Did your real-world study follow STROBE guidelines?

Is your observational research study following best practices? Is your methodology transparent? To help answer these questions, the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network created the STROBE guidelines. The STROBE guidelines–an acronym for The Strengthening the Reporting of Observational Studies in Epidemiology–aim to improve the transparency of the methods behind observational…

When (and how) to use population-adjusted indirect comparisons?

Population-adjusted indirect comparisons (PAICs) include both matching-adjusted indirect comparisons (MAICs) and Simulated treatment comparisons (STCs). The key data requirement for these methods is that they have individual patient data (IPD) from at least one clinical trial. This means the methods are most useful for studied funded by the clinical trial sponsor or when IPD clinical…

Near/far matching

What is better: propensity score matching or instrumental variables? How about both? That is basically what is proposed using a near/far matching approach as described in Baiocchi et al. (2012). In this paper, they use a two step approach to examine the causal affect of adopting a new treatment–carotid arterial stents (CAS)–versus and older treatment–carotid…

Dealing with missing data

If you are doing a cost-effectiveness analysis (CEA) that relies on clinical trial data, what should you do if there is missing data in the trial? A paper by Faria et al. (2014) helps to provide the answer. The first question is, how are the data missing. There are a few options for defining this…