Confounding

discussed, in part due to the relatively high frequency of unspecified codes for the underlying cause of death.²²³ In paper III, 31(3.6% of a total mortality of 846 deaths (including TC death)) were registered with ill-defined or missing causes of death. Additionally, older patients usually suffer from comorbidity and several causes of death may thus be plausible, and autopsies are rarely done.

Since 50 % of all deaths in the Scandinavian countries happen after age of 80 years, this is an important reason for why the quality of the NCoDR is questionable.²¹⁷ For younger patients, the cause of death is often clear-cut, and autopsy rates are quite high. Errors on cause of death might lead to non-differential misclassification. However, it is possible that a previous medical history of cancer will lead to erroneous use of a cancer diagnosis on the death-certificate when in fact the cause of death is unknown, and this will lead to differential misclassification.

Surveillance bias occurs when exposed individuals are subject to a closer surveillance, and as a result, the detection of the study outcome is more likely in the exposed.²¹⁵ In cohort studies, surveillance bias can be considered as a kind of information bias, but it may also be considered a selection bias depending on study design (i.e. case-control study). In paper I, the SC risk after surgery only was highest within the first 10+1years of follow-up, suggesting influence of

surveillance bias. Further, no increase of mortality was observed after surgery in paper III, possibly due to early detection However, in the same manner we would then expect the SC risk after PBCT and RT to be increased within the first 10 years of follow up. As this was not the case, we regard that the increased SC risk after surgery was not the product of surveillance bias. In paper II, surveillance bias might have contributed to the majority of second TCs being diagnosed as stage I.

5.1.4 Confounding

Confounding occurs when the association between an exposure and an outcome is influenced by a third variable (a confounding variable or confounder).²¹⁵ Thus, an alternative explanation exists for the observed association. A confounding variable must be causally associated with the outcome and also non-causally or causally associated with the exposure, but it is not an intermediate variable in the causal pathway between exposure and outcome (Figure 9). Accordingly, to identify a

confounding variable, expert knowledge regarding pathophysiological and clinical mechanisms is required. The association between exposure and outcome might be induced, strengthened,

weakened or eliminated by the confounding variable.²¹⁵

52

Figure 9. The definition of confounding. Adapted from Szklo et al.²¹⁵

Note: A unidirectional arrow indicates a causal relationship and a bidirectional arrow indicates a noncausal relationship.

Once identified, the presence of confounding variables can be managed in two ways: at the planning stage through study design or at the analytical stage through statistical correctional methods. The study design can reduce and to some extent also help avoid confounding.

Confounding is much more likely to happen in observational (i.e. cohort studies) than experimental epidemiological studies (i.e. randomized controlled trials).²¹⁵ In the latter, the process of

randomization, if successful, will produce two groups that are supposed to be similar regarding known confounding factors. In observational study designs, the idea of matching has been

introduced as a strategy to try to reduce the effect of confounding factors. Matching is commonly used in case-control studies where cases and controls are picked in a manner so that they are alike regarding confounding factors.²¹⁵ Matching is however infrequently used in cohort studies, the main reasons being the large size of most cohort studies and that a multitude of variables regarding exposures and outcomes often are investigated in the same study. Confounding in cohort studies are thus better dealt with at the analytical stage. For this to be feasible, the study must be carefully planned so that possible confounding factors can be accounted for. When a confounding variable is suspected, it can be further assessed and its effect reduced by various statistical techniques like stratification or adjustment in various regression methods, generating corrected or adjusted estimates.^214,215

Confounder

Exposure

Outcome

53

Overall, in papers I and III, increasing age is the single most important risk factor for the outcomes (SCs and mortality).¹ In paper II, age is also associated with second TC risk.²³

Additionally, age is associated with the exposure in our study, as those treated with RT (seminomas) are generally older than those treated with PBCT (the majority of which are

nonseminomas). Accordingly, age is an important confounding factor in our study. The effect of age on the association was controlled for in all the relevant statistical analyses in the three papers.

SIRs and SMRs are methods of indirect age adjustment, and in Cox regression, age was always included in the models. Additionally, in paper II, we stratified on a dichotomized age variable.

Adverse health behaviors like smoking, alcohol abuse, physical inactivity and an unhealthy diet are, as mentioned in chapter 1.6.7, associated with increased cancer incidence and premature mortality.^182-185 As much as 35% of excess SC risk is presumably related to modifiable lifestyle factors like smoking and alcohol.¹⁸⁶ In addition to lifestyle risk factors,183,225-227 coronary heart disease is also associated with modifiable risk factors like hypertension and hyperlipidemia.^228,229 An important limitation of our study is the lack of information on lifestyle and coronary heart disease risk factors for all participants. Thus, the potential confounding effect of such risk factors on our results cannot be assessed or adjusted for using available statistical methods. The most

important lifestyle risk factor related to both SC and mortality is smoking,¹⁸² and consequently I will discuss this risk factor in more detail in the following. As described, a confounding variable must also be related non-causally or causally to the exposure. In the smoking example, this means that the smoking behavior among TCS must differ across the different treatment groups and/or differ compared with the general population. As described in chapter 1.6.5, smoking has not been observed as more common in TCS compared with controls in previous studies,^111,166 while younger cancer survivors had a higher smoking prevalence.¹⁶⁸ However, a recent Danish study observed higher current smoking prevalence among TCS compared with a reference population, with the highest prevalence among TCSs treated with BEP.²³⁰

It is also possible that underlying genetic aberrations or shared environmental exposures predispose some TCS for other cancers,^27,28,182 or that epigenetic changes brought on by PBCT increase the morbidity in TCS.²³¹ More research within these fields are needed before their potential roles as confounding variables can be evaluated.

54

Traits associated with TC as well as presence of GCNIS are potential confounding factors in paper II if they are associated with exposure status (treatment or age) in addition to being related to the second TC risk. The lack of this information constitutes possible limitations in paper II.