Research Question 2:

Whilst controlling for regime complexity and support with medicines, does the proposed theoretical model predict medication adherence in the group overall, the IDD and non-IDD population?

Research hypotheses

To address this research question four hypotheses were tested:

VIII. After controlling for confounders, the overall model of medicines adherence would significantly predict medicines non-adherence in the group overall, the IDD and non-IDD population

IX. After controlling for confounders, support with medicines and regime complexity, ID would predict medicines non-adherence in the group overall X. After controlling for confounders, support with medicines, and regime

complexity, depression would predict adherence in the non-IDD group

XI. After controlling for confounders, support with medicines, and regime complexity depression perceived level of social support would predict adherence in the IDD group.

The purpose of these analyses was to establish which independent factors predicted medicines non-adherence. Correlation between the most important factors, mean scores and medicines non-adherence were also investigated. This modelling tested: (1) whether Bandura’s social cognitive theoretical model was predictive of adherence, (2) which independent factors were associated with adherence and (3) whether cut-off scores of statistically significant independent factors could predict medicines non-adherence in the group overall in the IDD and non-IDD population.

128 Chapter 3: Methods

After controlling for confounders, the overall model of medicines adherence will significantly predict medicines non-adherence

To investigate whether the overall model predicted medicines non-adherence, a multiple regression analysis was carried out. Multiple regression is used to predict a score of an independent variable based on multiple dependent variables (Brace et al, 2013; Laerd, 2017).

Carrying out this type of test acknowledges that human behaviour is influenced by many interrelated variables. In multiple regression there is a correction for the correlations among the predictor variables (Laerd, 2017).

The procedure was a three-step procedure (1) the purposeful selection of independent variables entered in the regression model; (2) regression analysis; (3) repeating the regression analysis whilst controlling for potential confounders.

The first step, purposeful selection, began with a univariate analysis of each independent variable. A significant result from univariate analysis was defined as a p-value < 0.15 in the group overall. This p value was selected across all groups as a p-value < 0.05 may fail to identify variables known to be important. This was important in this study as all factors were selected due to their significant association with adherence in previous studies.

The second step, a multiple regression, by analysing selected variables simultaneously using the enter function on SPSS to detect the predictive value of the overall model and, which variables were significant or important predictors of adherence. These steps were repeated for the group overall, the IDD group and the non-IDD group.

The final step was to investigate whether confounding independent factors had any effect on medication adherence using hierarchical multiple regression. Confounding and independent variables were entered sequentially into the model. Confounding variables and independent variables were then inputted simultaneously using the enter function. The effect of these potential confounder variables was removed by using hierarchical multiple regression (Laerd,

Chapter 3: Methods 129

2017). The measure of most importance is the r2 and r2 changes in p values after controlling for confounders.

Confounding variables controlled for were: (1) support with medications, and (2) regime complexity (number of medicines and use of insulin). These factors were selected based on results from the systematic review reporting associations between adherence and support and complexity of regime.

Univariate, sequential and hierarchical multiple regression results were expressed in the standard form as the r2 which is the proportion of variance in the dependent variable which is explained by the independent variables. Cohen’s classification of effect size was used signifying a r2=0-0.2 small effect size, r2=0.2-0.5 medium effect size and r2=0.5-0.8 large effect size (Brace et al, 2013; Cohen, 1988). Statistical significance and F ratio (F) of the overall model and between dependent and independent variables were also reported. Statistical significance provides an estimate of how the overall model predicted medication adherence, and which independent factor was the most important predictor of medication adherence and F ratio is the variance due to the manipulation of the factor(s) divided by the variance due to error. If the overall model was statistically significant hypothesis VII would be upheld.

Determining which independent factor was statistically significantly

associated with adherence in the group overall, IDD and non-IDD groups.

To investigate which independent factors were predictors of adherence the model table was inspected to determine which factors were associated with adherence in the group overall, the IDD and non-IDD group before and after controlling for confounders. If ID was the most important predictor of adherence hypothesis VIII would be upheld. If depression score was significantly associated with medicines non-adherence in the IDD and non-IDD groups then hypotheses IX and X would be upheld.

130 Chapter 3: Methods

Determining correlations between mean score of independent factors significantly associated with medicines non-adherence in the group overall, IDD group and non-IDD group.

To determine predictors of adherence according to mean score, results from the model were examined. A linear regression was performed using the syntax function on SPSS on those which were most importantly associated with adherence either before or after

controlling for confounders, in the group overall, the IDD group or the non-IDD group. This was to provide a comprehensive estimate of adherence according to significant factors across all groups. A similar procedure was followed as detailed in 3.6.4.3. This was to predict which mean score within the independent factor was associated with medicines non-adherence in each of the groups and propose scores from significant independent factors that may predict medicines non-adherence. The score was reported as a mean value across each of the MMAS8 scores from 4, 5 meaning poor adherence, 6, 7 meaning medium adherence and 8 meaning excellent adherence.

Chapter 3: Methods 131