Likelihood-based confidence intervals for the risk ratio using double sampling with over-reported binary data

2011 ◽  
Vol 55 (1) ◽  
pp. 813-823 ◽  
Author(s):  
Dewi Rahardja ◽  
Dean M. Young
Keyword(s):  
Confidence Intervals ◽  
Risk Ratio ◽  
Binary Data ◽  
Double Sampling

Obtaining Confidence Intervals for the Risk Ratio in Cohort Studies

Biometrics ◽  
1978 ◽  
Vol 34 (3) ◽  
pp. 469 ◽  
Author(s):  
D. Katz ◽  
J. Baptista ◽  
S. P. Azen ◽  
M. C. Pike
Keyword(s):  
Cohort Studies ◽  
Confidence Intervals ◽  
Risk Ratio

scholarly journals Privacy-protecting estimation of adjusted risk ratios using modified Poisson regression in multi-center studies

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Di Shu ◽  
Jessica G. Young ◽  
Sengwee Toh
Keyword(s):  
Data Analysis ◽  
Confidence Intervals ◽  
Risk Ratio ◽  
Poisson Regression ◽  
Binary Outcomes ◽  
Real World Data ◽  
Individual Level ◽  
Adjusted Risk ◽  
Level Data ◽  
Risk Ratios

Abstract Background Multi-center studies can generate robust and generalizable evidence, but privacy considerations and legal restrictions often make it challenging or impossible to pool individual-level data across data-contributing sites. With binary outcomes, privacy-protecting distributed algorithms to conduct logistic regression analyses have been developed. However, the risk ratio often provides a more transparent interpretation of the exposure-outcome association than the odds ratio. Modified Poisson regression has been proposed to directly estimate adjusted risk ratios and produce confidence intervals with the correct nominal coverage when individual-level data are available. There are currently no distributed regression algorithms to estimate adjusted risk ratios while avoiding pooling of individual-level data in multi-center studies. Methods By leveraging the Newton-Raphson procedure, we adapted the modified Poisson regression method to estimate multivariable-adjusted risk ratios using only summary-level information in multi-center studies. We developed and tested the proposed method using both simulated and real-world data examples. We compared its results with the results from the corresponding pooled individual-level data analysis. Results Our proposed method produced the same adjusted risk ratio estimates and standard errors as the corresponding pooled individual-level data analysis without pooling individual-level data across data-contributing sites. Conclusions We developed and validated a distributed modified Poisson regression algorithm for valid and privacy-protecting estimation of adjusted risk ratios and confidence intervals in multi-center studies. This method allows computation of a more interpretable measure of association for binary outcomes, along with valid construction of confidence intervals, without sharing of individual-level data.

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