Do Oscar Winners Live Longer than Less Successful Peers? A Reanalysis of the Evidence

The large survival advantagealmost 4 yearsfor Academy Awardwinning actors and actresses over their less successful peers (1) continues to receive attention. We point out that the statistical method used to derive the statistically significant survival difference gave the Oscar winners an unfair advantage. We suggest how readers might recognize and avoid similar biases in other research reports. Redelmeier and Singh's report (1) was based on 235 Oscar winners, 527 nominees (nonwinners), and 887 performers who were never nominated (controls). Controls were selected from performers who were the same sex and approximately the same age in years as the nominees and who performed in the movies for which the nominees were nominated. In the primary analysis, survival was measured from performers' day of birth, but other definitions of time zero were also used. In all but 1 of the KaplanMeier, log-rank, and Cox proportional hazards analyses reported, each performer was classified as a winner or nonwinner from the outset. One reported analysis used winner as a time-dependent covariate to reflect the fact that all started out as nonwinners but that some changed status over time. In Redelmeier and Singh's more emphasized comparison, KaplanMeier curves showed that life expectancy was 3.9 years longer for winners. The Cox model, with winner as a fixed-in-time covariate, yielded mortality rate reductions ranging from 28% (no adjustment) to 23% (adjustment for 7 other covariates), all with 95% confidence limits more than 0%. The 1 reported set of analyses that treated each performer's status as dynamic (time-dependent) yielded a mortality rate reduction of 20%; the lower limit of the CI was 0%, that is, the reduction was just significant at the conventional level (P= 0.05). Redelmeier and Singh's abstract and their Figure focused on the 3.9-year life-expectancy advantage and the 28% mortality rate reduction for winners, which were obtained without adjustment and without taking into account that a performer's status changed with time. Figure. Lexis diagram showing life course for 9 selected performers (all nominated), along with their status at the time of the 8 risk sets (1 at each death). A Lexis diagram (4) represents each performer's time course as a diagonal line, with advancing age on the vertical axis and advancing calendar time on the horizontal axis. Winners, by virtue of their having lived long enough to win, were, in hindsight, immortal in the years that preceded their win. Circles and squares at the left of the figure indicate ages at which winners won and ages at death of those who died without winning. The analyses that classified those who ultimately won as winners from the outset gave them an inbuilt survival advantage by crediting the winner's life-years before winning toward survival subsequent to winning. These immortal years (2, 3) were a requirement for membership in the winners' group: Winners had to survive long enough to winmore than 79 years in the 2 most extreme cases (Figure). Performers who did not win had no minimum survival requirement, and some died before some winners had won, that is, before some longevity contests could begin. For example, 145 nonwinners had already died by age 65 years, that is, before 15 of the winners had won. These unfair pairings (for example, Richard Burton vs. George Burns) were implicitly included in the overall longevity contest between the 2 groups and contributed to the apparent survival advantage of the winners, even if winning brought no survival benefit. To estimate the longevity benefits of winning an Oscar, the comparison should begin at the time that each performer first wins, and the remaining longevity contest should only include those alive at the same age as the winner was when he or she won. A winner may legitimately be included in comparisons (risk sets) before winning, but only as a nonwinner. An analysis in which the status of a performer who won is treated as a winner throughout, even in risk sets before winning, produces an immortal time bias. As we illustrate in the Figure, a longevity that is measured from a time zero that precedes the performer's Oscar win (for example, an individualized one, such as the day each performer's first film was released, or a common one, such as each performer's initial or 50th birthday, as used in Redelmeier and Singh's analysis [1]) will necessarily contain some immortal time. No immortality guarantee exists for those who do not win. In a similar manner, the matching process, involving a performer who played opposite a nominee, ensured that a control was alive when a person who ultimately won was nominated but not necessarily when that winner won (the comparison of 235 winners vs. 527 other nominees did not involve a matching process). The authors reported 1 analysis in which each performer's status was updated in each risk set. In the Table, we compare the results from the types of analyses they used (original) with our reanalyses (new). Our methods are described more fully in the Appendix. All of our analyses treat each performer's status as dynamic. The database on which our analyses are based is available online. In our reanalyses, which take the immortal time as well as the covariates sex and year of birth into account, the point estimate of the actuarial advantage is approximately 1 year and is not statistically significantly different from 0 (the 95% CI is compatible with 0). The estimated percentage mortality rate reduction is also correspondingly smaller. Supplement. Data set Table. Original Analysis and Reanalyses of the Longevity Difference among Oscar Winners and Less Successful Performers* We directly estimated the magnitude of the immortal time bias (Appendix). In our comparison of winners versus nominees, we estimated that not accounting for immortal time produced an artifactual longevity advantage of 0.8 year and a mortality rate ratio of 0.94. In the comparison of winners versus controls, not accounting for the immortal timenow more substantialbetween the year of a winning performer's first film and the year he or she first won produced an artificial longevity advantage of 1.7 years and a mortality rate ratio of 0.87. In 1843, William Farr (5) described the statistical artifact created by classifying persons by their status at the end of follow-up and analyzing them as if they had been in these categories from the outset. He used as examples the greater longevity of persons who reached higher ranks within their professions (bishops vs. curates, judges vs. barristers, and generals vs. lieutenants). Despite textbook warnings (2, 6, 7), analyses overlooking this subtle bias are still common today. In some longevity comparisons (1, 4, 8), the consequences of an incorrect conclusion are minor. In the evaluation of the time-extension benefits of therapy (3, 9, 10), the consequences are more serious. Therefore, how do we detect potential immortal time bias? We suggest that when reports compare 2 groups, such as winners versus nominees, one should carefully examine when and how persons enter a group. Does being in or moving to a group have a time-related requirement? Is the classification based on the status at time zero or later? If later, is this accounted for? Is the term status, which implies potential change, more appropriate than the term group, which implies, as in a clinical trial, that group membership is fixed from the outset? Is it sufficient to classify a person just once, or do we need to reclassify the person-moments, that is, the person at different times? Showing timelines, as in the Figure, may help. Of course, readers and commentators should be doubly cautious whenever they encounter statistical results that seem too extreme to be true.