The Impact

Abstract Multiple imputation is a popular approach to handling missing data. Although it was originally motivated by survey nonresponse problems, it has been readily applied to other data settings. However, its general behavior still remains unclear when applied to survey data with complex sample designs, including clustering. Recently, Lewis et al. (2014) compared single- and multiple-imputation analyses for certain incomplete variables in the 2008 National Ambulatory Medicare Care Survey, which has a nationally representative, multistage, and clustered sampling design. Their study results suggested that the increase of the variance estimate due to multiple imputation compared with single imputation largely disappears for estimates with large design effects. We complement their empirical research by providing some theoretical reasoning. We consider data sampled from an equally weighted, single-stage cluster design and characterize the process using a balanced, one-way normal random-effects model. Assuming that the missingness is completely at random, we derive analytic expressions for the within- and between-multiple-imputation variance estimators for the mean estimator, and thus conveniently reveal the impact of design effects on these variance estimators. We propose approximations for the fraction of missing information in clustered samples, extending previous results for simple random samples. We discuss some generalizations of this research and its practical implications for data release by statistical agencies.

Point-Poisson, point-pps, and modified point-pps sampling: efficiency and variance estimation

1992 ◽  
Vol 22 (8) ◽  
pp. 1071-1078 ◽  
Author(s):  
H.T. Schreuder ◽  
Z. Ouyang ◽  
M. Williams
Keyword(s):  
Variance Estimation ◽  
Regular Point ◽  
Regression Estimator ◽  
Variance Estimator ◽  
Poisson Sampling ◽  
Variance Estimators ◽  
Sample Tree ◽  
Fixed Sample ◽  
Regression Estimators ◽  
Generalized Regression Estimator

Modified point-pps (probability proportional to size) sampling selects at least one sample tree per point and yields a fixed sample size. Point-Poisson sampling is as efficient as this modified procedure but less efficient than regular point-pps sampling in a simulation study estimating total volume using either the Horvitz–Thompson (ŶHT) or the weighted regression estimator (Ŷwr). Point-pps sampling is somewhat more efficient than point-Poisson sampling for all estimators except ŶHT, and point-Poisson sampling is always somewhat more efficient than modified point-pps sampling across.all estimators. For board foot volume the regression estimators are more efficient than ŶHT for all three procedures. Point-pps sampling is always most efficient, except for ŶHT, and point-Poisson sampling is always more efficient than the modified point-pps procedure. We recommend using Ŷgr (generalized regression estimator), Ŷwr, or ŶHT for total volume and Ŷgr for board foot volume. Three variance estimators estimate the variances of the regression estimates with small bias; we recommend the simple bootstrap variance estimator because it is simple to compute and does as well as its two main competitors. It does well for ŶHT, too, for all three procedures and should be used for ŶHT in point-Ppisson sampling in preference to the Grosenbaugh variance approximation. An unbiased variance estimator is given for ŶHT with the modified point-pps procedure, but the simple bootstrap variance is equally good.

scholarly journals An Approximate Point-based Alternative for the Estimation of Variance under Big BAF Sampling

2020 ◽  
Author(s):  
Thomas B. Lynch ◽  
Jeffrey H Gove ◽  
Timothy G Gregoire ◽  
Mark J Ducey
Keyword(s):  
Forest Inventory ◽  
Variance Estimation ◽  
Basal Area ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Forest Types ◽  
Variance Estimators ◽  
Computer Simulation Studies ◽  
New Formula ◽  
Estimation Of Variance

Abstract BackgroundA new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including \dbh s and heights needed for volume estimation.MethodsThe new estimator is derived using the \Dm\ from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce's formula.ResultsSeveral computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas.ConclusionsA possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the \dbh-height relationship can be affected substantially by density perhaps through competition. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1/n where n is the number of sample points.

scholarly journals An approximate point-based alternative for the estimation of variance under big BAF sampling

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Thomas B. Lynch ◽  
Jeffrey H. Gove ◽  
Timothy G. Gregoire ◽  
Mark J. Ducey
Keyword(s):  
Forest Inventory ◽  
Variance Estimation ◽  
Basal Area ◽  
Volume Estimation ◽  
Delta Method ◽  
Sampling Point ◽  
Variance Estimator ◽  
Computer Simulation Studies ◽  
New Formula ◽  
Estimation Of Variance

Abstract Background A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes Bitterlich sampling (point sampling) with two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation. Methods The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula. Results Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas. Conclusions A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition. We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of $\frac {1}{n}$ 1 n where n is the number of sample points.

