The Importance of Making Assumptions in Bias Analysis

Quantitative bias analyses allow researchers to adjust for uncontrolled confounding, given specification of certain bias parameters. When researchers are concerned about unknown confounders, plausible values for these bias parameters will be difficult to specify. Ding and VanderWeele developed bounding factor and E-value approaches that require the user to specify only some of the bias parameters. We describe the mathematical meaning of bounding factors and E-values and the plausibility of these methods in an applied context. We encourage researchers to pay particular attention to the assumption made, when using E-values, that the prevalence of the uncontrolled confounder among the exposed is 100% (or, equivalently, the prevalence of the exposure among those without the confounder is 0%). We contrast methods that attempt to bound biases or effects and alternative approaches such as quantitative bias analysis. We provide an example where failure to make this distinction led to erroneous statements. If the primary concern in an analysis is with known but unmeasured potential confounders, then E-values are not needed and may be misleading. In cases where the concern is with unknown confounders, the E-value assumption of an extreme possible prevalence of the confounder limits its practical utility

Weight change over 9 years and subsequent risk of venous thromboembolism in the ARIC cohort

Background/objectives: Weight gain increases risk of cardiovascular disease, but has not been examined extensively in relationship to venous thromboembolism (VTE). The association between weight change over 9 years and subsequent VTE among participants in the Atherosclerosis Risk in Communities (ARIC) study was examined, with a hypothesis that excess weight gain is a risk factor for VTE, relative to no weight change. Subjects/methods: Quintiles of 9-year weight change were calculated (visit 4 1996–1998 weight minus visit 1 1987–1989 weight in kg: Quintile 1: ≥−1.81 kg; Quintile 2: 1.36 to ≤4.08 kg; Quintile 4: >4.08 to ≤7.71 kg; Quintile 5: >7.71 kg). Incident VTEs from visit 4 (1996–1998) through 2015 were identified and adjudicated using medical records. Hazard ratios (HRs) were calculated using Cox models. Results: 529 incident VTEs were identified during an average of 19 years of follow up. Compared to Quintile 2, participants in Quintile 5 of weight change had 1.46 times the rate of incident VTE (HR = 1.46 (95% CI 1.09, 1.95), adjusted for age, race, sex, income, physical activity, smoking, and prevalent CVD). The HR for Quintile 5 was modestly attenuated to 1.38 (95% CI 1.03, 1.84) when visit 1 BMI was included in the model. When examined separately, results were significant for unprovoked VTE, but not for provoked VTE. Among those obese at visit 1, both weight gain (HR 1.86 95% CI 1.27, 2.71) and weight loss (HR 2.11 95% CI 1.39, 3.19) were associated with incident VTE, compared with normal-weight participants with no weight change. Conclusions: Weight gain later life was associated with increased risk for unprovoked VTE. Among those with obesity, both weight gain and weight loss were associated with increased risk for VTE

Physical activity and lifetime risk of cardiovascular disease and cancer

Purpose Although the World Health Organization has recommended moderate- to vigorous-intensity physical activity (MVPA) to prevent cardiovascular disease (CVD) and some cancers, there are no estimates of lifetime risk of these noncommunicable diseases according to PA levels. We aimed to estimate the lifetime risk of CVD and cancers according to PA levels. Methods We followed 5807 men and 7252 women in the United States, 45-64 yr old, initially free of CVD and cancer from 1987 through 2012, and used a life table approach to estimate lifetime risks of CVD (coronary heart disease, heart failure, and stroke) and total cancer according to PA levels: poor (0 min·wk -1 of MVPA), intermediate (1-74 min·wk -1 of VPA or 1-149 min·wk -1 of MVPA), or recommended (≥75 min·wk -1 of VPA or ≥150 min·wk -1 of MVPA). Results During the 246,886 person-years of follow-up, we documented 4065 CVD and 3509 cancer events and 2062 non-CVD and 2326 noncancer deaths. In men, the lifetime risks of CVD from 45 through 85 yr were 52.7% (95% confidence interval = 49.4-55.5) for poor PA and 45.7% (42.7-48.3) for recommended PA. In women, the respective lifetime risks of CVD were 42.4% (39.5-44.9) and 30.5% (27.5-33.1). Lifetime risks of total cancer were 40.1% (36.9-42.7) for poor PA and 42.6% (39.7-45.2) for recommended activity in men and 31.4% (28.7-33.8) and 30.4% (27.7-32.9), respectively, in women. Conclusions Compared with a poor PA level, the PA recommended by the World Health Organization was associated with lower lifetime risk of CVD, but not total cancer, in both men and women

