Randomisation, blinding, and contamination

Although randomised controlled trials are the most rigorous type of scientific evaluation, the reliability and validity of the evidence from a trial has to rely on an appropriate procedure of random allocation (Kendall, 2003). Allocation in this study was determined by a stratified block randomisation, with random block size and stratified by the two study hospitals. This prevented too great an imbalance in the number of participants allocated to each arm and avoided the risk of being able to predict the allocation in advance.

Data from the qualitative interviews showed that patients’ understandings of the design of the study and what procedures were involved in the study were variable. This has become a widely acknowledged problem in any randomised trial (Kerr et al., 2004, Ellis, 2000, Robinson et al., 2005). Although considerable effort had been made to provide clear and accurate information for patients’ consent in this study, doing so could not guarantee their full understanding of it, especially some difficult concepts such as randomisation. It suggests that more discussions and explanations about the purpose of randomisation need to be available for patients in order for investigators to be confident that consent given is fully informed.

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To avoid bias, randomised controlled trials should ideally be double blinded, where the participants and those responsible for their treatment or the evaluators are unaware of which treatment the participant is receiving (Pocock, 1983, Schulz and Grimes, 2002). However, due to the nature of the intervention participants could not be blinded to study group allocation. There is the risk therefore that some of the differences observed at follow- up may be due to social desirability bias. To counteract this potential problem it was ensured that the nurse collecting follow-up self-completion measures was unaware as to which group the participant had been allocated and was not the one who delivered the preoperative education intervention. Baseline measures were carried out prior to the random allocation. In conducting the analysis I was blind to study group labels. That some evidence of a difference in hours spent in ICU postoperatively was found also suggests that the difference in outcomes observed were not limited to self-reported outcomes.

Although the leaflet was kept in an envelope and patients allocated to preoperative education were asked not to pass on the leaflet, the possibility of contamination between the two groups cannot be excluded. The resources were not available to cluster randomise so patients allocated to different groups could, in theory, be cared for in the same preoperative ward alongside each other. Even if the participants in the usual care group could have been prevented from seeing the leaflet, it was impossible to avoid those in the preoperative education group sharing the knowledge and skills they had learned from the intervention by talking with those in the usual care group. So the risk of information leakage from the intervention group to the control group leading to contamination of the trial arms can only be minimised but not be avoided. However, the effect of

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contamination is likely to have resulted in an underestimation rather than an overestimation of differences between the two groups at follow-up, that is, the point estimate of an intervention’s effectiveness can be reduced due to contamination between the two trial arms (Winkens et al., 1997, Torgerson, 2001, Keogh-Brown et al., 2007).

8.3.4 Data analysis

Intention to treat analysis is generally considered the most robust analysis for randomised controlled trials because of the inclusion of patients not receiving intervention despite being randomised to intervention group (Pocock, 1983). However, the use of a strict intention to treat analysis was impossible in cases of missing data such as loss to follow-up (Abraha and Montedori, 2010). Per protocol analysis also did not seem to be an appropriate label to describe data analysis reported in this trial as it implies that any individual receiving a treatment they were not randomised to would be analysed according to the treatment received rather than the treatment randomly allocated (Sedgwick, 2011, Sedgwick, 2010, Shah, 2011).

In this trial, all participants who completed follow-up were analysed as a part of the group to which they were randomised and those lost to follow- up were excluded from analyses. The 14 participants who were lost to follow-up become effectively ineligible for the study by not having the surgery for which the intervention was designed and which would make the outcomes meaningful. Therefore, although these individuals were ‘lost to follow-up’ in the technical sense, an alternative way of dealing with them would be to have excluded them on the basis of their subsequent ‘ineligibility’.

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For the trial, independent-samples t-tests were used for anxiety, depression and pain. Differing effects of socio-demographic data such as age, gender and type of cardiac surgery should be taken into account when analysing the data. It seemed more appropriate to also report data analysis from linear regression models, whereby anxiety, symptoms of depression, and pain scores were compared between two groups at follow-up after controlling for baseline score, age, gender, education level, and surgery type.

The qualitative evaluation consisted of the rich descriptive narrative derived from semi-structured interviews with a sample of trial participants. By analysing interview participants’ own accounts, patients’ voices were heard, and their views on preoperative education and experiences of the intervention and the trial were therefore reflected. With regard to interview data analysis, it is important to ensure that the interpretation of data is as close to the actual meaning of participants’ accounts as possible (Silverman, 2001, Green and Thorogood, 2009).

Respondent validation, whereby participants are provided with written transcripts or data analysis to check and confirm whether they reflect participants’ own experience, may contribute to the credibility and rigor of findings (Mays and Pope, 2000, Barbour, 2001). However seeking validation from participants can be problematic because views may change over time. Researchers are then faced with dilemma when respondents wish to change data (Johnson and Waterfield, 2004). Through respondent validation, additional data are gathered and require further analysis (Papadopoulos et al., 2002). This may lead to confusion rather than confirmation. So the value of respondent validation may be questionable (Cutcliffe and McKenna, 2002, Horsburgh, 2003). Since the interview

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transcripts were not reported back to the participants, it is acknowledged that there may have been some meaning lost through interpretation and translation in this study.