The absence of head-to-head trials is a common challenge in comparative effectiveness research and health technology assessment. Indirect cross-trial treatment comparisons are possible, but can be biased by cross-trial differences in patient characteristics. Using only published aggregate data, adjustment for such biases may be impossible. Although individual patient data (IPD) would permit adjustment, they are rarely available for all trials. However, many researchers have the opportunity to access IPD for trials of one treatment, a new drug for example, but only aggregate data for trials of comparator treatments. We propose a method that leverages all available data in this setting by adjusting average patient characteristics in trials with IPD to match those reported for trials without IPD. Treatment outcomes, including continuous, categorical and censored time-to-event outcomes, can then be compared across balanced trial populations. The proposed method is illustrated by a comparison of adalimumab and etanercept for the treatment of psoriasis. IPD from trials of adalimumab versus placebo (n = 1025) were re-weighted to match the average baseline characteristics reported for a trial of etanercept versus placebo (n = 330). Re-weighting was based on the estimated propensity of enrolment in the adalimumab versus etanercept trials. Before matching, patients in the adalimumab trials had lower mean age, greater prevalence of psoriatic arthritis, less prior use of systemic treatment or phototherapy, and a smaller mean percentage of body surface area affected than patients in the etanercept trial. After matching, these and all other available baseline characteristics were well balanced across trials. Symptom improvements of ≥75% and ≥90% (as measured by the Psoriasis Area and Severity Index [PASI] score at week 12) were experienced by an additional 17.2% and 14.8% of adalimumab-treated patients compared with the matched etanercept-treated patients (respectively, both p < 0.001). Mean percentage PASI score improvements from baseline were also greater for adalimumab than for etanercept at weeks 4, 8 and 12 (all p < 0.05). Matching adjustment ensured that this indirect comparison was not biased by differences in mean baseline characteristics across trials, supporting the conclusion that adalimumab was associated with significantly greater symptom reduction than etanercept for the treatment of moderate to severe psoriasis.

In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine pharmaco-vigilance. In 2003, a group of statisticians, pharmaco-epidemiologists and pharmaco-vigilance professionals from the pharmaceutical industry and the US FDA formed the Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools to review best practices for the use of these methods.In this paper, we provide an overview of: (i) the statistical and operational attributes of several currently used methods and their strengths and limitations; (ii) information about the characteristics of various postmarketing safety databases with which these tools can be deployed; (iii) analytical considerations for using safety data-mining methods and interpreting the results; and (iv) points to consider in integration of safety data mining with traditional pharmaco-vigilance methods. Perspectives from both the FDA and the industry are provided. Data mining is a potentially useful adjunct to traditional pharmaco-vigilance methods. The results of data mining should be viewed as hypothesis generating and should be evaluated in the context of other relevant data. The availability of a publicly accessible global safety database, which is updated on a frequent basis, would further enhance detection and communication about safety issues.

A principle concern of pharmacovigilance is the timely detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of this process is the scientific acumen of the pharmacovigilance domain expert. There is understandably an interest in developing database screening tools to assist human reviewers in identifying associations worthy of further investigation (i.e., signals) embedded within a database consisting largely of background 'noise' containing reports of no substantial public health significance. Data mining algorithms are, therefore, being developed, tested and/or used by health authorities, pharmaceutical companies and academic researchers. After a focused review of postapproval drug safety signal detection, the authors explain how the currently used algorithms work and address key questions related to their validation, comparative performance, deployment in naturalistic pharmacovigilance settings, limitations and potential for misuse. Suggestions for further research and development are offered.

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The persistence rate of ICS is poor. Preventing early treatment discontinuation may be important to ensure maximal benefit from ICS treatment.

