Sensitivity Analysis in Observational Studies

2014 ◽  
Author(s):  
Paul R. Rosenbaum
Keyword(s):  
Sensitivity Analysis ◽  
Observational Studies

scholarly journals Causal Association Between Heart Failure and Alzheimer’s Disease: A Two-Sample Bidirectional Mendelian Randomization Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenglin Duan ◽  
Jingjing Shi ◽  
Guozhen Yuan ◽  
Xintian Shou ◽  
Ting Chen ◽  
...  
Keyword(s):  
Heart Failure ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sensitivity Analysis ◽  
Causal Relationship ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Relationships ◽  
Simple Mode

Background: Traditional observational studies have demonstrated an association between heart failure and Alzheimer’s disease. The strengths of observational studies lie in their speed of implementation, cost, and applicability to rare diseases. However, observational studies have several limitations, such as uncontrollable confounders. Therefore, we employed Mendelian randomization of genetic variants to evaluate the causal relationships existing between AD and HF, which can avoid these limitations.Materials and Methods: A two-sample bidirectional MR analysis was employed. All datasets were results from the UK’s Medical Research Council Integrative Epidemiology Unit genome-wide association study database, and we conducted a series of control steps to select the most suitable single-nucleotide polymorphisms for MR analysis, for which five primary methods are offered. We reversed the functions of exposure and outcomes to explore the causal direction of HF and AD. Sensitivity analysis was used to conduct several tests to avoid heterogeneity and pleiotropic bias in the MR results.Results: Our MR studies did not support a meaningful causal relationship between AD on HF (MR-Egger, p = 0.634 > 0.05; weighted median (WM), p = 0.337 > 0.05; inverse variance weighted (IVW), p = 0.471 > 0.05; simple mode, p = 0.454 > 0.05; weighted mode, p = 0.401 > 0.05). At the same time, we did not find a significant causal relationship between HF and AD with four of the methods (MR-Egger, p = 0.195 > 0.05; IVW, p = 0.0879 > 0.05; simple mode, p = 0.170 > 0.05; weighted mode, p = 0.110 > 0.05), but the WM method indicated a significant effect of HF on AD (p = 0.025 < 0.05). Because the statistical powers of IVW and MR-Egger are more than that of WM, we think that there is no causal effect of HF on AD. Sensitivity analysis and horizontal pleiotropy were not detected in the MR analysis.Conclusion: Our results did not provide significant evidence indicating any causal relationships between HF and AD in the European population. Therefore, more large-scale datasets or datasets related to similar factors are expected for further MR analysis.

scholarly journals Targeted therapies for previously treated advanced or metastatic renal cell carcinoma: systematic review and network meta-analysis

BMJ Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. e024691 ◽  
Author(s):  
Charlotta Karner ◽  
Kayleigh Kew ◽  
Victoria Wakefield ◽  
Natalie Masento ◽  
Steven J Edwards
Keyword(s):  
Systematic Review ◽  
Renal Cell Carcinoma ◽  
Sensitivity Analysis ◽  
Adverse Events ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Observational Studies ◽  
Meta Analysis ◽  
Targeted Treatment

ObjectiveTo compare the effectiveness and safety of treatments for advanced or metastatic renal cell carcinoma (amRCC) after treatment with vascular endothelial growth factor (VEGF)-targeted treatment.DesignSystematic review and network meta-analysis of randomised controlled trials (RCTs) and comparative observational studies. MEDLINE, EMBASE and Cochrane Library were searched up to January 2018.ParticipantsPeople with amRCC requiring treatment after VEGF-targeted treatment.InterventionsAxitinib, cabozantinib, everolimus, lenvatinib with everolimus, nivolumab, sorafenib and best supportive care (BSC).OutcomesPrimary outcomes were overall survival (OS) and progression-free survival (PFS); secondary outcomes were objective response rate (ORR), adverse events, and health-related quality of life (HRQoL).ResultsTwelve studies were included (n=5144): five RCTs and seven observational studies. Lenvatinib with everolimus significantly increased OS and PFS over everolimus (HR 0.61, 95% Credible Interval [95%CrI]: 0.36 to 0.96 and 0.47, 95%CrI: 0.26 to 0.77, respectively) as did cabozantinib (HR 0.66, 95%CrI: 0.53 to 0.82 and 0.51, 95%CrI: 0.41 to 0.63, respectively). This remained the case when observational evidence was included. Nivolumab also significantly improved OS versus everolimus (HR 0.74, 95%CrI: 0.57 to 0.93). OS sensitivity analysis, including observational studies, indicates everolimus being more effective than axitinib and sorafenib. However, inconsistency was identified in the OS sensitivity analysis. PFS sensitivity analysis suggests axitinib is more effective than everolimus, which may be more effective than sorafenib. The results for ORR supported the OS and PFS analyses. Nivolumab is associated with fewer grade 3 or grade 4 adverse events than lenvatinib with everolimus or cabozantinib. HRQoL could not be analysed due to differences in tools used.ConclusionsLenvatinib with everolimus, cabozantinib and nivolumab are effective in prolonging the survival for people with amRCC subsequent to VEGF-targeted treatment, but there is considerable uncertainty about how they compare to each other and how much better they are than axitinib and sorafenib.PROSPERO registrationnumberCRD42017071540.

Sensitivity Analysis for Unmeasured Confounding: E-Values for Observational Studies

2017 ◽  
Vol 167 (4) ◽  
pp. 285 ◽  
Author(s):  
A. Russell Localio ◽  
Catherine B. Stack ◽  
Michael E. Griswold
Keyword(s):  
Sensitivity Analysis ◽  
Observational Studies ◽  
Unmeasured Confounding

scholarly journals Simultaneous sensitivity analysis for observational studies using full matching or matching with multiple controls

2009 ◽  
Vol 2 (2) ◽  
pp. 203-211 ◽  
Author(s):  
Joseph L. Gastwirth ◽  
Abba M. Krieger ◽  
Paul R. Rosenbaum ◽  
Dylan Small
Keyword(s):  
Sensitivity Analysis ◽  
Observational Studies ◽  
Multiple Controls

Modern Algorithms for Matching in Observational Studies

2020 ◽  
Vol 7 (1) ◽  
pp. 143-176 ◽  
Author(s):  
Paul R. Rosenbaum
Keyword(s):  
Sensitivity Analysis ◽  
Propensity Score ◽  
Observational Studies ◽  
Propensity Scores ◽  
Variable Number ◽  
Exact Matching ◽  
Covariate Balance ◽  
Software Packages ◽  
Effective Use ◽  
Entire Number

Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related methods such as near-fine and refined balance, exact and near-exact matching, tactics addressing missing covariate values, the entire number, and checks of covariate balance. Matching structures are described, such as matching with a variable number of controls, full matching, subset matching and risk-set matching. Software packages in R are described. A brief review is given of the theory underlying propensity scores and the associated sensitivity analysis concerning an unobserved covariate omitted from the propensity score.

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