Faculty in the Department of Biostatistics devote great effort into genomic research (31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48), imaging data analysis (49,50,51,52,53), survival analysis (54,55,56,57,58,59,60,61), biomarker data analysis (62,63), drug interactions (64), causal inference (65,66,67), epidemiology studies (68,69,70,71,72), and many other diverse topics. Next is a discussion of some issues in the analyses of disease recurrence, cancer screening, and biomarker studies.
Although curing cancer remains a challenge, medical advancements have successfully transitioned many types of cancer from a rapidly fatal disease to a chronic disease. After their initial treatments, patients may experience a few disease recurrences and receive different salvage treatments after each such recurrence. That is, cancer patients usually experience the following process:
This is a long and complicated process. For many forms of cancer, a number of therapeutic options are available at the initial and at subsequent salvage treatment stages. In these circumstances, instead of considering only the effect of a treatment on the time to the next disease recurrence, it is important to understand the long-term effects of each treatment on the overall survival time. Optimizing treatment decisions during this process can minimize treatment toxicity, reduce drug resistance, prolong the survival time, and enhance the patient’s quality of life.
The duration of disease-free survival is commonly used in medical research to compare different treatments. This method considers the time to disease recurrence or death, whichever happens first. Equating death with disease recurrence in this manner does not give sufficient penalty to treatments associated with high death rates. This is a serious problem when the lifetime after disease recurrence can be substantial. Consequently, disease-free survival is not the best basis for making treatment decisions.
A number of statistical methods have been developed to address the shortcomings of methods based on the end point of disease-free survival (73,74,75,76,77,78). We have been using frailty models to analyze recurrent events and a terminal event, such as death (73,74,75). We provide estimation of the effects on survival by treatment sequence (76). We provide new and easily implemented statistical approaches to optimize treatment sequences for recurrent diseases (77,78). The optimized treatment sequences are personalized; that is, treatment decisions depend on a patient’s previous response, current disease status, characteristics, and genetic biomarkers. These methods can be applied to data from randomized or nonrandomized studies.
The importance of dealing with the problem of informative censoring is well known in statistics, but it remains a challenge. We have developed a frailty model for informative censoring (79) and a test for informative censoring in clustered survival data (80). They can be used to test the presence of informative censoring, to estimate the degree of association between censoring and the risks affecting survival, and to estimate treatment effects while accounting for the informative censoring. This model can also be used to assess the correlation between different competing risks. We have developed a method for conducting sensitivity tests for survival analysis (81).
Screening for risk factors or early evidence of disease is important for cancer prevention. The distribution of the preclinical duration of cancer is unobservable, but knowledge of this distribution would be of great help in many situations. For example, such knowledge can help in making recommendations about optimal cancer-screening frequencies. We have developed a nonparametric method to estimate the preclinical duration distribution using data from a randomized early cancer detection trial (82). This estimation method is expected to have good practical use.
An important aspect of modern cancer research is the identification of molecular and genetic markers that predict an individual’s cancer risk and future response to a treatment and the validation of these identified markers. We have provided a method to use in building and validating a prognostic index for biomarker studies (83). This method is especially useful when there are many markers under consideration, which is currently true of typical biomarker trials because of the use of high-throughput arrays and other modern biomedical technologies.
The statistical analyses we have conducted for numerous biomarker studies are crucial for the translation of laboratory research results into clinical innovations, with a frequent goal of replacing toxic chemotherapies with safe and effective targeted therapies. A good example of targeted therapy is the use of tyrosine kinase inhibitors (TKIs, such as imatinib) to successfully control chronic myeloid leukemia (CML). Working with the Department of Leukemia, we found that CCL3 (MIP-1a) plasma levels were associated with the risk of disease progression in chronic lymphocytic leukemia (CLL) (84). Then, we helped design a phase I/II study to determine the effects of an Syk-JAK inhibitor on this biomarker. We also found that DNA methylation predicted survival and response to therapy in patients with MDS (28). Another finding was that the gene that produces the protein survivin was highly expressed in leukemic stem cells and predicted poor clinical outcomes in AML (85). These are examples of the biomarker discoveries to which we have contributed (86,87,88,89,90,91,92,93,94).