Background The Women’s Health Initiative randomized trial found greater coronary heart disease (CHD) risk in women assigned to estrogen/progestin therapy than in those assigned to placebo. Observational studies had previously suggested reduced CHD risk in hormone users. Methods Using data from the observational Nurses’ Health Study, we emulated the design and intention-to-treat (ITT) analysis of the randomized trial. The observational study was conceptualized as a sequence of “trials” in which eligible women were classified as initiators or noninitiators of estrogen/progestin therapy. Results The ITT hazard ratios (95% confidence intervals) of CHD for initiators versus noninitiators were 1.42 (0.92 – 2.20) for the first 2 years, and 0.96 (0.78 – 1.18) for the entire follow-up. The ITT hazard ratios were 0.84 (0.61 – 1.14) in women within 10 years of menopause, and 1.12 (0.84 – 1.48) in the others (P value for interaction = 0.08). These ITT estimates are similar to those from the Women’s Health Initiative. Because the ITT approach causes severe treatment misclassification, we also estimated adherence-adjusted effects by inverse probability weighting. The hazard ratios were 1.61 (0.97 – 2.66) for the first 2 years, and 0.98 (0.66 – 1.49) for the entire follow-up. The hazard ratios were 0.54 (0.19 – 1.51) in women within 10 years after menopause, and 1.20 (0.78 – 1.84) in others (P value for interaction = 0.01). Finally, we also present comparisons between these estimates and previously reported NHS estimates. Conclusions Our findings suggest that the discrepancies between the Women’s Health Initiative and Nurses’ Health Study ITT estimates could be largely explained by differences in the distribution of time since menopause and length of follow-up.

This article reviews methods to estimate treatment effectiveness research using observational data. The basic idea is using an observational study to emulate a hypothetical randomised trial by comparing initiators vs. non-initiators of treatment. After adjustment for baseline confounders, one can estimate the analogue of the intention-to-treat effect. We also explain two approaches to adjust for imperfect adherence using the per-protocol and as-treated analyses after adjusting for measured time-varying confounding and selection bias using inverse probability weighting of marginal structural models. As an example, we implemented these methods to estimate the effect of statins for primary prevention of coronary heart disease (CHD) using data from electronic medical records in the United Kingdom. Despite strong confounding by indication, our approach detected a potential benefit of statin therapy. The analogue of the intention-to-treat hazard ratio of CHD was 0.89 (0.73, 1.09) for statin initiators vs. noninitiators. The hazard ratio of CHD was 0.84 (0.54, 1.30) in the per-protocol analysis and 0.79 (0.41, 1.41) in the as-treated analysis for 2-years of use vs. no use. In contrast, a conventional comparison of current users vs. never users of statin therapy resulted in a hazard ratio of 1.31 (1.04, 1.66). We provide a flexible and annotated SAS program to implement the proposed analyses.

The increasing availability of large healthcare databases is fueling an intense debate on whether real-world data should play a role in the assessment of the benefit-risk of medical treatments. In many observational studies, for example, statin users were found to have a substantially lower risk of cancer than in meta-analyses of randomized trials. While such discrepancies are often attributed to a lack of randomization in the observational studies, they may be explained by flaws that can be avoided by explicitly emulating a target trial. Using the electronic health records of 733,804 UK adults, we emulated a target trial of statins and cancer and compared our estimates with those obtained using previously applied analytic approaches. Over the 10-year follow-up, 28,408 individuals developed cancer. Under the target trial approach, estimated observational analogs of intention-to-treat and per-protocol 10-year cancer-free survival differences were-0.5% (95% CI-1.0%, 0.0%) and-0.3% (95% CI-1.5%, 0.5%), respectively. By contrast, previous analytic approaches yielded estimates that appeared strongly protective. Our findings highlight the importance of explicitly emulating a target trial to reduce bias in the effect estimates derived from observational analyses.

Recommended Citation: Cain, Lauren E.; Robins, James M.; Lanoy, Emilie; Logan, Roger; Costagliola, Dominique; and Hernán, Miguel A. (2010) an approach for using observational data to emulate randomized clinical trials that compare dynamic regimes of the form "initiate treatment within a certain time period of some time-varying covariate first crossing a particular threshold." We applied this method to data from the French Hospital database on HIV (FHDH-ANRS CO4), an observational study of HIV-infected patients, in order to compare dynamic regimes of the form "initiate treatment within m months after the recorded CD4 cell count first drops below x cells/mm 3 " where x takes values from 200 to 500 in increments of 10 and m takes values 0 or 3. We describe the method in the context of this example and discuss some complications that arise in emulating a randomized experiment using observational data.

