How to assess success of treatment when using multiple doses: the case of misoprostol for medical abortion. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Seuc, Armando; Shah, Iqbal; Ali, Moazzam; Diaz-Olavarrieta, Claudia; Temmerman, Marleen
• Is Part Of: Trials. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-12
• Subjects: Medical abortion -- Binomial proportion -- Survival analysis -- Competing risks; Group-randomized trials -- Periodicals; Randomized Controlled Trials -- Periodicals; 615.0727
• DOI: 10.1186/s13063-015-1035-0
• Languages: English
• ISSNs: 1745-6215

Abstract

Abstract Background The assessment of treatment success in clinical trials when multiple (repeated) doses (courses) are involved is quite common, for example, in the case of infertility treatment with assisted reproductive technology (ART), and medical abortion using misoprostol alone or in combination with mifepristone. Under these or similar circumstances, most researchers assess success using binomial proportions after a certain number of consecutive doses, and some have used survival analysis. In this paper we discuss the main problems in using binomial proportions to summarize (the overall) efficacy after two or more consecutive doses of the relevant treatment, particularly for the case of misoprostol in medical abortion studies. We later discuss why the survival analysis is best suited under these circumstances, and illustrate this by using simulated data. Methods The formulas required for the binomial proportion and survival analysis (without and with competing risks) approaches are summarized and analytically compared. Additionally, numerical results are computed and compared between the two approaches, for several theoretical scenarios. Results The main conceptual limitations of the binomial proportion approach are identified and discussed, caused mainly by the presence of censoring and competing risks, and it is demonstrated how survival analysis can solve these problems. In general, the binomial proportion approach tends to underestimate the "real" success rate, and tends to overestimate the corresponding standard error. Conclusions Depending on the rates of censored observations or competing events between repeated doses of the treatment, the bias of the binomial proportion approach as compared to the survival analysis approaches varies; however, the use of the binomial approach is unjustified as the survival analysis options are well known and available in multiple statistical packages. Our conclusions also apply to other situations where success is estimated after multiple (repeated) doses (courses) of the treatment.