To evaluate the effect of age on “hemorrhagic reactions,” the reports were stratified into the following age groups: 0-59 years and more than 60 years. According to the definition of the World Health Organization (WHO) of the United Nations, elderly people are those who are aged 65 years or more.
Using established pharmacovigilance indices 23 (link), we evaluated the ROR to establish the effects of DOACs on “hemorrhagic reactions.” “Cases” were defined as patients who reported “hemorrhagic reactions,” while “non-cases” consisted of patients associated with all other reports. The ROR is the ratio of the odds of reporting ARs versus all other reactions associated with DOACs compared with the reporting odds for all other drugs present in the database. To compare the “cases” and “non-cases,” we calculated the RORs as (a:c) / (b:d). The RORs were expressed as point estimates with a 95% confidence interval (CI). The signal was considered positive if the lower limit of 95% CI was > 1 and the reported number was ≥ 2 36 (link).
The use of ROR allows adjustment using multiple logistic regression analysis and provides the advantage of controlling covariates 37 (link),38 (link). In this analysis, the results were refined by dedicated correction to detect confounding factors that may be present in the database. We calculated the adjusted ROR to control the covariates using the multiple logistic regression analysis. The report was stratified according to age as follows: 0-59- and ≥ 60-year-old group. To construct a multiple logistic model that coded report year, sex, stratified age group, and drug, the following multiple logistic model was used for analysis:
(Y = reporting year, S = sex, A = stratified age group, and D = drug (apixaban, rivaroxaban, edoxaban, and dabigatran)).
The adjusted ROR was calculated using the 0-59-year-old group as the control group. The effectiveness of explanatory variables was evaluated using a stepwise method with a significance level of 0.05 (forward, and backward) 27 (link),28 (link). Using the likelihood ratio test, the influence of explanatory variables was evaluated. As the difference of -2log likelihood follows chi-square distribution with one degree of freedom, the results with p ≤ 0.05 were considered statistically significant. Data analysis was performed using JMP software version 12.0 (SAS Institute Inc., Cary, NC, USA).
Time-to-onset duration was calculated from the time of a patient's first prescription to the occurrence of hemorrhagic reactions. The records with completed AR occurrence and prescription start date were used for the time-to-onset analysis. It was necessary to consider right truncation when evaluating the time-to-onset of ARs. We determined an analysis period of 365 days after the start of administration. The median duration, quartiles, and Weibull shape parameters (WSPs) were used to evaluate the time-to-onset data. The scale parameter α of Weibull distribution determines the scale of the distribution function. A larger scale value (α) stretches the distribution, whereas a smaller scale value (α) shrinks data distribution. The WSP β of Weibull distribution determines the shape of distribution function. Larger and smaller shape values produce left- and right-skewed curves, respectively. The shape parameter β of Weibull distribution was used to indicate the level of hazard over time without a reference population. When β is equal to 1, the hazard is estimated to be constant over time. If β is greater than 1 and 95% CI of β excluded the value 1, the hazard was considered to increase with time 30 (link),31 (link),39 (link). The time-to-onset analysis was performed using JMP software version 12.0 (SAS Institute Inc., Cary, NC, USA).
Using established pharmacovigilance indices 23 (link), we evaluated the ROR to establish the effects of DOACs on “hemorrhagic reactions.” “Cases” were defined as patients who reported “hemorrhagic reactions,” while “non-cases” consisted of patients associated with all other reports. The ROR is the ratio of the odds of reporting ARs versus all other reactions associated with DOACs compared with the reporting odds for all other drugs present in the database. To compare the “cases” and “non-cases,” we calculated the RORs as (a:c) / (b:d). The RORs were expressed as point estimates with a 95% confidence interval (CI). The signal was considered positive if the lower limit of 95% CI was > 1 and the reported number was ≥ 2 36 (link).
The use of ROR allows adjustment using multiple logistic regression analysis and provides the advantage of controlling covariates 37 (link),38 (link). In this analysis, the results were refined by dedicated correction to detect confounding factors that may be present in the database. We calculated the adjusted ROR to control the covariates using the multiple logistic regression analysis. The report was stratified according to age as follows: 0-59- and ≥ 60-year-old group. To construct a multiple logistic model that coded report year, sex, stratified age group, and drug, the following multiple logistic model was used for analysis:
(Y = reporting year, S = sex, A = stratified age group, and D = drug (apixaban, rivaroxaban, edoxaban, and dabigatran)).
The adjusted ROR was calculated using the 0-59-year-old group as the control group. The effectiveness of explanatory variables was evaluated using a stepwise method with a significance level of 0.05 (forward, and backward) 27 (link),28 (link). Using the likelihood ratio test, the influence of explanatory variables was evaluated. As the difference of -2log likelihood follows chi-square distribution with one degree of freedom, the results with p ≤ 0.05 were considered statistically significant. Data analysis was performed using JMP software version 12.0 (SAS Institute Inc., Cary, NC, USA).
Time-to-onset duration was calculated from the time of a patient's first prescription to the occurrence of hemorrhagic reactions. The records with completed AR occurrence and prescription start date were used for the time-to-onset analysis. It was necessary to consider right truncation when evaluating the time-to-onset of ARs. We determined an analysis period of 365 days after the start of administration. The median duration, quartiles, and Weibull shape parameters (WSPs) were used to evaluate the time-to-onset data. The scale parameter α of Weibull distribution determines the scale of the distribution function. A larger scale value (α) stretches the distribution, whereas a smaller scale value (α) shrinks data distribution. The WSP β of Weibull distribution determines the shape of distribution function. Larger and smaller shape values produce left- and right-skewed curves, respectively. The shape parameter β of Weibull distribution was used to indicate the level of hazard over time without a reference population. When β is equal to 1, the hazard is estimated to be constant over time. If β is greater than 1 and 95% CI of β excluded the value 1, the hazard was considered to increase with time 30 (link),31 (link),39 (link). The time-to-onset analysis was performed using JMP software version 12.0 (SAS Institute Inc., Cary, NC, USA).
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