A note on design-based versus model-based variance estimation in stereology

2002 ◽  
Vol 34 (03) ◽  
pp. 484-490 ◽  
Author(s):  
Asger Hobolth ◽  
Eva B. Vedel Jensen
Keyword(s):  
Mathematical Model ◽  
Original Problem ◽  
Variance Estimation ◽  
Point Of View ◽  
Systematic Sampling ◽  
Variance Estimator ◽  
Shape Model ◽  
Model Based ◽  
Versus Model ◽  
Special Case

Recently, systematic sampling on the circle and the sphere has been studied by Gual-Arnau and Cruz-Orive (2000) from a design-based point of view. In this note, it is shown that their mathematical model for the covariogram is, in a model-based statistical setting, a special case of the p-order shape model suggested by Hobolth, Pedersen and Jensen (2000) and Hobolth, Kent and Dryden (2002) for planar objects without landmarks. Benefits of this observation include an alternative variance estimator, applicable in the original problem of systematic sampling. In a wider perspective, the paper contributes to the discussion concerning design-based versus model-based stereology.

scholarly journals The Association Between Dental Anxiety And Psychiatric Disorders And Symptoms: A Systematic Review

2018 ◽  
Vol 14 (1) ◽  
pp. 207-222 ◽  
Author(s):  
Harri Halonen ◽  
Jenna Nissinen ◽  
Heli Lehtiniemi ◽  
Tuula Salo ◽  
Pirkko Riipinen ◽  
...  
Keyword(s):  
Systematic Review ◽  
Psychiatric Disorders ◽  
Web Of Science ◽  
Dental Anxiety ◽  
Data Sources ◽  
Inclusion Criteria ◽  
Clinical Interviews ◽  
Specific Phobias ◽  
Axis I ◽  
High Level

Background:A growing amount of evidence suggests that dental anxiety is associated with other psychiatric disorders and symptoms. A systematic review was conducted to critically evaluate the studies of comorbidity of dental anxiety with other specific phobias and other Axis I psychiatric disorders.Objective:The aim of the review was to explore how dental anxiety is associated with other psychiatric disorders and to estimate the level of comorbid symptoms in dental anxiety patients.Methods:The review was conducted and reported in accordance with the MOOSE statement. Data sources included PubMed, PsycInfo, Web of Science and Scopus.Results:The search produced 631 hits, of which 16 unique records fulfilled the inclusion criteria. The number of eligible papers was low. Study populations were heterogeneous including 6,486 participants, and a total of 25 tests and in few cases clinical interviews were used in the evaluation processes. The results enhanced the idea about the comorbidity between dental anxiety and other psychiatric disorders. The effect was found strong in several studies.Conclusion:Patients with a high level of dental anxiety are more prone to have a high level of comorbid phobias, depression, mood disorders and other psychiatric disorders and symptoms.

Literature Review : Burnout pada Perawat Selama Pandemi COVID-19

2021 ◽  
Vol 1 ◽  
pp. 1300-1305
Author(s):  
Arti Ayuningtiyas ◽  
Benny Arief Sulistyanto
Keyword(s):  
Literature Review ◽  
Emotional Exhaustion ◽  
Mean Value ◽  
Moderate Level ◽  
Personal Accomplishment ◽  
Google Scholar ◽  
Inclusion Criteria ◽  
Nurse Burnout ◽  
The Mean ◽  
High Level

