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Aim: To assess the risk of urinary tract infection (UTI) occurrence associated with sodium‐glucose cotransporter‐2 (SGLT2) inhibitor use relative to biguanide use in diabetes in a population‐based cohort study using a target trial emulation framework. Methods: Using a Japanese nationwide administrative claims database, we constructed a cohort of patients aged ≥40 years who were dispensed SGLT2 inhibitors, dipeptidyl peptidase‐4 (DPP‐4) inhibitors or biguanides between April 2014 and March 2015. For computational ease, we randomly sampled 100% of SGLT2 inhibitor users, 3% of DPP‐4 inhibitor users, and 20% of biguanide users; new antidiabetic drug initiators were analysed. We estimated the intention‐to‐treat (ITT) hazard ratios (HRs) of UTI with inverse probability of treatment (IPT)‐weighted Coxʼs proportional hazards models that ignored subsequent treatment changes. Treatment weights were computed using patient sex, age, medications, medical history and hospitalization history. We also estimated per‐protocol (PP) HRs using IPT‐ and inverse probability of censoring‐weighted Coxʼs models that adjusted for nonrandom treatment changes. Results: We analysed 11 364 SGLT2 inhibitor initiators, 9035 DPP‐4 inhibitor initiators, and 10 359 biguanide initiators. When compared with biguanide initiators, SGLT2 inhibitor initiators had a crude HR of 1.14 (95% confidence interval [CI] 1.05‐1.24), an ITT HR of 0.94 (95% CI 0.86‐1.03), and a PP HR of 0.90 (95% CI 0.78‐1.03); and DPP‐4 inhibitor initiators had a crude HR of 1.13 (95% CI 1.04‐1.23), an ITT HR of 0.85 (95% CI 0.77‐0.94), and a PP HR of 0.83 (95% CI 0.71‐0.95). Conclusion: Use of SGLT2 inhibitors or DPP‐4 inhibitors did not increase the risk of UTI compared with biguanide use. Accounting for treatment changes did not substantially influence the estimated effects. [ABSTRACT FROM AUTHOR]

G‐estimation of structural nested models (SNMs) plays an important role in estimating the effects of time‐varying treatments with appropriate adjustment for time‐dependent confounding. As SNMs for a failure time outcome, structural nested accelerated failure time models (SNAFTMs) and structural nested cumulative failure time models have been developed. The latter models are included in the class of structural nested mean models (SNMMs) and are not involved in artificial censoring, which induces several difficulties in g‐estimation of SNAFTMs. Recently, restricted mean time lost (RMTL), which corresponds to the area under a distribution function up to a restriction time, is attracting attention in clinical trial communities as an appropriate summary measure of a failure time outcome. In this study, we propose another SNMM for a failure time outcome, which is called structural nested RMTL model (SNRMTLM) and describe randomized and observational g‐estimation procedures that use different assumptions for the treatment mechanism in a randomized trial setting. We also provide methods to estimate marginal RMTLs under static treatment regimes using estimated SNRMTLMs. A simulation study evaluates finite‐sample performances of the proposed methods compared with the conventional intention‐to‐treat and per‐protocol analyses. We illustrate the proposed methods using data from a randomized controlled trial for cardiovascular disease with treatment changes. G‐estimation of SNRMTLMs is a useful tool to estimate the effects of time‐varying treatments on a failure time outcome. [ABSTRACT FROM AUTHOR]