This is a protocol for a study in qhich we shall seek to generate dose-response curves relating the daily doses of different statins currently licensed for clinical use to their effects in reducing LDL cholesterol, for comparison of calculated ED50 values with the dosages typically used in clinical practice. This will also allow a comparison of the different dosages of different statins that are capable of producing the same LDL-lowering effect.
\n \n\n \n \nThis is a protocol for a study in which we shall seek to generate dose-response curves relating the daily doses of different statins currently licensed for clinical use to their effects in reducing LDL cholesterol, for comparison of calculated ED50 values with the dosages typically used in clinical practice. This will also allow a comparison of the different dosages of different statins that are capable of producing the same LDL-lowering effect.
\n \n\n \n \nObjectives To characterise serum creatinine and urinary protein testing in UK general practices from 2005 to 2013 and to examine how the frequency of testing varies across demographic factors, with the presence of chronic conditions and with the prescribing of drugs for which kidney function monitoring is recommended. Design Retrospective open cohort study. Setting Routinely collected data from 630 UK general practices contributing to the Clinical Practice Research Datalink. Participants 4 573 275 patients aged over 18 years registered at up-to-standard practices between 1 April 2005 and 31 March 2013. At study entry, no patients were kidney transplant donors or recipients, pregnant or on dialysis. Primary outcome measures The rate of serum creatinine and urinary protein testing per year and the percentage of patients with isolated and repeated testing per year. Results The rate of serum creatinine testing increased linearly across all age groups. The rate of proteinuria testing increased sharply in the 2009-2010 financial year but only for patients aged 60 years or over. For patients with established chronic kidney disease (CKD), creatinine testing increased rapidly in 2006-2007 and 2007-2008, and proteinuria testing in 2009-2010, reflecting the introduction of Quality and Outcomes Framework indicators. In adjusted analyses, CKD Read codes were associated with up to a twofold increase in the rate of serum creatinine testing, while other chronic conditions and potentially nephrotoxic drugs were associated with up to a sixfold increase. Regional variation in serum creatinine testing reflected country boundaries. Conclusions Over a nine-year period, there have been increases in the numbers of patients having kidney function tests annually and in the frequency of testing. Changes in the recommended management of CKD in primary care were the primary determinant, and increases persist even after controlling for demographic and patient-level factors. Future studies should address whether increased testing has led to better outcomes.
\n \n\n \n \nBackground: People with diabetes are told that drinking alcohol may increase their risk of hypoglycaemia. Aims: To report the effects of alcohol consumption on glycaemic control in people with diabetes mellitus. Methods: Medline, EMBASE and the Cochrane Library databases were searched in 2015 to identify randomized trials that compared alcohol consumption with no alcohol use, reporting glycaemic control in people with diabetes. Data on blood glucose, HbA1c and numbers of hypoglycaemic episodes were pooled using random effects meta-analysis. Results: Pooled data from nine short-term studies showed no difference in blood glucose concentrations between those who drank alcohol in doses of 16\u201380 g (median 20 g, 2.5 units) compared with those who did not drink alcohol at 0.5, 2, 4 and 24 h after alcohol consumption. Pooled data from five medium-term studies showed that there was no difference in blood glucose or HbA1c concentrations at the end of the study between those who drank 11\u201318 g alcohol/day (median 13 g/day, 1.5 units/day) for 4\u2013104 weeks and those who did not. We found no evidence of a difference in number of hypoglycaemic episodes or in withdrawal rates between randomized groups. Conclusions: Studies to date have not provided evidence that drinking light to moderate amounts of alcohol, with or without a meal, affects any measure of glycaemic control in people with Type 2 diabetes. These results suggest that current advice that people with diabetes do not need to refrain from drinking moderate quantities of alcohol does not need to be changed; risks to those with Type 1 diabetes remain uncertain.
