Findings from the TOPP study showed that those who stayed compared to those who dropped out over the15-year period differed in baseline educational level but not in regard to baseline mental health and relationship variables. Furthermore, the two groups did not differ significantly regarding associations between variables. The results from the simulation study showed that mean estimates became substantially biased even at relatively weak dependencies between follow-up variables and attrition, whereas estimates of associations between variables were more robust to dependencies between attrition and study variables. In addition, mean estimates, but not regression estimates, were strongly affected by attrition rate. The results are more thoroughly discussed below.
Temperamental sociability was a significant predictor of short-term attrition (baseline to one-year follow-up), in that high scores on sociability predicted higher chances of dropping out. Apart from a study showing that antisocial personality predicted having died at follow-up
, there are few studies on adult personality and attrition from population-based studies. Our finding shows that psychological variables other than psychopathology can be important for understanding attrition.
In a long-term perspective (baseline to 15-year follow-up), educational level predicted drop-out. The sample became moderately biased towards having more well-educated participants over time, which is in accordance with previous attrition studies finding that socio-demographic variables predict drop-out
[2, 4, 9–11].
An important question when examining long-term attrition was whether those who stayed and those who dropped out differed on psychological and social variables at baseline. Some population-based studies have found weak to moderate dependencies between adult psychiatric diagnosis and attrition after adjusting for other variables
[9, 10]. In studies where self-rating measures were used, psychological distress was found to have no effect or a weak to moderate effect after adjusting for other variables
[2, 4]. The results of the present study are more in accordance with the latter, as psychological characteristics of neither the women nor children nor qualities of the spouse/partner relationship predicted long-term attrition. Slightly divergent results may be due to different measures of psychological distress. Our results are also in accordance with previous research showing no associations between baseline child characteristics, such as temperament and anxiety, and attrition in population-based studies
Even though baseline sociability and educational level predicted attrition, the baseline associations between these variables and mental health were the same among those who later dropped out and those who remained in the study. Of the 15 correlations between psychological distress and other variables examined at baseline, none were significantly different for participants and non-participants at one- or 15-year follow-up. The current findings thus show that even if those who stay and those who drop out of a study differ regarding mean levels of some variables, estimates of associations can be robust to such differences.
The current simulation study provided information about effects of attrition dependent on follow-up as well as baseline variables. The results showed that mean estimates became increasingly biased as attrition rates increased. At 50% and 70% attrition rates, mean estimates became extremely biased, even at weak dependencies between attrition and follow-up variables. Mean estimates became increasingly biased as the dependency between risk of attrition and the study variable got stronger. Therefore, mean estimates from longitudinal studies should be interpreted with caution, even when attrition is only weakly dependent on the variables of interest. These results are in accordance with findings from a study of the effect of selective enrolment in a large population-based study of pregnant women
. Nilsen and colleagues used information about medical conditions among non-responders from a national register and concluded that mean estimates of age, number of cigarettes smoked, birth weight, and other medical variables were biased among participants because of selective participation in the study
The simulation study further showed that regression estimates were only minimally affected by attrition rate. Regression estimates and their 95% coverage were very similar at both lower and higher attrition rates. In addition, the degree of dependency between attrition and the follow-up variable had only weak effects on regression estimates and their 95% coverages. This was the case both when the population association between predictor and outcome was weak and when it was moderate. Naturally, the proportion of samples that rejected the false null hypothesis of a zero association between the two study variables was higher with stronger population associations. This proportion did not decrease notably as the dependency between attrition and follow-up variables increased. The effect of attrition on estimates of associations between variables thus seemed to be limited to the effect of reduced N when attrition was only dependent on follow-up variables.
However, when attrition became increasingly dependent on both baseline and follow-up variables, the regression estimates were seriously biased, and the 95% coverage dropped dramatically. For weak population associations between variables, the proportion of samples that succeeded in rejecting the false null hypothesis also decreased when attrition became increasingly dependent on both baseline and follow-up variables.
The current results indicate that attrition related to both baseline and follow-up variables has far worse consequences for regression estimates than attrition that is only related to follow-up variables. Being able to account for attrition related to baseline variables can thus reduce the negative consequences of selective attrition on regression estimates. Modern techniques for handling missing data (e.g. full information maximum likelihood and multiple imputation methods) are effective in adjusting for missingness that is dependent on variables with information from all participants
. In longitudinal studies with attrition, the researcher typically has information on baseline variables from all participants, but lack of information on follow-up variables from those who have dropped out. The current results suggest that using such techniques to account for attrition related to baseline variables can reduce the negative effects of selective attrition on regression estimates even if these techniques do not account for attrition related to follow-up variables.
Graham and Donaldson
 reported from their simulation study that non-random attrition affected estimation of the effect of an intervention. They concluded that correlation estimates were biased when attrition differed between the control group and the intervention group, but that correlation estimates based on complete cases were unbiased when attrition was the same in both groups, even though attrition was dependent on measured and unmeasured variables. They did not compare different degrees of dependency between attrition and the study variables. Our results thus extend their findings by showing effects of attrition with different degrees of dependency on baseline and follow-up variables.
Although the real life study had several strengths-being population-based, extending over a long period of time, and having a relatively large number of participants-there are also some limitations.
First, individuals with the highest levels of mental health problems and alcohol use tend to participate less often than others in population-based studies
. Even though the current results indicate that samples in long-term longitudinal studies may be comparable to those in cross-sectional studies, both kinds of studies face challenges regarding generalizability to persons with high levels of mental health problems. Second, staff at the health care centers organized the data collection at the first three time points, whereas the questionnaires were distributed by mail at later waves. Differences in data collection methods may have influenced attrition in the short-term compared to long-term perspective. Third, some of the measures showed somewhat low reliability, and this may have affected the results. Fourth, the results of this attrition study may be generalizable only to questionnaire studies. Thus, other kinds of studies, such as those employing interviews, need to be examined separately. Fifth, some argue that the Bonferroni correction produces too conservative p-thresholds and therefore too high risk of type II errors
. Working status was a significant predictor of long-term attrition before but not after Bonferroni correction. However, there were no other differences before and after Bonferroni correction in the adjusted solutions. Bonferroni correcting of the results thus had only minimal impact on our conclusions. Moreover, the power analysis conducted showed that relatively small effects in the population could be detected with high probabilities. Thus, the non-findings in the real life study are probably not a result of low statistical power. Sixth, attrition from the TOPP study was mainly due to refusal to participate. Other reasons for attrition from population-based studies, such as death or failure to locate, may have other consequences for generalizability of findings. Finally, the sample size used in the current simulation study was similar to the baseline sample size in the real life study. Different sample sizes can provide different confidence intervals and thus different results regarding statistical significance. Therefore, further simulation studies are needed to examine the effect of attrition under several different conditions.