Literature DB >> 21150353

Adjusting for selection effects in epidemiologic studies: why sensitivity analysis is the only "solution".

Sara Geneletti, Alexina Mason, Nicky Best.   

Abstract

Mesh：

Year:  2011        PMID： 21150353     DOI： 10.1097/EDE.0b013e3182003276

Source DB:  PubMed          Journal:  Epidemiology        ISSN： 1044-3983            Impact factor:   4.822


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  12 in total

1.  On the assumption of bivariate normality in selection models: a Copula approach applied to estimating HIV prevalence.

Authors:  Mark E McGovern; Till Bärnighausen; Giampiero Marra; Rosalba Radice
Journal:  Epidemiology       Date:  2015-03       Impact factor: 4.822

2.  Interviewer identity as exclusion restriction in epidemiology.

Authors:  Till Bärnighausen; Jacob Bor; Speciosa Wandira-Kazibwe; David Canning
Journal:  Epidemiology       Date:  2011-05       Impact factor: 4.822

3.  Assessment of potential bias from non-participation in a dynamic clinical cohort of long-term childhood cancer survivors: results from the St. Jude Lifetime Cohort Study.

Authors:  Rohit P Ojha; S Cristina Oancea; Kirsten K Ness; Jennifer Q Lanctot; D Kumar Srivastava; Leslie L Robison; Melissa M Hudson; James G Gurney
Journal:  Pediatr Blood Cancer       Date:  2012-09-28       Impact factor: 3.167

4.  Implementation of Instrumental Variable Bounds for Data Missing Not at Random.

Authors:  Jessica R Marden; Linbo Wang; Eric J Tchetgen Tchetgen; Stefan Walter; M Maria Glymour; Kathleen E Wirth
Journal:  Epidemiology       Date:  2018-05       Impact factor: 4.822

5.  Are all biases missing data problems?

Authors:  Chanelle J Howe; Lauren E Cain; Joseph W Hogan
Journal:  Curr Epidemiol Rep       Date:  2015-07-12

6.  Selection bias modeling using observed data augmented with imputed record-level probabilities.

Authors:  Caroline A Thompson; Onyebuchi A Arah
Journal:  Ann Epidemiol       Date:  2014-08-12       Impact factor: 3.797

7.  Validation, replication, and sensitivity testing of Heckman-type selection models to adjust estimates of HIV prevalence.

Authors:  Samuel J Clark; Brian Houle
Journal:  PLoS One       Date:  2014-11-17       Impact factor: 3.240

8.  National HIV prevalence estimates for sub-Saharan Africa: controlling selection bias with Heckman-type selection models.

Authors:  Daniel R Hogan; Joshua A Salomon; David Canning; James K Hammitt; Alan M Zaslavsky; Till Bärnighausen
Journal:  Sex Transm Infect       Date:  2012-12       Impact factor: 3.519

9.  Do Differential Response Rates to Patient Surveys Between Organizations Lead to Unfair Performance Comparisons?: Evidence From the English Cancer Patient Experience Survey.

Authors:  Catherine L Saunders; Marc N Elliott; Georgios Lyratzopoulos; Gary A Abel
Journal:  Med Care       Date:  2016-01       Impact factor: 2.983

10.  Adjusting HIV prevalence estimates for non-participation: an application to demographic surveillance.

Authors:  Mark E McGovern; Giampiero Marra; Rosalba Radice; David Canning; Marie-Louise Newell; Till Bärnighausen
Journal:  J Int AIDS Soc       Date:  2015-11-26       Impact factor: 5.396
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