\n \n\n \n \nAims Although there are reports that \u03b2-adrenoceptor antagonists (beta-blockers) and diuretics can affect glycaemic control in people with diabetes mellitus, there is no clear information on how blood glucose concentrations may change and by how much. We report results from a systematic review to quantify the effects of these antihypertensive drugs on glycaemic control in adults with established diabetes. Methods We systematically reviewed the literature to identify randomized controlled trials in which glycaemic control was studied in adults with diabetes taking either beta-blockers or diuretics. We combined data on HbA1c and fasting blood glucose using fixed effects meta-analysis. Results From 3864 papers retrieved, we found 10 studies of beta-blockers and 12 studies of diuretics to include in the meta-analysis. One study included both comparisons, totalling 21 included reports. Beta-blockers increased fasting blood glucose concentrations by 0.64 mmol l-1 (95% CI 0.24, 1.03) and diuretics by 0.77 mmol l-1 (95% CI 0.14, 1.39) compared with placebo. Effect sizes were largest in trials of non-selective beta-blockers (1.33, 95% CI 0.72, 1.95) and thiazide diuretics (1.69, 95% CI 0.60, 2.69). Beta-blockers increased HbA1c concentrations by 0.75% (95% CI 0.30, 1.20) and diuretics by 0.24% (95% CI -0.17, 0.65) compared with placebo. There was no significant difference in the number of hypoglycaemic events between beta-blockers and placebo in three trials. Conclusions Randomized trials suggest that thiazide diuretics and non-selective beta-blockers increase fasting blood glucose and HbA1c concentrations in patients with diabetes by moderate amounts. These data will inform prescribing and monitoring of beta-blockers and diuretics in patients with diabetes.
\n \n\n \n \nStandard methods for meta-analysis of dose\u2013response data in epidemiology assume a model with a single scalar parameter, such as log-linear relationships between exposure and outcome; such models are implicitly unbounded. In contrast, in pharmacology, multi-parameter models, such as the widely used E max model, are used to describe relationships that are bounded above and below. We propose methods for estimating the parameters of a dose\u2013response model by meta-analysis of summary data from the results of randomized controlled trials of a drug, in which each trial uses multiple doses of the drug of interest (possibly including dose 0 or placebo). We assume that, for each randomized arm of each trial, the mean and standard error of a continuous response measure and the corresponding allocated dose are available. We consider weighted least squares fitting of the model to the mean and dose pairs from all arms of all studies, and a two-stage procedure in which scalar inverse-variance meta-analysis is performed at each dose, and the dose\u2013response model is fitted to the results by weighted least squares. We then compare these with two further methods inspired by network meta-analysis that fit the model to the contrasts between doses. We illustrate the methods by estimating the parameters of the E max model to a collection of multi-arm, multiple-dose, randomized controlled trials of alogliptin, a drug for the management of diabetes mellitus, and further examine the properties of the four methods with sensitivity analyses and a simulation study. We find that all four methods produce broadly comparable point estimates for the parameters of most interest, but a single-stage method based on contrasts between doses produces the most appropriate confidence intervals. Although simpler methods may have pragmatic advantages, such as the use of standard software for scalar meta-analysis, more sophisticated methods are nevertheless preferable for their advantages in estimation.
\n \n\n \n \nBackground Various lipid measurements in monitoring/screening programmes can be used, alone or in cardiovascular risk scores, to guide treatment for prevention of cardiovascular disease (CVD). Because some changes in lipids are due to variability rather than true change, the value of lipid-monitoring strategies needs evaluation. Objective To determine clinical value and cost-effectiveness of different monitoring intervals and different lipid measures for primary and secondary prevention of CVD. Data sources We searched databases and clinical trials registers from 2007 (including the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, the Clinical Trials Register, the Current Controlled Trials register, and the Cumulative Index to Nursing and Allied Health Literature) to update and extend previous systematic reviews. Patient-level data from the Clinical Practice Research Datalink and St Luke\u2019s Hospital, Japan, were used in statistical modelling. Utilities and health-care costs were drawn from the literature. Methods In two meta-analyses, we used prospective studies to examine associations of lipids with CVD and