Daniel, Rhian

Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways.

Burgess, S., Daniel, R. M., Butterworth, A. S., Thompson, S. G., & the EPIC-InterAct Consortium (2015) · International Journal of Epidemiology

Advanced Causal Inference TechniquesGenetic Associations and Epidemiology

Cited by 543

Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets

Daniel, R., Zhang, J., & Farewell, D. (2021) · Biometrical Journal

Advanced Causal Inference TechniquesStatistical Methods in Clinical Trials

Cited by 167

Using causal diagrams to guide analysis in missing data problems

Daniel, R. M., Kenward, M. G., Cousens, S. N., & De Stavola, B. L. (2012) · Statistical Methods in Medical Research

Advanced Causal Inference TechniquesStatistical Methods and Bayesian Inference

Cited by 155

A comparison of methods to adjust for continuous covariates in the analysis of randomised trials

Kahan, B. C., Rushton, H., Morris, T. P., & Daniel, R. M. (2016) · BMC Medical Research Methodology

Statistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

Cited by 74

The impact of a cash transfer programme on tuberculosis treatment success rate: a quasi-experimental study in Brazil

J Carter, D., Daniel, R., Torrens, A. W., N Sanchez, M., Maciel, E. L. N., Bartholomay, P., Barreira, D. C., Rasella, D., Barreto, M. L., Rodrigues, L. C., et al. (2019) · BMJ Global Health

Tuberculosis Research and EpidemiologyPoverty, Education, and Child Welfare

Cited by 69

Analysis of tuberculosis prevalence surveys: new guidance on best-practice methods

Floyd, S., Sismanidis, C., Yamada, N., Daniel, R., Lagahid, J., Mecatti, F., Vianzon, R., Bloss, E., Tiemersma, E., Onozaki, I., et al. (2013) · Emerging Themes in Epidemiology

Tuberculosis Research and EpidemiologyStatistical Methods in Epidemiology

Cited by 44