Efficient statistical inference for a parallel study with missing data by using an exact method

Shan, Guogen1; Hutson, Alan2; Wilding, Gregory E.3; Ma, Changxing3; Tian, Guo-Liang4

2019-05-04

10.1080/10543406.2019.1605782

Journal of Biopharmaceutical Statistics   影响因子和分区

1054-3406

1520-5711

In a parallel group study comparing a new treatment with a standard of care, missing data often occur for various reasons. When the outcome is binary, the data from such studies can be summarized into a 2x3 contingency table, with the missing observations in the last column. When the missingness is neither related to the outcome of interest nor related to other outcomes from the study but it is covariate dependent with the sole covariate being treatment, this type of missing data mechanism is considered as missing at random. In 2016, Tian et al. proposed three statistics to test the hypothesis that the response rate is equivalent for a parallel group study with missing data. The asymptotic limiting distributions of these test statistics were used for statistical inference. However, asymptotic approaches for testing proportions generally do not have satisfactory performance with regard to type I error rate control for a clinical trial with the sample size from small to medium. For this reason, we consider an exact approach based on maximization to provide valid and efficient statistical inference for a parallel group study with missing data. Exact approaches can guarantee the type I error rate and they are computationally feasible in this setting. We conduct extensive numerical studies to compare the performance of the exact approach based on the three statistics for a one-sided hypothesis testing problem. We conclude that the exact approach based on the likelihood ratio statistic is more powerful than the exact approach based on the other two statistics. Two real clinical trial data sets are used to illustrate the application of the proposed exact approach.

Data generating mechanism exact test one-sided test parallel study unconditional test

SCI

英语

其他

National Natural Foundation of China[11771199]

Pharmacology & Pharmacy ; Mathematics

Pharmacology & Pharmacy ; Statistics & Probability

WOS:000469139500005

TAYLOR & FRANCIS INC

Web of Science

1.UNLV, Sch Publ Hlth, Epidemiol & Biostat Program, Las Vegas, NV 89154 USA
2.Roswell Pk Canc Inst, Dept Biostat & Bioinformat, Buffalo, NY 14263 USA
3.SUNY Buffalo, Dept Biostat, Buffalo, NY USA
4.Southern Univ Sci & Technol, Dept Math, Shenzhen, Peoples R China

Shan, Guogen,Hutson, Alan,Wilding, Gregory E.,et al. Efficient statistical inference for a parallel study with missing data by using an exact method[J]. Journal of Biopharmaceutical Statistics,2019,29(3):478-490.

Shan, Guogen,Hutson, Alan,Wilding, Gregory E.,Ma, Changxing,&Tian, Guo-Liang.(2019).Efficient statistical inference for a parallel study with missing data by using an exact method.Journal of Biopharmaceutical Statistics,29(3),478-490.

Shan, Guogen,et al."Efficient statistical inference for a parallel study with missing data by using an exact method".Journal of Biopharmaceutical Statistics 29.3(2019):478-490.