mortality, and randomised controlled trials to estimate lipid-lowering effects of atorvastatin doses. Patient-level data were used to estimate progression and variability of lipid measurements over time, and hence to model lipid-monitoring strategies. Results are expressed as rates of true-/false-positive and true-/false-negative tests for high lipid or high CVD risk. We estimated incremental costs per quality-adjusted life-year. Results A total of 115 publications reported strength of association between different lipid measures and CVD events in 138 data sets. The summary adjusted hazard ratio per standard deviation of total cholesterol (TC) to high-density lipoprotein (HDL) cholesterol ratio was 1.25 (95% confidence interval 1.15 to 1.35) for CVD in a primary prevention population but heterogeneity was high (I2\u2009=\u200998%); similar results were observed for non-HDL cholesterol, apolipoprotein B and other ratio measures. Associations were smaller for other single lipid measures. Across 10 trials, low-dose atorvastatin (10 and 20\u2009mg) effects ranged from a TC reduction of 0.92\u2009mmol/l to 2.07\u2009mmol/l, and low-density lipoprotein reduction of between 0.88\u2009mmol/l and 1.86\u2009mmol/l. Effects of 40\u2009mg and 80\u2009mg were reported by one trial each. For primary prevention, over a 3-year period, we estimate annual monitoring would unnecessarily treat 9 per 1000 more men (28 vs. 19 per 1000) and 5 per 1000 more women (17 vs. 12 per 1000) than monitoring every 3 years. However, annual monitoring would also undertreat 9 per 1000 fewer men (7 vs. 16 per 1000) and 4 per 1000 fewer women (7 vs. 11 per 1000) than monitoring at 3-year intervals. For secondary prevention, over a 3-year period, annual monitoring would increase unnecessary treatment changes by 66 per 1000 men and 31 per 1000 women, and decrease undertreatment by 29 per 1000 men and 28 per 1000 men, compared with monitoring every 3 years. In cost-effectiveness, strategies with increased screening/monitoring dominate. Exploratory analyses found that any unknown harms of statins would need utility decrements as large as 0.08 (men) to 0.11 (women) per statin user to reverse this finding in primary prevention. Limitation Heterogeneity in meta-analyses. Conclusions While acknowledging known and potential unknown harms of statins, we find that more frequent monitoring strategies are cost-effective compared with others. Regular lipid monitoring in those with and without CVD is likely to be beneficial to patients and to the health service. Future research should include trials of the benefits and harms of atorvastatin 40 and 80\u2009mg, large-scale surveillance of statin safety, and investigation of the effect of monitoring on medication adherence. Study registration This study is registered as PROSPERO CRD42013003727. Funding The National Institute for Health Research Health Technology Assessment programme.
\n \n\n \n \nObjectives: To assess the diagnostic accuracy of three personal breathalyser devices available for sale to the public marketed to test safety to drive after drinking alcohol. Design: Prospective comparative diagnostic accuracy study comparing two single-use breathalysers and one digital multiuse breathalyser (index tests) to a police breathalyser (reference test). Setting: Establishments licensed to serve alcohol in a UK city. Participants: Of 222 participants recruited, 208 were included in the main analysis. Participants were eligible if they were 18 years old or over, had consumed alcohol and were not intending to drive within the following 6 h. Outcome measures: Sensitivity and specificity of the breathalysers for the detection of being at or over the UK legal driving limit (35 \u03bcg/100 mL breath alcohol concentration). Results: 18% of participants (38/208) were at or over the UK driving limit according to the police breathalyser. The digital multiuse breathalyser had a sensitivity of 89.5% (95% CI 75.9% to 95.8%) and a specificity of 64.1% (95% CI 56.6% to 71.0%). The single-use breathalysers had a sensitivity of 94.7% (95% CI 75.4% to 99.1%) and 26.3% (95% CI 11.8% to 48.8%), and a specificity of 50.6% (95% CI 40.4% to 60.7%) and 97.5% (95% CI 91.4% to 99.3%), respectively. Self-reported alcohol consumption threshold of 5 UK units or fewer had a higher sensitivity than all personal breathalysers. Conclusions: One alcohol breathalyser had sensitivity of 26%, corresponding to false reassurance for approximately one person in four who is over the limit by the reference standard, at least on the evening of drinking alcohol. The other devices tested had 90% sensitivity or higher. All estimates were subject to uncertainty. There is no clearly defined minimum sensitivity for this safetycritical application. We conclude that current regulatory frameworks do not ensure high sensitivity for these devices marketed to consumers for a decision with potentially catastrophic consequences.
