Comparing MSMs to standard analysis methods

The results from the models using MSMs with IPTW and also joint IPTW and IPCW weights, were compared with results using standard analysis methods that in theory would not correctly adjust for time-dependent confounders affected by prior treatment. There was an a priori belief based on existing literature that covariates such as BMI, HbA1c, and other measures of diabetes severity satisfied the definition of time-dependent confounders affected by prior treatment. In addition, initial analyses (although univariate) suggested that there were associations between these covariates and cancer in our data (7.3.1). However, the MSMs produced results that were similar to those obtained via standard analysis methods. The standard pooled logistic regression model with time updated exposure and full baseline adjustment estimated an HR for metformin use of 0.96 (0.87 – 1.06) for covariate specification A, compared to 0.97 (0.84 – 1.13) for the MSM with joint IPTW and IPCW weights. None of the varying levels of adjustment for confounding via standard analysis methods produced noticeably different results.

An obvious reason for the lack of difference is that the post baseline confounding between initiation of metformin and cancer incidence is not as strong as initially hypothesised. One reason for this could be that not enough patients were initiating treatment far enough away from baseline for the values of the confounders to change sufficiently to make post-baseline confounding apparent. The analysis in chapter 6 suggested that the median time to treatment after study entry was 2 months. Considering HbA1c is a measure of long term glucose control over approximately 3 months, this means that the post baseline confounding would at most affect around 50% of the population. In the analysis of cumulative medication, the differences in estimates for > 7 years exposure between standard methods and MSM with both IPTW and IPCW were more noticeable, which supports the idea that longer follow up may be needed for the time-dependent confounding to become apparent.

HbA1c was by far the strongest predictor of treatment initiation through time, which is unsurprising due to the UK diabetes treatment guidelines being primarily based on observed HbA1c levels. However, for there to be significant confounding, there must be clear association between HbA1c and risk of cancer. Review articles examining diabetes and cancer risk overall

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[19, 20] suggest that the epidemiological evidence for hyperglycaemia and cancer risk indicates an association, but not necessarily causality. They suggest for example, that in non-insulin deficient situations, hyperglycaemia is a proxy indicator for hyperinsulemia, which is a more plausible causal risk factor. However, they also acknowledge that many cancers require glucose for energy, so a causal association should not be completely disregarded. This suggests that HbA1c should certainly be considered as a confounder, and therefore requires the use of MSMs to model time updated exposure. In a practical sense however, the necessity of the adjustment (in both the MSM and standard analysis) then depends on the strength of the association. Two meta-analyses, one in clinical trial data and another using observational research provide possible quantifications of the effect of hyperglycaemia on cancer risk. A meta-analysis of safety data from clinical trials in patients with T2DM comparing intensive vs normal glucose control were suggestive of a marginal decrease in risk of cancer incidence with tighter glucose control, albeit with a relatively wide confidence interval, pooled HR (0.91, 0.79-1.05) [204]. In overweight patients with T2DM, there was larger decrease in risk of cancer mortality with intensive glucose control (HR 0.74, 0.37-1.48). Although the authors are cautious and overall suggest there is no evidence of an increased risk of cancer with poor glucose control, their estimates do not exclude it. A larger meta-analysis of 14 studies looking at various site specific analyses reported associations between raised HbA1c and increased risk of cancer that were much larger in magnitude [205]. However, the quality of the included studies has not been thoroughly examined and the same issues of time-dependent confounding may also be relevant to these studies. Based on these findings, it is certainly clear that HbA1c should be considered a potential time-dependent confounder, however for the MSM to be beneficial over standard methods, the association must be apparent in the data. In the data used for this analysis, there was some evidence of a univariate association between current HbA1c and risk of cancer, though the increase in risk was relatively consistent around a 10% increase for all HbA1c categories vs 6% (Table 7.4). Overall, if the effect of raised HbA1c on cancer risk is only around a 10% increased risk, it is quite possible that when taking into consideration factors such as measurement error and frequency of HbA1c measurements in CPRD, the effect of adjusting for HbA1c as a time- dependent confounder via use of MSMs may not have a prominent effect on the overall estimate of risk. However, with the current literature providing wide-ranging estimates for the actual association, it was necessary and useful to explore the effect that use of MSMs may have had on the estimated risk.

Similarly, BMI has been shown to be associated with risk of cancer [195, 196, 206] . Therefore, this was an important potential time-dependent confounder. Original guidelines suggested that

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metformin should be prescribed to more overweight patients, due to its weight reduction properties, and in terms of the secondary analysis, that sulfonylureas should be prescribed more cautiously in overweight patients due to the potential for it causing further weight gain. However, only about 4% of the population studied were diagnosed with T2DM before the year 2000, beyond which BMI was less clearly associated with choice of treatment. Therefore, although it can still be useful to adjust for in the model, it may have made less of a difference between the standard methods and MSMs than initially hypothesised. Another issue may be that the association between BMI and risk of cancer differs for different cancer types [195, 196]. By combining all cancers into a single outcome, differing effects of BMI on different cancers combine in ways that are hard to predict. This could have reduced additional benefit of adjustment for confounding by BMI (both baseline and time dependent). The slightly larger observed changes between standard models and MSMs in the site specific analysis support this possibility, though the imprecision of these analyses limits interpretation of these differences.