Tandard Cox model stratified by suppression episode and with time reset
Tandard Cox model stratified by suppression episode and with time reset to zero at the beginning of each new episode (see [25]). However viral rebound is interval censored because it is only known to have occurred at some point between one measurement and the next. The standard Cox model is known to underestimateYoung et al. BMC Infectious Diseases (2015) 15:Page 3 ofhazard ratios when measurement error is added to event times [26]. In our first analyses, blips were categorised by magnitude as low (50?99 copies/mL), medium (200?99 copies/mL) or high (500?99 copies/mL) [2]. Indicator variables were used to represent these categories for the first blip per episode; these indicator values were set when a first blip occurred and remained constant until the end of the episode. In subsequent analyses, we represented blip magnitude by a single continuous variable, scaled per 100 copies/mL. In some analyses, this variable was updated to reflect the magnitude of the latest blip in an episode (rather than the first blip) or the cumulative value of blips in an episode to date, or we added another time dependent variable representing the number of blips in the episode to date. All analyses used the same set of covariates: gender, injection drug use as the most likely mode of infection, age PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28388412 at the beginning of the PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/26104484 suppression episode, the year the suppression episode began, the assay used to measure the blip, cART categories and CD4 cell count. These last two covariates were updated whenever their values changed within a suppression episode. We did not censor patients if they stopped taking cART because such censoring could be informative; rather we included a category for ‘no cART’. These covariates were all used in an earlier study [2] except current CD4 cell count which we added to our model because this is a strong predictor of HIV progression even in patients with a suppressed viral load [27]. CD4 cell count was represented by a linear spline with a knot at 200 cells/L and scaled per 100 cells/L [27]. To estimate the effect of covariates on the predictive value of a blip, we added appropriate interaction terms to our analyses (rather than carry out Vesatolimod web separate analyses for different values of a covariate) [28]. All models were fit in SAS 9.3. Model parameters are reported as the estimated hazard ratio (HR) and its 95 confidence interval (95 CI).boosted protease inhibitor (PI, 47 ) or a non-nucleoside reverse transcriptase inhibitor (NNRTI, 42 ), but the latter was less common in subsequent episodes (30 ). The rate of blips was 8.7 per 100 person years in first suppression episodes and 12.3 per 100 person years in subsequent suppression episodes. The rate of viral rebound was 5.6 per 100 person years in first suppression episodes and 10.6 per 100 person years in subsequent suppression episodes. In first suppression episodes, 19 of 785 rebounds were preceded by a blip; in subsequent suppression episodes, 22 of 695 rebounds were preceded by a blip. Of the 2035 blips recorded, 84 , 12 and 4 were of low, medium and high magnitude respectively. The time between a blip and the next viral load measurement decreased with increasing blip magnitude: a median of 2.8, 1.9 and 1.5 months for low, medium and high magnitude blips respectively. A change in cART between a blip and the next viral load measurement was more likely as blip magnitude increased: 2.4 , 4.6 and 6.1 of low, medium and high magnitude blips, respectively, were followed by.