\n \n\n \n \nBACKGROUND: The majority of patients with chronic kidney disease are diagnosed and monitored in primary care. Glomerular filtration rate (GFR) is a key marker of renal function, but direct measurement is invasive; in routine practice, equations are used for estimated GFR (eGFR) from serum creatinine. We systematically assessed bias and accuracy of commonly used eGFR equations in populations relevant to primary care. CONTENT: MEDLINE, EMBASE, and the Cochrane Library were searched for studies comparing measured GFR (mGFR) with eGFR in adult populations comparable to primary care and reporting both the Modification of Diet in Renal Disease (MDRD) and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations based on standardized creatinine measurements. We pooled data on mean bias (difference between eGFR and mGFR) and on mean accuracy (proportion of eGFR within 30% of mGFR) using a random-effects inverse-variance weighted metaanalysis. We included 48 studies of 26 875 patients that reported data on bias and/or accuracy. Metaanalysis of within-study comparisons in which both formulae were tested on the same patient cohorts using isotope dilution-mass spectrometry-traceable creatinine showed a lower mean bias in eGFR using CKD-EPI of 2.2 mL/min/1.73 m2 (95% CI, 1.1-3.2; 30 studies; I2 74.4%) and a higher mean accuracy of CKD-EPI of 2.7% (1.6 -3.8; 47 studies; I2 55.5%). Metaregression showed that in both equations bias and accuracy favored the CKD-EPI equation at higher mGFR values. SUMMARY: Both equations underestimated mGFR, but CKD-EPI gave more accurate estimates of GFR.
\n \n\n \n \nPoint-of-care (POC) devices could be used to measure hemoglobin A1c (HbA1c) in the doctors' office, allowing immediate feedback of results to patients. Reports have raised concerns about the analytical performance of some of these devices. We carried out a systematic review and meta-analysis using a novel approach to compare the accuracy and precision of POC HbA1c devices. Medline, Embase and Web of Science databases were searched in June 2015 for published reports comparing POC HbA1c devices with laboratory methods. Two reviewers screened articles and extracted data on bias, precision and diagnostic accuracy. Mean bias and variability between the POC and laboratory test were combined in a meta-analysis. Study quality was assessed using the QUADAS2 tool. Two researchers independently reviewed 1739 records for eligibility. Sixty-one studies were included in the meta-analysis of mean bias. Devices evaluated were A1cgear, A1cNow, Afinion, B-analyst, Clover, Cobas b101, DCA 2000/Vantage, HemoCue, Innovastar, Nycocard, Quo-Lab, Quo-Test and SDA1cCare. Nine devices had a negative mean bias which was significant for three devices. There was substantial variability in bias within devices. There was no difference in bias between clinical or laboratory operators in two devices. This is the first meta-analysis to directly compare performance of POC HbA1c devices. Use of a device with a mean negative bias compared to a laboratory method may lead to higher levels of glycemia and a lower risk of hypoglycaemia. The implications of this on clinical decision-making and patient outcomes now need to be tested in a randomized trial.
\n \n\n \n \nAims/hypothesis: Observational studies suggest that metformin may reduce cancer risk by approximately one-third. We examined cancer outcomes and all-cause mortality in published randomised controlled trials (RCTs). Methods: RCTs comparing metformin with active glucose-lowering therapy or placebo/usual care, with minimum 500 participants and 1-year follow-up, were identified by systematic review. Data on cancer incidence and all-cause mortality were obtained from publications or by contacting investigators. For two trials, cancer incidence data were not available; cancer mortality was used as a surrogate. Summary RRs, 95% CIs and I 2statistics for heterogeneity were calculated by fixed effects meta-analysis. Results: Of 4,039 abstracts identified, 94 publications described 14 eligible studies. RRs for cancer were available from 11 RCTs with 398 cancers during 51,681 person-years. RRs for all-cause mortality were available from 13 RCTs with 552 deaths during 66,447 person-years. Summary RRs for cancer outcomes in people randomised to metformin compared with any comparator were 1.02 (95% CI 0.82, 1.26) across all trials, 0.98 (95% CI 0.77, 1.23) in a subgroup analysis of active-comparator trials and 1.36 (95% CI 0.74, 2.49) in a subgroup analysis of placebo/usual care comparator trials. The summary RR for all-cause mortality was 0.94 (95% CI 0.79, 1.12) across all trials. Conclusions/interpretation: Meta-analysis of currently available RCT data does not support the hypothesis that metformin lowers cancer risk by one-third. Eligible trials also showed no significant effect of metformin on all-cause mortality. However, limitations include heterogeneous comparator types, absent cancer data from two trials, and short follow-up, especially for mortality. \u00a9 2012 Springer-Verlag.
