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- An Introduction to Causal Inference by Pearl, Judea
- Meta-Analysis of Observational Studies with Unmeasured Confounders by McCandless, Lawrence C.
- Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study by Xiao, Yongling/ Abrahamowicz, Michal and Moodie, Erica E. M.
- Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach by Radice, Rosalba/ Ramsahai, Roland/ Grieve, Richard/ Kreif, Noemi/ Sadique, Zia and Sekhon, Jasjeet S.
- Special Issue on Causal Inference in Health Research by Moodie, Erica E. M./ Kaufman, Jay S. and Platt, Robert W.
Relationship between Derivatives of the Observed and Full Loglikelihoods and Application to Newton-Raphson Algorithm
Citation Information: The International Journal of Biostatistics. Volume 2, Issue 1, Pages –, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1010, March 2006
- Published Online:
In the case of incomplete data we give general relationships between the first and second derivatives of the loglikelihood relative to the full and the incomplete observation set-ups. In the case where these quantities are easy to compute for the full observation set-up we propose to compute their analogue for the incomplete observation set-up using the above mentioned relationships: this involves numerical integrations. Once we are able to compute these quantities, Newton-Raphson type algorithms can be applied to find the maximum likelihood estimators, together with estimates of their variances. We detail the application of this approach to parametric multiplicative frailty models and we show that the method works well in practice using both a real data and a simulated example. The proposed algorithm outperforms a Newton-Raphson type algorithm using numerical derivatives.