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2022

Review: Acceptance Sampling

Abstract

Standards for quality control become higher as technology is miniaturized and advances. It is necessary to develop advanced sampling plans to meet high standards because traditional sampling plans need a relatively big sample size that is not economical. There have been approaches to reduce the sample size while controlling the correctness of a test. This paper includes basic approaches and their implications, such as some principles of sample size reduction. One can understand basic concepts of acceptance sampling plans and how to calculate corresponding probabilities by reading this paper. Anyone who is interested in sampling plans but doesn't know where to start is a potential reader for this paper.

Review: Sample size issues in time series regressions of counts on environmental exposures

Author: Ben G. Armstrong, Antonio Gasparrini, Aurelio Tobias and Francesco Sera

Journal: BMC Medical Research Methodology (2020)

요약

  1. Effect measure에 관련된 검정력을 특정 값 이상으로 보장하기 위해서 얼마나 많은 sample size가 필요한지 알고 싶다.
  2. 이 논문에서 소개된 approximation for Standard error of effect measure를 사용하면, 검정력을 보장하기 위한 count의 수를 알 수 있다. (sample size 대신)

Review: Attributable risk from distributed lag models

Author: Antonio Gasparrini and Michela Leone

Journal: BMC Medical Research Methodology (2014)

Suggested prerequisite: understanding about the DLNM framework.

요약

  1. multi-exposure attributable risk 개념을 응용해 attributable risk가 temporal dimension을 반영하도록 개념을 확장함.
  2. 이 방법은 특정 exposure range에 대해서 attributable risk를 계산할 수 있다는 장점도 가지고 있음.

Review: Multivariate meta-analysis for non-linear and other multi-parameter associations

Author: A. Gasparrini, B. Armstrong, and M. G. Kenward

Journal: Statistics in Medicine (2012)

요약

  1. Multivariate regression을 응용해서, outcome이 다변수 벡터인 경우도 메타분석이 가능하다.
  2. 첫번째 저자가 내용을 정리해 R package "mvmeta"를 만들었으니, 활용하면 된다.
  3. 이후에 나온 패키지 "mixmeta"는 이 모델에서 좀 더 일반화된 모델을 다룬다.