\n \n\n \n \nBackground:The aim was to examine the association between smoking cessation and prognosis in smoking-related cancer as it is unclear that cessation reduces mortality.Methods:In this retrospective cohort study from 1999 to 2013, we assessed the association between cessation during the first year after diagnosis and all-cause and cancer-specific mortality.Results:Of 2882 lung, 757 upper aero-digestive tract (UAT) and 1733 bladder cancer patients 27%, 29% and 21% of lung, UAT and bladder cancer patients quit smoking. In lung cancer patients that quit, all-cause mortality was significantly lower (HR: 0.82 (0.74-0.92), while cancer-specific mortality (HR: 0.89 (0.76-1.04) and death due to index cancer (HR: 0.90 (0.77-1.05) were non-significantly lower. In UAT cancer, all-cause mortality (HR: 0.81 (0.58-1.14), cancer-specific mortality (HR: 0.84 (0.48-1.45), and death due to index cancer (HR: 0.75 (0.42-1.34) were non-significantly lower. There was no evidence of an association between quitting and mortality in bladder cancer. The HRs were 1.02 (0.81-1.30) for all-cause, 1.23 (0.81-1.86) for cancer specific, and 1.25 (0.71-2.20) for death due to index cancer. These showed a non-significantly lower risk in sensitivity analyses.Conclusions:People with lung and possibly UAT cancer who quit smoking have a lower risk of mortality than people who continue smoking.
\n \n\n \n \nPURPOSE Smoking cessation after a diagnosis of lung, bladder, and upper aerodigestive tract cancer appears to improve survival, and support to quit would improve cessation. The aims of this study were to assess how often general practitioners provide active smoking cessation support for these patients and whether physician behavior is influenced by incentive payments. METHODS Using electronic primary care records from the UK Clinical Practice Research Datalink, 12,393 patients with incident cases of cancer diagnosed between 1999 and 2013 were matched 1 to 1 to patients with incident cases of coronary heart disease (CHD) diagnosed during the same time. We assessed differences in the proportion for whom physicians updated smoking status, advised quitting, and prescribed cessation medications, as well as the proportion of patients who stopped smoking within a year of diagnosis. We further examined whether any differences arose because the physicians were offered incentives to address smoking in patients with CHD and not cancer. RESULTS At diagnosis, 32.0% of patients with cancer and 18.2% of patients with CHD smoked tobacco. Patients with cancer were less likely than patients with CHD to have their general practitioners update smoking status (OR = 0.18; 95% CI, 0.17-0.19), advise quitting (OR = 0.38; 95% CI, 0.36-0.40), or prescribe medication (OR = 0.67; 95% CI, 0.63-0.73), and they were less likely to have stopped smoking (OR = 0.76; 95% CI, 0.69-0.84). One year later 61.7% of patients with cancer and 55.4% with CHD who were smoking at diagnosis were still smoking. Introducing incentive payments was associated with more frequent interventions, but not for patients with CHD specifically. CONCLUSIONS General practitioners were less likely to support smoking cessation in patients with cancer than with CHD, and patients with cancer were less likely to stop smoking. This finding is not due to the difference in incentive payments.
\n \n\n \n \nObjective: To systematically review studies quantifying the associations of long term (clinic), mid-term (home), and short term (ambulatory) variability in blood pressure, independent of mean blood pressure, with cardiovascular disease events and mortality. Data sources: Medline, Embase, Cinahl, and Web of Science, searched to 15 February 2016 for full text articles in English. Eligibility criteria for study selection: Prospective cohort studies or clinical trials in adults, except those in patients receiving haemodialysis, where the condition may directly impact blood pressure variability. Standardised hazard ratios were extracted and, if there was little risk of confounding, combined using random effects meta-analysis in main analyses. Outcomes included all cause and cardiovascular disease mortality and cardiovascular disease events. Measures of variability included standard deviation, coefficient of variation, variation independent of mean, and average real variability, but not night dipping or day-night variation. Results: 41 papers representing 19 observational cohort studies and 17 clinical trial cohorts, comprising 46 separate analyses were identified. Long term variability in blood pressure was studied in 24 papers, mid-term in four, and short-term in 15 (two studied both long term and short term variability). Results from 23 analyses were excluded from main analyses owing to high risks of confounding. Increased long term variability in systolic blood pressure was associated with risk of all cause mortality (hazard ratio 1.15, 95% confidence interval 1.09 to 1.22), cardiovascular disease mortality (1.18, 1.09 to 1.28), cardiovascular disease events (1.18, 1.07 to 1.30), coronary heart disease (1.10, 1.04 to 1.16), and stroke (1.15, 1.04 to 1.27). Increased mid-term and short term variability in daytime systolic blood pressure were also associated with all cause mortality (1.15, 1.06 to 1.26 and 1.10, 1.04 to 1.16, respectively). Conclusions: Long term variability in blood pressure is associated with cardiovascular and mortality outcomes, over and above the effect of mean blood pressure. Associations are similar in magnitude to those of cholesterol measures with cardiovascular disease. Limited data for mid-term and short term variability showed similar associations. Future work should focus on the clinical implications of assessment of variability in blood pressure and avoid the common confounding pitfalls observed to date.
