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1.  A dynamic approach for reconstructing missing longitudinal data using the linear increments model 
Biostatistics (Oxford, England)  2010;11(3):453-472.
Missing observations are commonplace in longitudinal data. We discuss how to model and analyze such data in a dynamic framework, that is, taking into consideration the time structure of the process and the influence of the past on the present and future responses. An autoregressive model is used as a special case of the linear increments model defined by Farewell (2006. Linear models for censored data, [PhD Thesis]. Lancaster University) and Diggle and others (2007. Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal. Journal of the Royal Statistical Society, Series C (Applied Statistics, 56, 499–550). We wish to reconstruct responses for missing data and discuss the required assumptions needed for both monotone and nonmonotone missingness. The computational procedures suggested are very simple and easily applicable. They can also be used to estimate causal effects in the presence of time-dependent confounding. There are also connections to methods from survival analysis: The Aalen–Johansen estimator for the transition matrix of a Markov chain turns out to be a special case. Analysis of quality of life data from a cancer clinical trial is analyzed and presented. Some simulations are given in the supplementary material available at Biostatistics online.
PMCID: PMC3293429  PMID: 20388914
Cancer clinical trial; Dynamic approach; Linear increments model; Longitudinal data; Missing data; Quality of life
2.  Frailty modeling of bimodal age-incidence curves of nasopharyngeal carcinoma in low-risk populations 
Biostatistics (Oxford, England)  2009;10(3):501-514.
The incidence of nasopharyngeal carcinoma (NPC) varies widely according to age at diagnosis, geographic location, and ethnic background. On a global scale, NPC incidence is common among specific populations primarily living in southern and eastern Asia and northern Africa, but in most areas, including almost all western countries, it remains a relatively uncommon malignancy. Specific to these low-risk populations is a general observation of possible bimodality in the observed age-incidence curves. We have developed a multiplicative frailty model that allows for the demonstrated points of inflection at ages 15–24 and 65–74. The bimodal frailty model has 2 independent compound Poisson-distributed frailties and gives a significant improvement in fit over a unimodal frailty model. Applying the model to population-based cancer registry data worldwide, 2 biologically relevant estimates are derived, namely the proportion of susceptible individuals and the number of genetic and epigenetic events required for the tumor to develop. The results are critically compared and discussed in the context of existing knowledge of the epidemiology and pathogenesis of NPC.
PMCID: PMC2697345  PMID: 19329819
Carcinogenesis; Compound Poisson; Frailty; Nasopharyngeal carcinoma; Survival analysis

Results 1-2 (2)