Clarifying assumptions in age-period-cohort analyses and validating results

被引:15
作者
Masters, Ryan [1 ]
Powers, Daniel [2 ]
机构
[1] Univ Colorado, Boulder, CO 80309 USA
[2] Univ Texas Austin, Austin, TX 78712 USA
来源
PLOS ONE | 2020年 / 15卷 / 10期
关键词
CROSS-SECTION SURVEYS; LIFE EXPECTANCY GAP; INTRINSIC ESTIMATOR; UNITED-STATES; ADULT MORTALITY; FUTILE QUEST; TRENDS; MODELS; DISPARITIES; YANG;
D O I
10.1371/journal.pone.0238871
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background Age-period-cohort (APC) models are often used to decompose health trends into period- and cohort-based sources, but their use in epidemiology and population sciences remains contentious. Central to the contention are researchers' failures to 1) clearly state their analytic assumptions and/or 2) thoroughly evaluate model results. These failures often produce varying conclusions across APC studies and generate confusion about APC methods. Consequently, scholarly exchanges about APC methods usually result in strong disagreements that rarely offer practical advice to users or readers of APC methods. Methods We use research guidelines to help practitioners of APC methods articulate their analytic assumptions and validate their results. To demonstrate the usefulness of the guidelines, we apply them to a 2015American Journal of Epidemiologystudy about trends in black-white differences in U.S. heart disease mortality. Results The application of the guidelines highlights two important findings. On the one hand, some APC methods produce inconsistent results that are highly sensitive to researcher manipulation. On the other hand, other APC methods estimate results that are robust to researcher manipulation and consistent across APC models. Conclusions The exercise shows the simplicity and effectiveness of the guidelines in resolving disagreements over APC results. The cautious use of APC models can generate results that are consistent across methods and robust to researcher manipulation. If followed, the guidelines can likely reduce the chance of publishing variable and conflicting results across APC studies.
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页数:21
相关论文
共 61 条
[51]  
STATA StataCorp, 2017, STATA STAT SOFTWARE
[52]  
Tukey J.W., 1977, Exploratory Data Analysis, DOI DOI 10.1007/978-1-4419-7976-6
[53]  
WILLIAMS D R, 1992, Ethnicity and Disease, V2, P126
[54]   Social sources of racial disparities in health - Policies in societal domains, far removed from traditional health policy, can have decisive consequences for health. [J].
Williams, DR ;
Jackson, PB .
HEALTH AFFAIRS, 2005, 24 (02) :325-334
[55]   A methodological comparison of age-period-cohort models: The intrinsic estimator and conventional generalized linear models [J].
Yang, Y ;
Fu, WJJ ;
Land, KC .
SOCIOLOGICAL METHODOLOGY, 2004, VOL 34, 2004, 34 :75-110
[56]   The intrinsic estimator for age-period-cohort analysis: What it is and how to use it [J].
Yang, Yang ;
Schulhofer-Wohl, Sam ;
Fu, Wenjiang J. ;
Land, Kenneth C. .
AMERICAN JOURNAL OF SOCIOLOGY, 2008, 113 (06) :1697-1736
[57]   Age-period-cohort analysis of repeated cross-section surveys - Fixed or random effects? [J].
Yang, Yang ;
Land, Kenneth C. .
SOCIOLOGICAL METHODS & RESEARCH, 2008, 36 (03) :297-326
[58]   A mixed models approach to the age-period-cohort analysis of repeated cross-section surveys, with an application to data on trends in verbal test scores [J].
Yang, Yang ;
Land, Kenneth C. .
SOCIOLOGICAL METHODOLOGY 2006, VOL 36, 2006, 36 :75-97
[59]  
Yang Yang., 2013, AGE PERIOD COHORT AN
[60]   Misunderstandings, Mischaracterizations, and the Problematic Choice of a Specific Instance in Which the IE Should Never Be Applied [J].
Yang, Yang Claire ;
Land, Kenneth C. .
DEMOGRAPHY, 2013, 50 (06) :1969-1971