Sample size calculations in pediatric clinical trials conducted in an ICU: a systematic review

Abstract

At the design stage of a clinical trial, several assumptions have to be made. These usually include guesses about parameters that are not of direct interest but must be accounted for in the analysis of the treatment effect and also in the sample size calculation (nuisance parameters, e.g. the standard deviation or the control group event rate). We conducted a systematic review to investigate the impact of misspecification of nuisance parameters in pediatric randomized controlled trials conducted in intensive care units. We searched MEDLINE through PubMed. We included all publications concerning two-arm RCTs where efficacy assessment was the main objective. We included trials with pharmacological interventions. Only trials with a dichotomous or a continuous outcome were included. This led to the inclusion of 70 articles describing 71 trials. In 49 trial reports a sample size calculation was reported. Relative misspecification could be calculated for 28 trials, 22 with a dichotomous and 6 with a continuous primary outcome. The median [inter-quartile range (IQR)] overestimation was 6.9 [-12.1, 57.8]% for the control group event rate in trials with dichotomous outcomes and -1.5 [-15.3, 5.1]% for the standard deviation in trials with continuous outcomes. Our results show that there is room for improvement in the clear reporting of sample size calculations in pediatric clinical trials conducted in ICUs. Researchers should be aware of the importance of nuisance parameters in study design and in the interpretation of the results.

Figure 1

Impact of misspecification of control event rate on total sample size. The sample sizes required to detect RRs with 80% power at a 5% two-sided type I error level are shown. The dotted vertical line indicates the assumed CER. (See text for further explanation). CER, control group event rate; RR, risk ratio.

Figure 2

Illustration of minimum detectable effect size. The minimum detectable effect size (MDES) is the minimum difference between groups that yields a statistically significant result. The power of the study is calculated using the clinically relevant effect size (CRES). The density of the left curve to the right of the MDES represents the type I error, i.e. 2.5% (one-sided) in this case. The density of the middle curve to the right of the MDES represents the power for the MDES to be detected, i.e. 50%. The density of the right curve to the right of the MDES represents the power for the CRES to be detected, i.e. the power of such a study, which is 98% in this example. CRES, clinically relevant effect size; MDES, minimum detectable effect size.

Figure 3

Flowchart of the search and inclusion procedure.

Figure 4

Misspecification of nuisance parameters. Relative misspecification of NPs in the trials reviewed = (Expected value of NP – Observed value of NP)/Observed value of NP.

Figure 5

Relative misspecification (as a percentage) of MDES in trials with a dichotomous outcome. The vertical axis represents the quantity: (MDES with the assumed NP – MDES with the observed NP)/MDES with the observed NP. MDES, minimum detectable effect size; NP, nuisance parameter.