Background Recent studies have raised concerns about potential increased cardiovascular (CV) risk in type 2 diabetes patients treated with some peroxisome proliferator-activated receptor gamma (PPAR-gamma) agonists. Objective To ascertain the risk of hospitalization for acute myocardial infarction (AMI) in type 2 diabetes patients treated with pioglitazone relative to rosiglitazone. Methodology Using data covering 2003-2006 from a large health care insurer in the US, a retrospective cohort study was conducted in patients who initiated treatment with pioglitazone or rosiglitazone. The hazard ratio (HR) of incident hospitalization for AMI after initiation of treatment with these drugs was estimated from multivariate Cox's proportional hazards survival analysis; similarly, the HR was ascertained for hospitalization for the composite endpoint of AMI or coronary revascularization (CR). Results A total of 29 911 eligible patients were identified in the database; 14 807 in the pioglitazone and 15 104 in the rosiglitazone group. Baseline demographics, medical history, and dispensed medications were generally well balanced between groups. The unadjusted HR for hospitalization for AMI was 0.82, 95%CI: 0.67-1.01. After adjustment for baseline covariates the HR was 0.78, 95%CI: 0.63-0.96. The adjusted HR for the composite of AMI or CR was 0.85, 95%CI: 0.75-0.98. Conclusion This retrospective cohort study showed that pioglitazone, in comparison with rosiglitazone, is associated with a 22% relative risk reduction of hospitalization for AMI in patients with type 2 diabetes. KEY POINTS pioglitazone, in comparison to rosiglitazone, is associated with a 22% relative risk reduction of myocardial infarction. pioglitazone, in comparison to rosiglitazone, is associated with a 15% relative risk reduction of the composite endpoint of myocardial infarction or coronary revascularization.

ObjectiveTo compare the efficacy and safety of a concentrated formulation of insulin glargine (Gla-300) with other basal insulin therapies in patients with type 2 diabetes mellitus (T2DM).DesignThis was a network meta-analysis (NMA) of randomised clinical trials of basal insulin therapy in T2DM identified via a systematic literature review of Cochrane library databases, MEDLINE and MEDLINE In-Process, EMBASE and PsycINFO.Outcome measuresChanges in HbA1c (%) and body weight, and rates of nocturnal and documented symptomatic hypoglycaemia were assessed.Results41 studies were included; 25 studies comprised the main analysis population: patients on basal insulin-supported oral therapy (BOT). Change in glycated haemoglobin (HbA1c) was comparable between Gla-300 and detemir (difference: −0.08; 95% credible interval (CrI): −0.40 to 0.24), neutral protamine Hagedorn (NPH; 0.01; −0.28 to 0.32), degludec (−0.12; −0.42 to 0.20) and premixed insulin (0.26; −0.04 to 0.58). Change in body weight was comparable between Gla-300 and detemir (0.69; −0.31 to 1.71), NPH (−0.76; −1.75 to 0.21) and degludec (−0.63; −1.63 to 0.35), but significantly lower compared with premixed insulin (−1.83; −2.85 to −0.75). Gla-300 was associated with a significantly lower nocturnal hypoglycaemia rate versus NPH (risk ratio: 0.18; 95% CrI: 0.05 to 0.55) and premixed insulin (0.36; 0.14 to 0.94); no significant differences were noted in Gla-300 versus detemir (0.52; 0.19 to 1.36) and degludec (0.66; 0.28 to 1.50). Differences in documented symptomatic hypoglycaemia rates of Gla-300 versus detemir (0.63; 0.19to 2.00), NPH (0.66; 0.27 to 1.49) and degludec (0.55; 0.23 to 1.34) were not significant. Extensive sensitivity analyses supported the robustness of these findings.ConclusionsNMA comparisons are useful in the absence of direct randomised controlled data. This NMA suggests that Gla-300 is also associated with a significantly lower risk of nocturnal hypoglycaemia compared with NPH and premixed insulin, with glycaemic control comparable to available basal insulin comparators.

The observed differences between vendors could partially be explained by their differing methods of data cleaning and transformation as well as by the specific features of individual algorithms. The choices of vendors and available data mining configurations maximize the exploratory capacity of data mining, but they also raise questions about the claimed objectivity of data mining results and can make data mining exercises susceptible to confirmation bias given the exploratory nature of data mining in pharmacovigilance. When reporting results, the vendor and all data mining configuration details should be specified.