3. Materials and Methods
3.7 Predictors and Covariates
3.7.4 Covariates
Participants reported history of tobacco smoking as never, former and current and also in terms of number of cigarettes per day and alcohol use in number of drinks per day. The presence of traditional cardiovascular risk factors at baseline was based on a self-reported history of physician-diagnosed hypertension, diabetes mellitus, high cholesterol, as well as self-reported use of medications for hypertension, diabetes mellitus, or high cholesterol. History of chronic disease occurrence for self- reported conditions was confirmed via confirmation medical records for a small random sample of SCCS participants. Total physical activity was measured as total metabolic equivalent-hours per day spent doing light, moderate and strenuous occupational/household work and moderate and vigorous exercise.
3.7.4.1 Challenges related to covariate measurements 3.7.4.1.1 Determination of covariates at baseline
Demographic, anthropometric, and cardiovascular risk factors will be determined by self-report of a physician diagnosis and use of medications (for traditional cardiovascular risk factors). While self- report may be susceptible to recall and misclassification bias, these methods have been successfully used and validated in large epidemiologic cohorts. Many of the questions on the SCCS questionnaire were adapted from questionnaires used and validated in other settings, and a series of independent validation studies using biomarkers, repeat interviews or medical records have demonstrated the reliability of the questionnaire within the SCCS population for variables such as tobacco use status, self-reported diseases including diabetes, height and weight.
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3.7.4.1.2 Handling changes in covariates over time
Another major challenge we’re faced with is the temporal variations in the values of certain covariates during the follow-up period particularly smoking status, diabetes status and body weight.
Ignoring these secular variations in the values of these key covariates could be a potential source of bias of our effect estimates and the direction of bias can be difficult to predict. One option would be to model these variables as time-varying covariates (TVCs) in our multivariable Cox Models. There is follow-up data for close to 60% of the SCCS cohort. If this proportion is the same for our current sub-cohort (n = 27078, identified based on our inclusion criteria for the current analyses) we could perform time- dependent analyses for close to 16,000 persons.
However this poses another conundrum; namely: how do we guard against any potential selection bias if those with covariate data for these key time-varying covariates differ meaningfully from those without ( n ~ 10,000) on other important covariates such demographics and other lifestyle and clinical factors? Also, given our goals of investigating differences in measures of frequency (incidence rates) across groups defined by race and sex as well as examining potential departures from additivity of effects by race and sex, an analyses restricted to this smaller sample may lack sufficient power. Striking an optimum bias-precision trade-off in this approach would be crucial. One way of boosting the power and precision of these supplemental analyses using TVCs would be to perform multiple imputation of
covariate values for study participants without covariate data at follow-up, if the data can be considered to be “missing at random”. By utilizing multiple imputation in an attempt to “preserve” information we would simultaneously reduce bias in estimates and provide estimates of the variance-covariance estimates of beta-hat penalized for imputation [127].
Another important consideration in our supplemental analysis would be the selection of the functional form for modelling smoking status in relation to post-HF survival. As seen with previous studies between smoking status and survival different options e.g. using a step function for “current smoking” that is updated at every time point (1 if yes, 0 if no) or using a time-lagged variable could yield
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different results [129]. In the Coronary Artery Surgery Study (CASS) in which patients with mild angina were randomized to medical treatment or CABG, data on smoking status were collected every 6 months, and, for the first analysis, a step function (with 6 months intervals) was used [130]. Much to the surprise of the investigators, whilst not statistically significant, the estimated effect of current smoking on survival was positive i.e. protective. A closer look at the patient’s smoking histories revealed that most patients who died were in fact smokers but many had quit smoking at the last follow-up before their death. In several instances this was apparently explainable by hospitalization for a myocardial infarction or congestive heart failure and other compelling health reasons that prompted smoking cessation at last follow-up. So if smokers with a high risk of death quit just before dying we may have ended up with a scenario similar to confounding by indication. By using time-lagged covariates or percentage of time during follow-up for which the subject smoked the investigators found an inverse relationship (as
expected) between smoking and survival. In order to adequately model the confounding effect of smoking in our proposed supplemental analyses, similar approaches would have to be employed.
Similarly, study participants could likely experience weight gains (or loss) during follow-up suggesting it may be appropriate to model weight-height index using TVCs vis-a-vis its relationship with post-HF mortality. However, persons who are subsequently diagnosed with HF may experience some unintentional weight loss before HF diagnosis (and lower BMI has been found to be associated with lower post-HF survival – the “obesity paradox”). Hence, would it be appropriate to utilize their weight measurements during follow-up that may be taken just before HF diagnosis? On the other hand if the baseline measure is used, would that be a true measure of the exposure experience for persons whose HF is diagnosed several years later after enrollment? It may be argued that both approaches have trade-offs that have to be weighed-up against each other.
Also we would have to give careful thought to our working hypothesis of the relationships between our time varying covariates as well as their relationship with HF incidence. For example let’s examine the hypothesized relationship between smoking, body weight (or weight-height index) and heart
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failure risk. First, at baseline there could be an association between smoking status and weight-height index, based on a priori epidemiologic evidence suggesting weight loss among smokers. Second, persons who have lost weight while smoking may subsequently decide to either a) smoke even more in order to foster more weight loss or b) quit; having achieved their goal. The association could be conceived as shown below:
If these assumptions of the associations between these variables are true, then smoking status at baseline could be considered as a confounder of the association between W/Hn and HF risk; while smoking status at follow-up would be considered a mediator hence the term “time-varying confounder- mediator” in some statistics literature. Adjusting for only the baseline or follow-up values of cigarette smoking will lead to biased estimates. One option would be to use marginal structural models to estimate the effect of weight-height index on HF risk by appropriate control for the effects of time-dependent confounders. In these models, the predictor – outcome association is estimated in a regression model that is weighted using the inverse probability of treatment weights [131].
3.7.4.1.3 Residual Confounding and Unmeasured Confounders
The unique design of the SCCS cohort with participants who are of similar (and generally low) SES regardless of race or sex, as well as the well-documented nature of the information on socioeconomic
Smoking Status at baseline
Heart Failure Smoking Status
at follow-up Weight-height
Index (W/Hn)
Other Covariates
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characteristics that can be additionally controlled for statistically, would mitigate potential confounding by socioeconomic factors of differences in HF risk and post-HF survival by race and sex.
However there is always the possibility of residual confounding due to unmeasured factors. One such unmeasured factor is the treatment for HF; the latter not be available via linkage with the Medicare part A/B carrier and outpatient base files. As an important predictor of heart failure survival, if there are unobserved differential patterns in HF treatment between groups defined by race and sex, these could confound our findings on the patterns of post-HF survival. Importantly, even if HF therapy didn’t differ across levels of any of our predictors of interest, being a strong predictor of the outcome, failing to adjust for it moves our hazard ratios towards the null.