Chapter 6 106 Exercise and fitness variables were collected by self-reports. At baseline patients were asked about exercise history (yes/no). Sample size Data were collected for regression modelling, so therefore sample size calculation has been determined for this purpose with the graph of Miles and Shevlin [27]. The graph illustrates the sample size needed to achieve different levels of power, for different effect sizes, as the number of predictors vary. For ten predictors and a medium effect size, a number of 150 participants is needed. Statistical analysis All statistical analyses were performed in R version 4.0.3 using “mice” for multiple imputation, “rms” for logistic regression modelling, “epi” for calculating the AUC, “cutpointr” for calculation of the “optimal” cut-off value, and “rmda” for decision curve analysis. For all analyses, p < 0.05 was considered statistically significant. Missing data Following the in-dept considerations of the patterns of missing data, the data were assumed to be missing at random. First the amount of missingness for each variable was calculated; the difference between the sample size and the number of useable observations. Second, Fisher exact tests were used to analyze differences in baseline characteristics between patients with missing and complete data. Finally, multiple imputation was used to create and analyze five multiple imputed datasets. Incomplete variables were imputed under fully conditional specification. Analyses were done in each imputed dataset and pooled using Rubin's rules in the primary analysis. Baseline characteristics Baseline characteristics are presented with appropriate measures of central tendency and dispersion for the overall cohort and for patients who are adherent and are non-adherent. Model development First, the outcome variable was dichotomized: RAdMAT-NL scores £ 54 = 0 (nonadherent), > 54 = 1 (adherent). Second, logistic regression modelling was used with all candidate predictors. Continuous variables were handled as they had a linear relationship. The categorical variable MRC score was dichotomized (no limitations: 0-2 = 0; and limitations: 3-5 = 1) because patients with MRC 3-5 have limitations of activity due to dyspnea during daily life and are eligible for PR (MRC 0-2 are not) [28]. To create a parsimonious model that can more efficiently be used in clinical practice, variable selection using backward selection was performed with a p-value of > 0.05 for elimination. Bootstrap samples (n = 500) were used in which the
RkJQdWJsaXNoZXIy MjY0ODMw