However, if the meta-analysis cannot prove or exclude the existence of a relevant treatment effect, then more tests can be planned within the meta-experiment, and the meta-analysis is definitely then updated. sample size calculation. Inside a simulation study, we compared a meta-experiment approach to the classical approach to assess treatment effectiveness. The meta-experiment approach involves use of meta-analyzed results from 3 randomized tests of fixed sample size, 100 subjects. The classical approach involves a single randomized trial with the sample size calculated on the basis of an calculations can be used if sufficient info is definitely available but motivated researchers to use sample sizes normally. Bacchetti et al [11] argued that experts should take into account costs and feasibility when justifying the sample size of their trial. One isolated example is definitely De Groot’s trial that analyzed a rare disease [12]. They identified the sample size by resources rather than statistical considerations. Simultaneously, Clarke et al [13,14] repeated their call to design and report randomized trials in light of other comparable research. They clearly stated that reports of clinical trials should begin and end with up-to-date systematic reviews of other relevant evidence. Although meta-analyses are intrinsically retrospective studies, some authors suggested prospective meta-analyses [15]. Thus, Chalmers et al. encouraged researchers to use information from research currently in progress and to plan collaborative analyses [15], indicating that is drawn from a normal distribution with mean log(1.5) and SD 0.1. The success rate in the control group is usually drawn from a beta distribution with mean 30% and SD 10%. With the conventional approach, relative errors are simulated to deduce the postulated hypothesis in designing the trial. The sample size 2n is usually calculated to ensure 80% power. A trial of size 2n is usually simulated from the Narcissoside true treatment effect and success rate, and analyzed. With the meta-experiment approach, the same theoretical distributions are used to draw 3 treatments effects and from the normal distribution of treatment effects. In the situation of a non-null treatment effect, we used a distribution with mean log(1.5). Then we draw a success rate from the Beta distribution. For each of these 2 parameters, we draw errors from the empirical error distributions previously observed. Combining the values drawn from the theoretical probability distribution and their associated errors, we derived an and from a normal distribution with mean 0 and success rate from the Beta distribution. We then simulated data for a trial of sample size 300, and data were analyzed by estimating the log of the odds ratio and a 95% CI. Details of parameters for the distributions and calculations are in the S1 File. Meta-experiment approach: in the meta-experiment approach, we neither and Cfrom the Beta distribution. Then, we Narcissoside simulated 3 randomized trials of size 100 each (i.e., 50 patients per group) with these parameters. Finally, we meta-analyzed the 3 estimated treatment effects. We used a random-effects model, allowing the estimated treatment effect to vary among the studies. Simulation parameters Treatment effect: we consider 2 distinct situations allowing for a treatment effect or not: OR of 1 1 (no treatment effect) and 1.5 (non-null treatment effect). Moreover, we assumed inter-study heterogeneity on the treatment effect [17] because of patient characteristics or how the intervention is usually implemented. Therefore, we defined a theoretical distribution for the true treatment effect, where the true effect is normally distributed, with mean = 0 in cases of no treatment effect and log(1.5) otherwise, with SD 0.1. The values were taken from a series of published meta-analyses [17,18]. Success rate in the control group: we also allowed the success rate associated with the control group to follow a probability distribution function. Indeed, patients may differ among studies, which may affect the theoretical success rate associated with the control group. Therefore, we used a Beta distribution, which allows the control arm success rate to vary between 0 to 100%, and set the mean to 30% with a SD of 10%. Statistical outputs We compared the statistical properties of the two approaches. We examined different statistical properties according to whether there was a treatment effect or not. Thus, for a non-null treatment effect, we assessed the following: Power: the proportion of significant results the coverage rate defined as the proportion of runs with the true OR 1.5 within the estimated 95% CI.They require us to specify values for parameters such as the treatment effect, which is often difficult because we lack sufficient prior information. of this paper is usually to provide an alternative design which circumvents the need for sample size IMPG1 antibody calculation. In a simulation study, we compared a meta-experiment approach to the classical approach to assess treatment efficacy. The meta-experiment approach involves use of meta-analyzed results from 3 randomized trials of fixed sample size, 100 subjects. The classical approach involves a single randomized trial with the sample size calculated on the basis of an calculations can be used if sufficient information is usually available but encouraged researchers to use sample sizes otherwise. Bacchetti et al [11] argued that researchers should take into account costs and feasibility when justifying the sample size of their trial. One isolated example is usually De Groot’s trial that studied a rare disease [12]. They decided the sample size by resources rather than statistical considerations. Simultaneously, Clarke et al [13,14] repeated their call to design and report randomized trials in light of other similar research. They clearly stated that reports of clinical trials should begin and end with up-to-date systematic reviews of other relevant evidence. Although meta-analyses are intrinsically retrospective studies, some authors suggested prospective meta-analyses [15]. Thus, Chalmers et al. encouraged researchers to use information from research currently in progress and to plan collaborative analyses [15], indicating that is drawn from a normal distribution with mean log(1.5) and SD 0.1. The success rate in the control group is usually drawn from a beta distribution with mean 30% and SD 10%. With the conventional approach, relative errors are simulated to deduce the postulated hypothesis in designing the trial. The sample size 2n is usually calculated to ensure 80% power. A trial of size 2n is usually simulated from the true treatment effect and success rate, and analyzed. With the meta-experiment approach, the same theoretical distributions are used to draw 3 treatments effects and from Narcissoside the normal distribution of treatment effects. In the situation of a non-null treatment effect, we used a distribution with mean log(1.5). Then we draw a success rate from the Beta distribution. For each of these 2 parameters, we draw errors from the empirical error distributions previously observed. Combining the values drawn from the theoretical probability distribution and their associated errors, we derived an and from a normal distribution with mean 0 and success rate from the Beta distribution. We then simulated data for a trial of sample size 300, and data were analyzed by estimating the log of the odds ratio and a 95% CI. Details of parameters for the distributions and calculations are in the S1 File. Meta-experiment approach: in the meta-experiment approach, we neither and Cfrom the Narcissoside Beta distribution. Then, we simulated 3 randomized trials of size 100 each (i.e., 50 patients per group) with these parameters. Finally, we meta-analyzed the 3 estimated treatment effects. We used a random-effects model, allowing the estimated treatment effect to vary among the studies. Simulation parameters Treatment effect: we consider 2 distinct situations allowing for a treatment effect or not: OR of 1 1 (no treatment effect) and 1.5 (non-null treatment effect). Moreover, we assumed inter-study heterogeneity on the treatment effect [17] because of patient characteristics or how the intervention is usually implemented. Therefore, we defined a theoretical distribution for the true treatment effect, where the true effect is normally distributed, with mean = 0 in cases of no treatment effect and log(1.5) otherwise, with SD 0.1. The values were taken from a series of published meta-analyses [17,18]. Success rate in the control group: we also allowed the success rate associated with the control group to follow a probability distribution function. Indeed, patients may differ among studies, which may affect the theoretical success rate associated with the control group. Therefore, we used a Beta distribution, which allows the control arm success rate to vary between 0 to 100%, and set the mean to 30% with a SD of 10%. Statistical outputs We likened the statistical properties of both approaches. We analyzed different statistical properties relating to whether there is a treatment impact.