Non-regular inference for dynamic weighted ordinary least squares: understanding the impact of solid food intake in infancy on childhood weight

作者：Simoneau Gabrielle^*; Moodie Erica E M; Platt Robertw; Chakraborty Bibhas

来源：Biostatistics, 2018, 19(2): 233-246.

DOI：10.1093/biostatistics/kxx035

• Adaptive treatment strategies
• Dynamic treatment regimens
• Dynamic weighted ordinary least squares
• Non-regular inference
• m-out-of-n bootstrap
• Personalized medicine

A dynamic treatment regime (DTR) is a set of decision rules to be applied across multiple stages of treatments. The decisions are tailored to individuals, by inputting an individual's observed characteristics and outputting a treatment decision at each stage for that individual. Dynamic weighted ordinary least squares (dWOLS) is a theoretically robust and easily implementable method for estimating an optimal DTR. As many related DTR methods, the dWOLS treatment effects estimators can be non-regular when true treatment effects are zero or very small, which results in invalid Wald-type or standard bootstrap confidence intervals. Inspired by an analysis of the effect of diet in infancy on measures of weight and body size in later childhood a setting where the exposure is distant in time and whose effect is likely to be small we investigate the use of the m-out-of-n bootstrap with dWOLS as method of analysis for valid inferences of optimal DTR. We provide an extensive simulation study to compare the performance of different choices of resample size m in situations where the treatment effects are likely to be non-regular. We illustrate the methodology using data from the PROmotion of Breastfeeding Intervention Trial to study the effect of solid food intake in infancy on long-term health outcomes.

• 出版日期2018-4
• 单位McGill