Comunicación y acceso a los datos del Registro

After 12 months, the mean reduction in DAS28, estimated from the MI data, was 1.75 units (SE 0.05) and overall, 33% of the variance of change in DAS28 after 12 months was explained by the complete case linear regression analysis (indicated by the adjusted R square value). Table 4-3 shows the results of the linear regression analysis of change in DAS28 after 12 months in which the change in DAS28 for the ‘baseline patient’ (defined in 4.3) was -1.78, consistent with the estimated mean. The effect of gender on change in DAS28 after 12 months almost reached statistical significance: males’ DAS28 reduced by 0.16 units more after 12 months compared to females’ (SE 0.09, p 0.079). One of the independently statistically significant variables in the model was fatigue VAS, also significant in the model of change in DAS28 after 6 months (4.3): each cm of baseline fatigue predicted a 0.03 unit less reduction in DAS28 at 12 months (SE 0.01, 0.023). Pain VAS was also a significant predictor of change in DAS28 after 12 months but its effect depended upon cohort. On average, reduction in DAS28 after 12 months was 0.06 units less per cm of baseline pain VAS for patients in YEAR B (SE 0.03, p 0.039), but for patients in YEAR C, the effect of baseline pain VAS on change in DAS28 was in the opposite direction: each cm of baseline pain VAS was associated with a 0.01 unit fall in DAS28. Thus, the effect of pain VAS was negligible in YEAR C, but it was associated with a slightly lesser reduction in DAS28 for patients in YEAR B. Another significant difference between YEAR B and C patients was found in relation to the effect of EMS category in this model. Although the group of YEAR B patients reporting baseline EMS ≥220 minutes did not have a significantly different change in DAS28 after 12 months compared to those reporting EMS 0- 35 minutes, the effect was different for YEAR C patients, for whom the average fall in DAS28 after 12 months was 0.55 units greater if baseline EMS was ≥220 minutes than if it was 0-35 minutes. The effect of baseline DAS28 on change in DAS28 was also different between the two cohorts. For YEAR B cases, fall in DAS28 was 0.87 units per unit of baseline DAS28, whereas for YEAR C cases, this value was 0.71 per unit of baseline DAS28. As indicated in Table 4-1, mean baseline DAS28 was lower in YEAR C, so this difference may indicate a variable rate of change in DAS28, that is, the change in DAS28 per unit of time

160

may differ depending on baseline DAS28, or cohort, or the rate may slow down or speed up with time. This will be explored further in 4.5, where growth curves of change in DAS28 with time are described.

As for the linear regression model of change in DAS28 after 6 months (Section 4.3), a Wald test was carried out to determine whether the interactions between cohort and different categories of EMS were significant in the MI and complete case models. The results indicated that the interactions were significant in the MI model (F statistic 2.82, df=369, p=0.0389), but not in the model using

complete cases, (F statistic 0.93, df= 515, p=0.4278). Thus, these interactions were retained in the MI model, but not in the analysis of the complete cases and therefore, differences in the coefficients for EMS categories between the 2 types of analysis were expected. Further comparison of the complete case and MI analyses revealed similar coefficients and SE. One of the more marked differences between the 2 analyses is found relating to the coefficients for ACPA, which was a significant predictor in the complete case model (DAS28 reduction 0.32 units less in ACPA positive cases, SE 0.15, p 0.037), but not the MI model (DAS28 reduction 0.17 units less in ACPA positive cases, SE 0.13, p 0.227). ACPA status was the most frequently missing variable in the dataset (46% missing, Chapter 3, Table 3-2 ) and this may contribute to bias in estimates obtained through analysis of the complete cases. The presence of auto antibodies has been linked to worse outcome, especially lower likelihood of remission in RA (reviewed in 1.8.2.3), but the present analysis did not support this finding. The difference between results from the complete case and MI analyses highlights the potential for bias in complete case analysis caused by missing data.

161

Table 4-3 Predictors of change in disease activity from baseline to twelve months in Yorkshire Early Arthritis Register

Multiple imputation analysis N=1416

Complete case analysis N=539 Coefficient (β) SE p Coefficient (β) SE p

Main model effects:

YEAR C -0.19 0.17 0.268 -0.17 0.13 0.172 Male gender -0.16 0.09 0.079 -0.40 0.13 0.002 Age† -0.02 0.03 0.618 -0.40 0.05 0.392 SD -0.00 0.01 0.757 0.01 0.01 0.676 RF positive 0.11 0.12 0.347 0.07 0.16 0.667 ACPA positive 0.17 0.13 0.227 0.32 0.15 0.037 Pain VAS 0.06 0.03 0.041 0.06 0.04 0.098 Fatigue VAS 0.03 0.01 0.023 0.05 0.03 0.023 EMS 40-75 min -0.11 0.17 0.502 0.07 0.16 0.683 90-210 min 0.16 0.16 0.328 0.22 0.17 0.201 ≥220 min 0.27 0.22 0.226 -0.26 0.20 0.180 DAS28 -0.87 0.06 <0.00 01 -0.90 0.09 <0.0001 Interaction effects: Cohort*Pain VAS -0.07 0.04 0.096 -0.10 0.05 0.032 Cohort*EMS: Not included in model 40-75 min 0.18 0.81 0.417 90-210 min -0.01 0.23 0.950 ≥220 min -0.55 0.28 0.058 Cohort*DAS28 0.16 0.08 0.052 0.31 0.11 0.004 Constant -1.78 0.15 <0.00 01 -1.80 0.17 <0.0001

Results of linear regression analysis. Outcome variable was change in DAS28 after 12 months (DAS28 at 12 months – baseline DAS28).

Statistically significant (p<0.05) coefficients are highlighted in bold. †_{Age was entered into the model (as age in years)/10.}

*Indicates an interaction effect between 2 variables. For EMS, the referent category was 0-35 minutes. Independent variables were measured at baseline.

ACPA, anti-citrullinated peptide antibodies; DAS28, disease activity score from counts of 28 joints; EMS, early morning stiffness duration; min, minutes; N, number (of cases); NS, non significant model interaction, where p ≥0.10, therefore not included in the final model; p, probability (statistical significance); RF, rheumatoid factor; SD, symptom duration; SE, standard error; VAS, visual analogue score (measured in centimetres); YEAR, Yorkshire Early Arthritis Register.

162

To assess the fit the linear regression model, a scatterplot of the residuals versus fitted values (Figure 4-3) was inspected and as there was no discernible pattern to the scatterplot, the assumption of homoscedasticity was met. A histogram of the distribution of the residuals (Figure 4-4) showed that they were approximately normally distributed with a mean close to zero and that their range fell within -3.3 to 3.3, which confirmed that there were no outliers.

Figure 4-3 Residual versus fittted plot for the linear regression model of change in Disease Activity Score using counts of 28 joints (DAS28) after 12 months for participants of Yorkshire Early Arthritis Register

Residual values represented the difference between observed change in DAS28 and change in DAS28 predicted by the model, whilst fitted values are those predicted by the model. -4 -2 0 2 4 Re sid u a ls -4 -3 -2 -1 0 1 Fitted values

163

Figure 4-4 Histogram to show distribution of residuals from the linear regression analysis of change in Disease Activity using counts of 28 joints (DAS28) after 12 months in Yorkshire Early Arthritis Register

Residual values represent the difference between actual change in DAS28 and change in DAS28 predicted by the model.

Range of standardised residual values (equal to actual residual divided by its standard error): -2.19 to 2.90