Controversy and Debate: Questionable utility of the relative risk in clinical research: Paper 2: Is the Odds Ratio “portable” in meta-analysis? Time to consider bivariate generalized linear mixed model

Objectives: A recent paper by Doi et al. advocated completely replacing the relative risk (RR) with the odds ratio (OR) as the effect measure in clinical trials and meta-analyses with binary outcomes. Besides some practical advantages of RR over OR, Doi et al.’s key assumption that the OR is “portable” in the meta-analysis, that is, study-specific ORs are likely not correlated with baseline risks, was not well justified. Study designs and settings: We summarized Spearman's rank correlation coefficient between study-specific ORs and baseline risks in 40,243 meta-analyses from the Cochrane Database of Systematic Reviews. Results: Study-specific ORs tend to be higher in studies with lower baseline risks of disease for most meta-analyses in Cochrane Database of Systematic Reviews. Using an actual meta-analysis example, we demonstrate that there is a strong negative correlation between OR (RR or RD) with the baseline risk and the conditional effects notably vary with baseline risks. Conclusions: Replacing RR or RD with OR is currently unadvisable in clinical trials and meta-analyses. It is possible that no effect measure is “portable” in a meta-analysis. In addition to the overall (or marginal) effect, we suggest presenting the conditional effect based on the baseline risk using a bivariate generalized linear mixed model

Stroke Incidence and Survival in American Indians, Blacks, and Whites: The Strong Heart Study and Atherosclerosis Risk in Communities Study

Background: American Indians (AIs) have high stroke morbidity and mortality. We compared stroke incidence and mortality in AIs, blacks, and whites. Methods and Results: Pooled data from 2 cardiovascular disease cohort studies included 3182 AIs from the SHS (Strong Heart Study), aged 45 to 74 years at baseline (1988–1990) and 3765 blacks and 10 413 whites from the ARIC (Atherosclerosis Risk in Communities) Study, aged 45 to 64 years at baseline (1987–1989). Stroke surveillance was based on self-report, hospital records, and death certificates. We estimated hazard ratios for incident stroke (ischemic and hemorrhagic combined) through 2008, stratified by sex and birth-year tertile, and relative risk for poststroke mortality. Incident strokes numbered 282 for AIs, 416 for blacks, and 613 for whites. For women and men, stroke incidence among AIs was similar to or lower than blacks and higher than whites. Covariate adjustment resulted in lower hazard ratios for most comparisons, but results for these models were not always statistically significant. After covariate adjustment, AI women and men had higher 30-day poststroke mortality than blacks (relative risk=2.1 [95% CI=1.0, 3.2] and 2.2 [95% CI=1.3, 3.1], respectively), and whites (relative risk=1.6 [95% CI=0.8, 2.5] and 1.7 [95% CI=1.1, 2.4]), and higher 1-year mortality (relative risk range=1.3–1.5 for all comparisons). Conclusions: Stroke incidence in AIs was lower than for blacks and higher than for whites; differences were larger for blacks and smaller for whites after covariate adjustment. Poststroke mortality was higher in AIs than blacks and whites

Odds Ratios are far from “portable” — A call to use realistic models for effect variation in meta-analysis

Objective: Recently Doi et al. argued that risk ratios should be replaced with odds ratios in clinical research. We disagreed, and empirically documented the lack of portability of odds ratios, while Doi et al. defended their position. In this response we highlight important errors in their position. Study design and setting: We counter Doi et al.’s arguments by further examining the correlations of odds ratios, and risk ratios, with baseline risks in 20,198 meta-analyses from the Cochrane Database of Systematic Reviews. Results: Doi et al.’s claim that odds ratios are portable is invalid because 1) their reasoning is circular: they assume a model under which the odds ratio is constant and show that under such a model the odds ratio is portable; 2) the method they advocate to convert odds ratios to risk ratios is biased; 3) their empirical example is readily-refuted by counter-examples of meta-analyses in which the risk ratio is portable but the odds ratio isn't; and 4) they fail to consider the causal determinants of meta-analytic inclusion criteria: Doi et al. mistakenly claim that variation in odds ratios with different baseline risks in meta-analyses is due to collider bias. Empirical comparison between the correlations of odds ratios, and risk ratios, with baseline risks show that the portability of odds ratios and risk ratios varies across settings. Conclusion: The suggestion to replace risk ratios with odds ratios is based on circular reasoning and a confusion of mathematical and empirical results. It is especially misleading for meta-analyses and clinical guidance. Neither the odds ratio nor the risk ratio is universally portable. To address this lack of portability, we reinforce our suggestion to report variation in effect measures conditioning on modifying factors such as baseline risk; understanding such variation is essential to patient-centered practice