Case-only methods provide an attractive analytic option for studying the effects of transient exposures on the risk of acute events. These methods are appealing because chronic risk factors that are stable over time within a person cannot confound the analyses. However, these methods remain susceptible to confounding by time-varying factors and time trends in exposure. We propose an extension to existing case-only methods that enhances the ability of researchers to address the issue of confounding from time trends in exposure, and we demonstrate its use in an applied example. The proposed case-case time control design utilizes both within- and between-case comparisons.
We evaluated the performance of this method using simulations. Conducting case-crossover analyses on simulated datasets in which the probability of exposure increased over time resulted in effect estimates that were biased upward by approximately 10%. In our example, applying case-time-control analyses that sampled control person-time from a population not experiencing an increase in exposure over time resulted in similar (biased) estimates as the case-crossover analyses. However, the ability of the case-time-control design to adjust for exposure time trends depends on how well the trend among the cases can be approximated by the control group. The bias observed in any specific study would depend on the suitability of the external control group chosen. In other words, depending on the control group, the magnitude of bias from a case-time-control study could be larger or smaller than that found with a case-crossover analysis. Applying our proposed case-case time control analysis in our simulated example resulted in unbiased effect estimates despite the presence of a strong time trend in exposure. Sampling control person-time from the at-risk period of cases that have not yet occurred minimizes the risk of sampling person-time from an inappropriate control group.
When conducting a case-case-time control study, there are several practical considerations. First, it is important to emphasize that the case-case-time control, like other self-controlled designs, can be used to study only short-term exposures with transient effects on the risk of events with acute onset. This is because exposures occurring during a referent period must not have residual or carry-over effects on risk during the current period. The lag between current and referent period can be based on prior biologic knowledge of the effects of the exposure (e.g., drug pharmacodynamics). Sensitivity analyses should be performed using alternative lags to verify that results are not overly sensitive to these a priori assumptions.
Second, one must consider the duration of study follow-up, taking into account that some proportion of cases will not be able to be matched to controls derived from future cases. This is likely to be particularly true for health outcomes that occur toward the end of the follow-up period, as there are fewer subsequent cases from which to select potential matches. This feature of the study design has important implications for power calculations, in that cases that cannot be matched will not contribute to analyses. Even if most cases can be matched, increasing the number of future cases matched to each case will increase statistical efficiency.
Third, one must consider the permissible lag time between the outcome event for the current case and the outcome event for a matched future-case control. Person-time sampled from future cases needs to be sufficiently far removed in calendar time from the future-case event such that exposure can be reasonably assumed to be independent of the future-case event. If the exposure under investigation is indeed associated with the outcome, sampling person-time too close to the future-case event could lead to bias. On the other hand, person-time should be close enough to the future-case event that the exposure time trend estimated using the sampled person-time provides a good approximation of the exposure time trend for current cases. This consideration is particularly important when time trends in exposure may be non-linear or changing rapidly. Sensitivity analyses exploring alternative lag times between the case and future-case events are recommended.
Fourth, in addition to matching on time, future cases may be matched to current cases on other variables such as age, sex, or location. While matching on multiple factors may enhance validity of estimates, the tradeoff is the potential loss of precision if the number of factors used reduces the number of cases that can successfully be matched to future-cases.
In our applied example, the case-crossover approach produced estimates that indicated an elevated risk of stroke following brief exposure to vitamins, even though we observed no trend in exposure prevalence over calendar time. This biologically implausible result may be explained by a greater propensity for patients to seek treatment as their physical condition deteriorates or as early warning symptoms of stroke manifest. This can result in an increased probability of exposure to a variety of medical treatments in the time leading up to a stroke (i.e. protopathic bias). An increasing propensity to be treated in the time prior to an event is an example of an exposure time trend that can be better estimated using person-time sampled from matched future cases than from an external non-case control group. In this example, after adjusting for the effect of the exposure time trend using person-time sampled from future cases, there was no evidence of an increased risk of stroke following brief exposure to vitamins.
We have demonstrated through simulation study that, in the absence of other time-varying confounders, the case-case time control analysis is able to produce unbiased estimates when exposure prevalence increases monotonically over time. When other temporal or seasonal patterns are suspected, these methods may be adapted to account for additional temporal factors influencing exposure prevalence by borrowing from time-stratified control selection methods, such as those used in environmental epidemiology.3
In their case-only analyses, environmental epidemiologists often use selection strategies that involve matching control periods to case periods on day of week, season, or other temporal factors that may influence exposure. 3,7,10-12
Neither a bi-directional sampling approach nor a self-controlled case-series analysis were included in the comparison of case-only methods because these methods assume that exposure is neither censored or altered subsequent to the occurrence of the outcome. Although there are promising new methods for handling outcomes that censor or alter exposure probability, the computational intensity and assumptions required by these methods may limit their utility. 5,13
In conclusion, case-only analyses can be applied in situations where exposure status during follow-up is time-varying and there is a clear time of onset for the outcome of interest. Their advantages over more traditional cohort and case-control designs become particularly evident when an appropriate comparison group is difficult to identify, or when there are strong, time-invariant confounders that cannot be measured. 1
The within-subject comparisons used by case-only methods implicitly adjust for time-invariant confounding within a person, whether measured or unmeasured. Case-case methods add to previously developed case-only methods by adjusting for temporal changes in exposure prevalence without use of external controls or post-event person-time. Additionally, the case-case time control can reduce the impact of protopathic bias, a bias that can occur when early manifestations or warning signs of a disease lead to exposure. 8