\n \n\n \n \nBACKGROUND: Competing risks occur when populations may experience outcomes that either preclude or alter the probability of experiencing the main study outcome(s). Many standard survival analysis methods do not account for competing risks. We used mortality risk in people with diabetes with and without albuminuria as a case study to investigate the impact\u00a0of competing risks on measures of absolute and relative risk. METHODS: A population with type 2 diabetes was identified in Clinical Practice Research Datalink as part of a historical cohort study. Patients were followed for up to 9\u00a0years. To quantify differences in absolute risk estimates of cardiovascular and cancer, mortality standard (Kaplan-Meier) estimates were compared to competing-risks-adjusted (cumulative incidence competing risk) estimates. To quantify differences in measures of association, regression coefficients for the effect of albuminuria on the relative hazard of each outcome were compared between standard cause-specific hazard (CSH) models (Cox proportional hazards regression) and two competing risk models: the unstratified Lunn-McNeil model, which estimates CSH, and the Fine-Gray model, which estimates subdistribution hazard (SDH). RESULTS: In patients with normoalbuminuria, standard and competing-risks-adjusted estimates for cardiovascular mortality were 11.1% (95% confidence interval (CI) 10.8-11.5%) and 10.2% (95% CI 9.9-10.5%), respectively. For cancer mortality, these figures were 8.0% (95% CI 7.7-8.3%) and 7.2% (95% CI 6.9-7.5%). In patients with albuminuria, standard and competing-risks-adjusted estimates for cardiovascular mortality were 21.8% (95% CI 20.9-22.7%) and 18.5% (95% CI 17.8-19.3%), respectively. For cancer mortality, these figures were 10.7% (95% CI 10.0-11.5%) and 8.6% (8.1-9.2%). For the effect of albuminuria on cardiovascular mortality, regression coefficient values from multivariable standard CSH, competing risks CSH, and competing risks SDH models were 0.557 (95% CI 0.491-0.623), 0.561 (95% CI 0.494-0.628), and 0.456 (95% CI 0.389-0.523), respectively. For the effect of albuminuria on cancer mortality, these values were 0.237 (95% CI 0.148-0.326), 0.244 (95% CI 0.154-0.333), and 0.102 (95% CI 0.012-0.192), respectively. CONCLUSIONS: Studies of absolute risk should use methods that adjust for competing risks to avoid over-stating risk, such as the CICR estimator. Studies of relative risk should consider carefully which measure of association is most appropriate for the research question.
\n \n\n \n \nBackground: Type 2 (T2) diabetes and diabetic nephropathy are risk factors for a range of vascular and neoplastic outcomes. The competing nature of these outcomes means that the use of standard survival analysis techniques (like Kaplan-Meier (KM) curves and Cox proportional hazards (PH) regression) could introduce bias in risk estimates. The literature was reviewed to determine whether authors accounted for competing risks (CRs) when reporting time-to-event analyses in populations with T2 diabetes and nephropathy.\n\nMethods: Medline, EMBASE and CINAHL databases were searched for studies with \u22655 years follow up reporting vascular or neoplastic outcomes in patients with T2 diabetes and nephropathy.\n\nResults: The search returned 3,886 abstracts. Of these, 179 studies were eligible for full-text review, with 29 papers eligible for data extraction. No study accounted for CRs. Analyses liable to incorporate bias through ignoring competing outcomes were present in 21 studies, of which: all studies used Cox-PH regression, 6 studies used KM curves and 2 studies produced cumulative incidence estimates from Cox-PH analysis. The remaining 8 studies did not use time-to-event analyses.\n\nConclusions: Studies investigating cardiovascular or neoplastic outcomes in populations with DN fail to account for CRs in their analyses. Using standard analyses when populations are at increased risk of multiple outcomes is liable to introduce an unknown level of bias in risk estimates. Absolute risk estimates from KM and Cox-PH regression are well known to be susceptible to this, while many have identified relative risk measured from Cox-PH regression as also being susceptible.
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