AbstractDuring the Covid-19 pandemic, nurses are at the forefront. Many nurses experience stress and fatigue due to increased workload. Stress and fatigue that is obtained at work, both physical and psychological fatigue, is known as Burnout. This study aimed to describe the incidence of Burnout experienced by nurses during the Covid 19 Pandemic. This research is a literature review. This study was looking for some articles from PubMed,Garuda, and Google Scholar, with keyword. There were 5 articles that matched the research inclusion criteria. Critical analysis of 5 articles used the JBI instrument. These articles used the mean calculation. In the Emotional Exhaustion category, the mean value was 22.75 and SD was 8.828. it meant that the burnout level in the Emotional Exhaustion category was at a moderate level. In the depersonalization category, the mean value was 7.54 with SD 4.248. it meant that the burnout level in the depersonalization category was at a moderate level. In the personal accomplishment category, the mean was 19.676 with SD of 6.7. it mean that the burnout level in this category was a high level. The nurses experience burnout during the Covid-19 pandemic. The Emotional exhaustion category is a moderate level, depersonalization is at a moderate level, and personal accomplishment is at a high level.Keywords: Nurse, Burnout, Covid-19 pandemic AbstrakDimasa pandemi Covid-19 perawat berada pada garda terdepan, banyak perawat mengalami stress dan kelelahan dikarenakan beban kerja meningkat. Stress dan Kelelahan yang didapat saat kerja baik itu kelelahan fisik maupun psikis dikenal dengan nama Burnout penelitian ini bertujuan untuk mendeskripsikan kejadian Burnout yang dialami Perawat selama Pandemi Covid 19. Penelitian ini adalah literatur review. Hasil pencarian artikel dari database online yaitu Pubmed,Garuda dan Google Scholar, dengan kata kunci di dapatkan 5 artikel yang sesuai dengan kriteria inklusi penelitian. Analisa telaah kritis terhadap 5 artikel menggunakan instrument JBI. Terdapat 5 Artikel yang menggunakan perhitungan mean di dapatkan hasil kategori Emotional Exhaustion nilai mean sebanyak 22.75 dan SD 8.828 dimana hasil menunjukkan level burnout pada level sedang. Kategori depersonalization dengan nilai mean 7.54 dengan SD 4.248 dimana hasil burnout pada level sedang. Kategori personal accomplishment hasil mean 19.676 dengan SD 6.7 dimana hasil burnout menunjukkan level tinggi. Dari 5 Artikel yang di telaah di dapatkan Perawat mengalami Burnout selama pandemic Covid-19, kategori Emotional Exhaustion berada pada level sedang, depersonalization berada pada level sedang, dan personal accomplishment pada level tinggi.Kata Kunci : Perawat, Burnout, pandemi Covid-19

Using POS Data for Price Promotions Evaluation

2011 ◽  
pp. 267-285
Author(s):  
Danijel Bratina ◽  
Armand Faganel
Keyword(s):  
Panel Data ◽  
Marketing Research ◽  
Sales Promotion ◽  
Communication Tool ◽  
Price Promotions ◽  
Obvious Effect ◽  
Consumer Panel ◽  
Pos Data ◽  
High Level ◽  
Analysis Models

Price promotions have been largely dealt with in the literature. Yet there are just a few generalizations made so far about this powerful marketing communication tool. The obvious effect, that all authors who have studied price promotions emphasize, is quantity increase during price promotions. Inference studies about the decomposition of the sales promotion bump do not converge to a generalization or a law, but end in radically different results. Most of these studies use consumer panel data, rich of demographical characteristics and consumers’ purchasing history. Companies that use such data, available from marketing research industry, usually complain that data is old and expensive. The authors start with literature review on price promotions in which they present existing models based on consumer panel data (Bell, et al., 1999; Mela, et al., 1998; Moriarty, 1985; Walters, 1991; Yeshin, 2006). Next they present existing POS analysis models and compare their findings to show the high level of heterogeneity among results. All existing models are based on powerful databases provided by professional research institutions (i.e. Nielsen or IRI) that usually cover the whole market for the analysed brand category geographically. The authors next apply existing models to find which best suits data available for Slovenian FMCG market. They show two models analysis – quantity (SCAN*PRO) and market share (MCI) and their power for explanatory and forecasting research using POS data. Having dealt with more than 30 brand categories within a wider research, they conclude that the models developed are usable for a fast decision making process within a company, but their exploratory power is still poor compared to panel data.

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