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Discovering the causes of cancer and the means of prevention

Biased Sampling, Left Truncation and Survival Analysis, Lecture I - Dr. Jing Qin

Biostatistics Branch Seminar Series

September 16, 2019 | 1:30 PM – 3:30 PM

NCI Shady Grove 6E032/034 Rockville, MD

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Jing Qin, Ph.D.
Mathematical Statistician, Biostatistics Research Branch
National Institute of Allergy & Infectious Diseases, NIH


Biased sampling occurs when a proper randomization cannot be achieved; in this case, the observed sample will not be representative of the population of interest. Biased sampling problems appear in many areas of research, including, medicine, epidemiology and public health, social sciences, and economics. Left truncation and length-biased data are clearly encountered in applications of renewal processes, etiologic studies, genome-wide linkage studies, epidemiologic cohort studies, cancer prevention trials, and studies of labor economy. In observational studies, a prevalent cohort cohort design that draws samples from individuals with a condition or disease at the time of enrollment is generally more efficient and practical. The recruited patients who have already experienced an initiating event are followed prospectively for the failure event (e.g. disease progression or death) or are right censored. Under this sampling design, individuals with longer survival times measured from the onset of the disease are more likely to be included in the cohort, whereas those with shorter survival times are unconsciously excluded. Finding appropriate adjustments for the potential selection bias in analyzing length-biased data or more general biased sampling problems has been a long standing statistical problem.
In lecture I, I will discuss left truncation and right censored data problems, including maximum rank likelihood method, maximum partial likelihood method, matched case and control conditioning likelihood method as well as general imputation methods.

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**The mission of the Biostatistics Branch (BB) is to be an outstanding biostatistics unit that can contribute to the understanding of cancer etiology and to improve public health by the development and application of quantitative methods.  The BB Investigators develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.**