Jing Ning, PhD - Statistical Modeling and Adjustment for Sampling Biases
Jing Ning, PhD, Associate Professor, Department of Biostatistics, University of Texas M.D. Anderson Cancer Center presents recent work to adjust the sampling biases in diverse applications such as the survivorship bias in prevalent cohort, the self-reporting bias in longitudinal analysis and the publication bias in meta-analysis.
Bias sampling mechanisms are commonly encountered in applications where the subjects in a target population are not given an equal chance to be selected, either accidentally, by natural circumstances, or intentionally by design. Statistical methods not properly accounting for such a challenge often lead to invalid inferences. For example, evidence combined from published studies may lead to overly optimistic conclusions due to publication bias, and the well-known length bias can cause the screening to appear to be more successful than it really is.
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