Analyzing sickness absence with statistical models for survival data

Karl Bang Christensen; Per Kragh Andersen; Lars Smith-Hansen; Martin L Nielsen; Tage S Kristensen

Analyzing sickness absence with statistical models for survival data

Karl Bang Christensen, Per Kragh Andersen, Lars Smith-Hansen, Martin L Nielsen, Tage S Kristensen

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

52 Citationer (Scopus)

Abstract

Udgivelsesdato: 2007-Jun

Originalsprog	Engelsk
Tidsskrift	Scandinavian Journal of Work, Environment & Health
Vol/bind	33
Udgave nummer	3
Sider (fra-til)	233-9
Antal sider	6
ISSN	0355-3140
Status	Udgivet - 2007

Bibliografisk note

Keywords: Absenteeism; Humans; Models, Statistical; Poisson Distribution; Proportional Hazards Models; Regression Analysis; Sick Leave; Survival Analysis

Citationsformater

@article{2ee8d280edfa11ddbf70000ea68e967b,

title = "Analyzing sickness absence with statistical models for survival data",

abstract = "OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation between the psychosocial work environment and sickness absence were used to illustrate the results. RESULTS: Standard methods were found to underestimate true effect sizes by approximately one-tenth [method i] and one-third [method ii] and to have lower statistical power than frailty models. CONCLUSIONS: An uncritical use of standard methods may underestimate the effect of work environment exposures or leave predictors of sickness absence undiscovered.",

author = "Christensen, {Karl Bang} and Andersen, {Per Kragh} and Lars Smith-Hansen and Nielsen, {Martin L} and Kristensen, {Tage S}",

note = "Keywords: Absenteeism; Humans; Models, Statistical; Poisson Distribution; Proportional Hazards Models; Regression Analysis; Sick Leave; Survival Analysis",

year = "2007",

language = "English",

volume = "33",

pages = "233--9",

journal = "Scandinavian Journal of Work, Environment & Health",

issn = "0355-3140",

publisher = "Tyoterveyslaitos",

number = "3",

}

TY - JOUR

T1 - Analyzing sickness absence with statistical models for survival data

AU - Christensen, Karl Bang

AU - Andersen, Per Kragh

AU - Smith-Hansen, Lars

AU - Nielsen, Martin L

AU - Kristensen, Tage S

N1 - Keywords: Absenteeism; Humans; Models, Statistical; Poisson Distribution; Proportional Hazards Models; Regression Analysis; Sick Leave; Survival Analysis

PY - 2007

Y1 - 2007

N2 - OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation between the psychosocial work environment and sickness absence were used to illustrate the results. RESULTS: Standard methods were found to underestimate true effect sizes by approximately one-tenth [method i] and one-third [method ii] and to have lower statistical power than frailty models. CONCLUSIONS: An uncritical use of standard methods may underestimate the effect of work environment exposures or leave predictors of sickness absence undiscovered.

AB - OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation between the psychosocial work environment and sickness absence were used to illustrate the results. RESULTS: Standard methods were found to underestimate true effect sizes by approximately one-tenth [method i] and one-third [method ii] and to have lower statistical power than frailty models. CONCLUSIONS: An uncritical use of standard methods may underestimate the effect of work environment exposures or leave predictors of sickness absence undiscovered.

M3 - Journal article

C2 - 17572833

VL - 33

SP - 233

EP - 239

JO - Scandinavian Journal of Work, Environment & Health

JF - Scandinavian Journal of Work, Environment & Health

SN - 0355-3140

IS - 3

ER -