Abstract
BackgroundPatient-reported outcome measures (PROMs) are now frequently used in randomised controlled trials (RCTs) as primary endpoints. RCTs are longitudinal, and many have a baseline (PRE) assessment of the outcome and one or more post-randomisation assessments of outcome (POST). With such pre-test post-test RCT designs there are several ways of estimating the sample size and analysing the outcome data: analysis of post-randomisation treatment means (POST); analysis of mean changes from pre- to post-randomisation (CHANGE); analysis of covariance (ANCOVA).Sample size estimation using the CHANGE and ANCOVA methods requires specification of the correlation between the baseline and follow-up measurements. Other parameters in the sample size estimation method being unchanged, an assumed correlation of 0.70 (between baseline and follow-up outcomes) means that we can halve the required sample size at the study design stage if we used an ANCOVA method compared to a comparison of POST treatment means method. So what correlation (between baseline and follow-up outcomes) should be assumed and used in the sample size calculation? The aim of this paper is to estimate the correlations between baseline and follow-up PROMs in RCTs.MethodsThe Pearson correlation coefficients between the baseline and repeated PROM assessments from 20 RCTs (with 7173 participants at baseline) were calculated and summarised.ResultsThe 20 reviewed RCTs had sample sizes, at baseline, ranging from 49 to 2659 participants. The time points for the post-randomisation follow-up assessments ranged from 7 days to 24 months; 464 correlations, between baseline and follow-up, were estimated; the mean correlation was 0.50 (median 0.51; standard deviation 0.15; range − 0.13 to 0.91).ConclusionsThere is a general consistency in the correlations between the repeated PROMs, with the majority being in the range of 0.4 to 0.6. The implications are that we can reduce the sample size in an RCT by 25% if we use an ANCOVA model, with a correlation of 0.50, for the design and analysis. There is a decline in correlation amongst more distant pairs of time points.
Highlights
Patient-reported outcome measures (PROMs) are frequently used in randomised controlled trials (RCTs) as primary endpoints
A variety of summary statistics for the baseline and post-randomisation correlations were calculated, including (1) the unweighted sample mean and median; (2) a weighted sample mean, using the fixed effect inverse variance method [4], and (3) a sample mean with allowance for clustering by trial derived from a multilevel mixed-effects linear model with a random effect for the trial using restricted maximum likelihood estimation (REML) [8]
Three of the outcome measures, the Clinical Outcomes in Routine Evaluation Outcome Measure (CORE-OM), Pelvic Organ Prolapse/ Urinary Incontinence Sexual Questionnaire (PISQ-31) and Shoulder Pain and Disability Index (SPADI), have a total score and various subscales: both were included in the analysis
Summary
RCTs are longitudinal, and many have a baseline (PRE) assessment of the outcome and one or more post-randomisation assessments of outcome (POST) With such pre-test post-test RCT designs there are several ways of estimating the sample size and analysing the outcome data: analysis of post-randomisation treatment means (POST); analysis of mean changes from pre- to post-randomisation (CHANGE); analysis of covariance (ANCOVA). The aim of this paper is to estimate the correlations between baseline and follow-up PROMs in RCTs. Patient-reported outcome measures (PROMs) are frequently used in randomised controlled trials (RCTs) as primary endpoints. All RCTs are longitudinal, and many have a baseline, or pre-randomisation (PRE) assessment of the outcome, and one or more postrandomisation assessments of outcome (POST) For such pre-test post-test RCT designs, using a continuous primary outcome, the sample size estimation and the analysis of the outcome can be done using one of the following methods: 1. For brevity (and following Frison and Pocock’s nomenclature [1]), these methods will be referred to as POST, CHANGE and ANCOVA respectively
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