Table 5.

Logistic regression model of intervention effects on follow-up PEM status

Model Fit			Χ²(7) = 33.736, p < .001
Model Fit			AIC = 189.780, pseudo-R² = .269
Parameters	log Odds	SE	95% CI	Wald Χ²	Odds Ratio	p

Intercept	−0.373	0.741	−1.818, 1.115	−0.503	0.688	.153
Baseline PEM	1.540	0.373	0.823, 2.289	4.131	4.664	<.001^***
Age	−0.023	0.019	−0.060, 0.013	−1.242	0.977	.214
Gender^a	−0.993	0.595	−2.220, 0.144	−1.668	0.371	.095
Race/Ethnicity^a	0.757	0.436	−0.090, 1.627	1.738	2.132	.082
Symp. Onset^a	0.343	0.420	−0.474, 1.182	0.815	1.409	.415
Months Diag.	0.000	0.002	−0.004, 0.005	0.131	1.000	.896
Treatment^a	−0.705	0.372	−1.448, 0.018	−1.895	0.494	.058

Note. The model converged after four Fisher iterations. SE = Standard error. 95% CI = 95% Wald Confidence interval. PEM = post-exertional malaise. Symp. Onset = mode of symptom onset. Months Diag. = months since diagnosis at baseline.

Gender, race/ethnicity, mode of symptom onset, treatment, and PEM are dummy-coded dichotomous variables; index values (i.e., 1) are as follows: female, non-Hispanic White, gradual, highPEM, V-CBSM

p < .050

^**

p ≤ .010

^***

p ≤ .001