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Table 3 LINEAR MODELS, variable- oriented hypotheses

From: Analyzing repeated data collected by mobile phones and frequent text messages. An example of Low back pain measured weekly for 18 weeks

Research question Outcome Method of analysis Results from the model data set
All respondents, n = 244 Highly compliant respondents, answering 80% (≥15/18 weeks), n = 161
4A: Association of baseline variables with outcome Weekly recorded pain days, count variable, assuming a binominal distribution Multilevel mixed-effects logistic regression or generalized estimating equation assuming a logit link function (Long previous duration reference category) Subject specific OR = 3.31 (95% CI: 2.1-5.1) Population average OR = 1.96 (95% CI 1.4-2.6) (Note: Interaction duration*week significant) Subject specific OR = 2.67 (95% CI: 1.6-4.5) Population average OR =1.52 (95% CI 1.1- 2.2) (Note: Interaction duration*week significant)
4B: Association of baseline variables with outcome Weekly recorded pain days, count variable, assuming a Poisson distribution Multilevel mixed-effects Poisson regression assuming a log link function (Long previous duration reference category) Subject specific IRR = 1.92 (95% CI: 1.5 – 2.4) (Note: Interaction duration*week significant) Subject specific IRR = 1.82 (95% CI: 1.4 – 2.4) (Note: Interaction duration*week significant)
4 C: Association of baseline variables with outcome Weekly recorded pain days, considered a count variable and assuming a normal distribution Generalized linear regression or mixed linear model assuming an identity link function Average difference in pain days for Long duration – Short duration 1.20 (95% CI: 0.8 – 1.5) (Note: Interaction duration*week significant) Average difference in pain days for Long duration –Short duration 0.95 (95% CI: 0.6-1.4) Note: Interaction duration*week significant)