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Background Case-crossover studies have been widely used in various fields including pharmacoepidemiology. Vines and Farrington indicated in 2001 that when within-subject exposure dependency exists, conditional logistic regression can be biased. However, this bias has not been well studied. Methods We have extended findings by Vines and Farrington to develop a weighting method for the case-crossover study which removes bias from within-subject exposure dependency. Our method calculates the exposure probability at the case period in the case-crossover study which is used to weight the likelihood formulae presented by Greenland in 1999. We simulated data for the population with a disease where most patients receive a cyclic treatment pattern with within-subject exposure dependency but no time trends while some patients stop and start treatment. Finally, the method was applied to real-world data from Japan to study the association between celecoxib and peripheral edema and to study the association between selective serotonin reuptake inhibitor (SSRI) and hip fracture in Australia. Results When the simulated rate ratio of the outcome was 4.0 in a case-crossover study with no time-varying confounder, the proposed weighting method and the Mantel-Haenszel odds ratio reproduced the true rate ratio. When a time-varying confounder existed, the Mantel-Haenszel method was biased but the weighting method was not. When more than one control period was used, standard conditional logistic regression was biased either with or without time-varying confounding and the bias increased (up to 8.7) when the study period was extended. In real-world analysis with a binary exposure variable in Japan and Australia, the point estimate of the odds ratio (around 2.5 for the association between celecoxib and peripheral edema and around 1.6 between SSRI and hip fracture) by our weighting method was equal to the Mantel-Haenszel odds ratio and stable compared with standard conditional logistic regression. Conclusion Case-crossover studies may be biased from within-subject exposure dependency, even without exposure time trends. This bias can be identified by comparing the odds ratio by the Mantel-Haenszel method and that by standard conditional logistic regression. We recommend using our proposed method which removes bias from within-subject exposure dependency and can account for time-varying confounders.

Purpose: The safety of nonsteroidal anti-inflammatory drugs (NSAIDs) commonly used in Asia-Pacific countries has had limited study. We assessed the risk of hospitalization for gastrointestinal events with loxoprofen and mefenamic acid compared with other NSAIDs in Asia-Pacific populations. Methods: We conducted a cohort study using a distributed network with a common data model in Australia, Hong Kong, Japan, Korea, and Taiwan. We included patients who initiated diclofenac, loxoprofen, mefenamic acid, or celecoxib and followed them until their first gastrointestinal hospitalization, switch or discontinuation of medication, disenrollment, or end of database coverage. We used Cox proportional hazards models to assess hospitalization risk. Results: We identified 9879 patients in Japan, 70 492 in Taiwan, 263 741 in Korea, and 246 in Hong Kong who initiated an NSAID, and 44 013 patients in Australia, a predominantly Caucasian population. The incidence of gastrointestinal hospitalization was 25.6 per 1000 person-years in Japan, 32.8 in Taiwan, 11.5 in Korea, 484.5 in Hong Kong, and 35.6 in Australia. Compared with diclofenac, the risk of gastrointestinal events with loxoprofen was significantly lower in Korea (hazards ratio, 0.37; 95% CI, 0.25-0.54) but not in Japan (1.65; 95% CI, 0.47-5.78). The risk of gastrointestinal events with mefenamic acid was significantly lower in Taiwan (0.45; 95% CI, 0.26-0.78) and Korea (0.11; 95% CI, 0.05-0.27) but not Hong Kong (2.16; 95% CI, 0.28-16.87), compared with diclofenac. Conclusions: Compared with diclofenac, loxoprofen was associated with a lower risk of gastrointestinal hospitalizations in Korea and mefenamic acid with a lower risk in Taiwan and Korea Refereed/Peer-reviewed

Background Prescription sequence symmetry analysis (PSSA) is a signal detection method for adverse drug events. Its capacity to consistently detect adverse drug events across different settings has not been tested. We aimed to determine the consistency of PSSA results for detecting positive and negative control adverse drug events across different settings. Methods Using a distributed network model, we analyzed prescription dispensing data using PSSA in Australia, Hong Kong, Japan, Korea, and Taiwan. Positive control was amiodarone and thyroxine, as a marker of amiodarone-induced hypothyroidism, a known adverse event with a clear temporal relationship to amiodarone initiation. Negative controls were amiodarone and allopurinol, as a marker of amiodarone-induced gout and thyroxine and allopurinol, as a marker of thyroxine-induced gout. Gout is not recorded as an adverse event in product information for either medicine. Adjusted sequence ratios (ASR) were calculated for each country. Pooled estimates were obtained by using the generic inverse variance method. Results A positive association was identified between amiodarone and thyroxine in all settings with a pooled ASR 2.63 (95% confidence interval (CI) 1.47–4.72). Temporal analysis showed the effect occurred within the first few weeks of treatment. No significant associations were found for the negative controls in any setting; pooled ASR were 0.76 (95%CI 0.62–0.93) and 0.98 (95%CI 0.85–1.12) for amiodaroneallopurinol and thyroxine-allopurinol, respectively. Conclusion Despite different health settings, different populations, and different patterns of medicine utilization, PSSA gave consistent estimates across countries for a well-known positive association and two negative control adverse events. © 2015 The Authors Pharmacoepidemiology and Drug Safety Published by John Wiley & Sons Ltd. key words—prescription symmetry analysis; pharmacovigilance; amiodarone; hyperthyroidism; pharmacoepidemiology