Objective-To estimate the effect of combined antiretroviral therapy (cART) on mortality among HIV-infected individuals after appropriate adjustment for time-varying confounding by indication.Design-A collaboration of 12 prospective cohort studies from Europe and the United States (the HIV-CAUSAL Collaboration) that includes 62,760 HIV-infected, therapy-naïve individuals followed for an average of 3.3 years. Inverse probability weighting of marginal structural models was used to adjust for measured confounding by indication. Conclusions-We estimated that cART halved the average mortality rate in HIV-infected individuals. The mortality reduction was greater in those with worse prognosis at the start of follow-up. Results

Importance Cancer patients who use statins appear to have a substantially better survival than non-users in observational studies. However, this inverse association between statin use and mortality in cancer patients may be due to selection bias and immortal time bias. Objective We used observational data to emulate a randomized trial of statin initiation that is free of selection bias and immortal time bias. Design We used data on 17,372 cancer patients from the SEER-Medicare 2007-2009 database with complete follow-up until 2011. Individuals were duplicated, with each replicate assigned to either the strategy “statin initiation within 6 months of diagnosis” or “no statin initiation”. Replicates were censored when they stopped following their assigned strategy, and the potential selection bias was adjusted for via inverse-probability weighting. Hazard ratios (HR), cumulative incidences, and risk differences were calculated for all-cause mortality and cancer-specific mortality. We then compared our estimates with those obtained using the same analytic approaches as in previous observational studies. Setting The SEER-Medicare data is a linkage between 17 American cancer registries and claims files from Medicare and Medicaid in 12 states. Participants We included individuals with a new diagnosis of colorectal, breast, prostate, and bladder cancer who had not been prescribed statins for at least 6 months before cancer. Exposure Statin initiation within 6 months from cancer diagnosis. Main outcome measure Cancer-specific and all-cause mortality. Results The adjusted HR (95% CI) comparing statin initiation vs. no initiation was 1.00 (0.88-1.15) for cancer-specific mortality and 1.07 (0.93-1.21) for overall mortality. Cumulative incidence curves for each group were almost overlapping. On the contrary, the methods used by prior studies resulted in a strong inverse association between statin use and mortality. Conclusion and relevance After using methods that are not susceptible to selection bias from prevalent users and to immortal time bias, initiation of statins within 6 months of cancer diagnosis did not appear to improve 3-year cancer-specific or overall survival.

Deformation by (liD-pencil glide has been analyzed by an upper-bound model which combines a least-shear analysis and Piehler's maximum virtual work analysis. The least-shear analysis gives exact solutions if three 01D slip systems are active, while the maximum work analysis provides solutions for the case of four active slip systems. Independent checks are used to determine which solution method is appropriate. , Computer calculations using this model have been made to determine; (1) the orientation dependence of the Taylor factor for axisymmetric deformation; (2) the yield loci for textured materials having [100], [110] and [11 I] sheet metals and rotational symmetry; (3) the isotropic yield locus for randomly oriented materials; and (4) flow stresses along critical loading paths for various assumed textures with rotational symmetry. The latter calculations indicate that anisotropic yield loci of textured bcc metals with rotational symmetry are much better approximated by o-xa +orya + RJorx-Ory] a =(R + I)Y awhere R is the strain ratio and Y is the tensile yield strength with an exponent a = 6 rather than with a = 2 as postulated by Hill. It is not known how well upper-bound calculations like these represent actual yielding behavior. NOTATIONx, y

Summary We propose an approach to conduct mediation analysis for survival data with time-varying exposures, mediators, and confounders. We identify certain interventional direct and indirect effects through a survival mediational g-formula and describe the required assumptions. We also provide a feasible parametric approach along with an algorithm and software to estimate these effects. We apply this method to analyze the Framingham Heart Study data to investigate the causal mechanism of smoking on mortality through coronary artery disease. The estimated overall 10-year all-cause mortality risk difference comparing “always smoke 30 cigarettes per day” versus “never smoke” was 4.3 (95 % CI = (1.37, 6.30)). Of the overall effect, we estimated 7.91% (95% CI: = 1.36%, 19.32%) was mediated by the incidence and timing of coronary artery disease. The survival mediational g-formula constitutes a powerful tool for conducting mediation analysis with longitudinal data.