Table 2 Article classifications
Authors | Year | Taxonomy a | Further classification variables | Brief summary | |
|---|---|---|---|---|---|
| Â | V, HT, E, DMP | Prespecified or Emerging (single or multiple outcomes) | (Group) Sequential (monitoring) - yes/no | ||
Amit, Heiberger & Lane [25] | 2008 | V | Emerging (single & multiple) | No | Dot plot for emerging AEs, Kaplan-Meier and hazard function for single AEs and cumulative frequency plots, boxplots and line graphs for continuous outcomes |
Chuang-Stein, Le & Chen [26] | 2001 | V | Emerging (single) | No | Displays two-by-two frequencies graphically for emerging AEs, histograms and delta plots for continuous outcomes |
Chuang-Stein & Xia [27] | 2013 | V | Emerging (single & multiple) | No | Bar charts, Venn diagrams and Forest plots for emerging AEs, risk over time for single AEs and e-Dish plots for continuous outcomes |
Karpefors & Weatherall [28] | 2018 | V | Emerging (multiple) | No | Tendril plot for emerging AEs |
Southworth [29] | 2008 | V | Emerging (single) | No | Scatterplot with regression outputs for continuous outcomes |
Trost & Freston [30] | 2008 | V | Emerging (multiple) | No | Vector plots for continuous outcomes, includes 3 outcomes per plot |
Zink, Wolfinger & Mann [31] | 2013 | V | Emerging (multiple) | No | Volcano plot for emerging AEs |
Zink, Marchenko, Sanchez-Kam, Ma & Jiang [14] | 2018 | V | Emerging (multiple) | No | Heat map for emerging AEs |
Bolland & Whitehead [32] | 2000 | HT | Prespecified | Yes | Alpha spending function |
Fleishman & Parker [33] | 2012 | HT | Prespecified | Yes | Alpha spending function, adjustment to significance threshold and conditional power |
Lieu et al. [34] | 2007 | HT | Prespecified | Yes | Likelihood ratio test |
Liu [35] | 2007 | HT | Prespecified | No | Non-inferiority test |
Shih, Lai, Heyse & Chen [36] | 2010 | HT | Prespecified | Yes | Likelihood ratio test |
Agresti & Klingenberg [37] | 2005 | HT | Emerging (overall profile) | No | Multivariate likelihood ratio tests for overall AE numbers |
Bristol & Patel [38] | 1990 | HT | Emerging (overall profile) | No | Multivariate likelihood ratio test with Markov chains for overall AE numbers, incorporating recurrent events |
Chuang-Stein, Mohberg & Musselman [39] | 1992 | HT | Emerging (overall profile) | No | Multivariate test for overall AE numbers with chi-squared distribution, incorporating severity and participant acceptability scores |
Huang, Zalkikar & Tiwari [40] | 2014 | HT | Emerging (single) | Yes | Likelihood ratio tests for AE rate (i.e. incorporating exposure time), incorporating recurrent events |
Mehrotra & Adewale [41] | 2012 | HT | Emerging (multiple) | No | P-value adjustment |
Mehrotra & Heyse [42] | 2004 | HT | Emerging (multiple) | No | P-value adjustment |
Allignol, Beyersmann & Schmoor [43] | 2016 | E | Emerging (single) | No | Estimates cumulative incidence function in presence of competing risks |
Borkowf [44] | 2006 | E | Emerging (single) | No | Confidence interval for difference in proportions |
Evans & Nitsch [45] | 2012 | E | Emerging (single) | No | Proportions, incidences, odds ratios etc. |
Gong, Tong, Strasak & Fang [46] | 2014 | E | Emerging (single) | No | Non-parametric estimate for mean cumulative number of recurrent events in presence of competing risks |
Hengelbrock, Gillhaus, Kloss & Leverkus [47] | 2016 | E | Emerging (single) | No | Survival based methods to estimate hazard ratios for recurrent events |
Lancar, Kramar & Haie-Meder [48] | 1995 | E | Emerging (single) | No | Non-parametric estimate for prevalence allowing for recurrent events |
Leon-Novelo, Zhou, Nebiyou Bekele & Muller [49] | 2010 | E | Emerging (multiple) | No | Bayesian approach to estimate the probability of severity grading of events in treatment and control groups separately |
Liu, Wang, Liu & Snavely [50] | 2006 | E | Emerging (single) | No | Confidence interval for difference in exposure adjusted incidence rates |
Nishikawa, Tango & Ogawa [51] | 2006 | E | Emerging (single) | No | Estimates the cumulative incidence function in presence of competing risks and conditional estimate for recurrent events |
O’Gorman, Woolson & Jones [52] | 1994 | E | Emerging (single) | No | Confidence intervals for difference in proportion |
Rosenkranz [53] | 2006 | E | Emerging (single) | No | Survival based method to estimate dependence between AE time and discontinuation time |
Siddiqui [15] | 2009 | E | Emerging (single) | No | Non-parametric estimate for the cumulative mean number of events allowing for recurrent events |
Sogliero-Gilbert, Ting, & Zubkoff [54] | 1991 | E | Emerging (multiple) | No | A score to indicate abnormal laboratory values |
Wang & Quartey [55] | 2012 | E | Emerging (single) | No | Non-parametric estimate for mean cumulative event duration allowing for recurrent events |
Wang & Quartey [56] | 2013 | E | Emerging (single) | No | Semi-parametric estimate for mean cumulative event duration allowing for recurrent events |
Berry [57] | 1989 | DMP | Prespecified | Yes | Bayesian approach to estimate the posterior probability that event rate or incidence rate (incorporating exposure time) is greater in the treatment group compared to control group |
French, Thomas & Wang [58] | 2012 | DMP | Prespecified | Yes | Bayesian logit model and a piecewise exponential models to give posterior probabilities that predefined risk difference threshold is exceeded |
Yao, Zhu, Jiang & Xia [59] | 2013 | DMP | Prespecified | Yes | Bayesian beta-binomial model to give posterior probability that predefined risk difference threshold is exceeded |
Zhu, Yao, Xia & Jiang [60] | 201638 | DMP | Prespecified | Yes | Bayesian gamma-Poisson model to give posterior probability that predefined risk difference (incorporating exposure time) threshold is exceeded |
Berry & Berry [61] | 2004 | DMP | Emerging (multiple) | No | Bayesian hierarchical logit model to give posterior probability that event rate greater in treatment group compared to control group |
Chen, Zhao, Qin & Chen [62] | 2013 | DMP | Emerging (multiple) | Yes | Bayesian hierarchical logit model to give posterior probability that event rate greater in treatment group compared to control group for interim analysis |
Gould [63] | 2008 | DMP | Emerging (multiple) | No | Bayesian approach to estimate the posterior probability that AEs in treatment group produced by a larger process than AEs in control group |
Gould [64] | 2013 | DMP | Emerging (multiple) | No | Bayesian approach to estimate the posterior probability that AEs in treatment group produced by a larger process than AEs in control group accounting for exposure time |
McEvoy, Nandy & Tiwari [65] | 2013 | DMP | Emerging (multiple) | No | Bayesian multivariate approach to give posterior probability of difference in event rates based on indicator functions |
Xia, Ma & Carlin [66] | 2011 | DMP | Emerging (multiple) | No | Bayesian hierarchical logit and log-linear (incorporating exposure time) models to give posterior probability that event rate greater in treatment compared to control group |