Table 4.

Overview of study findings and expected relationships between cough counts and measures of asthma.

Types of validity		Expected relationships	Statistical method	Findings
Concurrent validity
	Cough counts and FeNO and lung function	Positive association with FeNO; negative association with lung function	Correlation	No significant correlations with FeNO; cough counts were negatively associated with FEV1 and FVC
	Cough counts and symptom diary data and VAS	Positive association	Correlation	Associated with limited activities and approached significance for shortness of breath and number of rescue medications use in the past 24 hours
	Cough counts and asthma control	Negative association	Correlation	Cough counts were negatively associated with asthma control
	Cough counts and quality of life	Negative association	Correlation	Approached significance with quality of life, activity, and symptom subscales
	Cough counts and health care utilization	Positive association	Correlation	No association between cough counts and health care use before the 7-day trial; however, cough counts showed positive association with health care use during the 7-day trial
Predictive validity
	Cough counts and asthma control and quality of life 3 months later	Cough counts predicting asthma control and quality of life	Multiple regression	Coughs predicted asthma control 3 months later explaining 42% of the variance in asthma control. Coughs predicted the quality of life total score and each of subscales 3 months later, explaining variance in quality of life, which ranged from 28% to 41%
	Cough counts and health care utilization 3 months later	Cough counts predicting health care utilization	Multiple regression	Coughs predicted health care utilization 3 months later explaining 76% of the variance in health care utilization
Clinical prediction
	Area under the curve		ROC curve analysis	0.71 (95% CI 0.58-0.84)
	Cutoff point		ROC curve analysis	0.56 (0.83 coughs/hour or 19.92 coughs/day)
	Sensitivity	Discrimination of positive asthma diagnosis by a cutoff	ROC curve analysis	51.3% sensitivity
	Specificity	Discrimination of negative asthma diagnosis by a cutoff	ROC curve analysis	72.7% specificity