Does being physically active prevent future disability in older people? Attenuated effects when taking time-dependent confounders into account

Abstract Background Causal experimental evidence that physical activity prevents disability in older people is sparse. Being physically active has nonetheless been shown to be associated with disability-free survival in observational studies. Observational studies are, however, prone to bias introdu...

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Main Authors: Stefan H. Kreisel, Christian Blahak, Hansjörg Bäzner, Michael G. Hennerici
Format: Article
Language:English
Published: BMC 2017-12-01
Series:BMC Geriatrics
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12877-017-0657-3
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spelling doaj-83ab629a0cb6490fa197cbe63e9b38cd2020-11-25T03:51:07ZengBMCBMC Geriatrics1471-23182017-12-0117111110.1186/s12877-017-0657-3Does being physically active prevent future disability in older people? Attenuated effects when taking time-dependent confounders into accountStefan H. Kreisel0Christian Blahak1Hansjörg Bäzner2Michael G. Hennerici3Geriatric Psychiatry; Department of Psychiatry and Psychotherapy, Evangelisches Klinikum BethelDepartment of Neurology, Universitäts Medizin MannheimDepartment of Neurology, Klinikum StuttgartDepartment of Neurology, Universitäts Medizin MannheimAbstract Background Causal experimental evidence that physical activity prevents disability in older people is sparse. Being physically active has nonetheless been shown to be associated with disability-free survival in observational studies. Observational studies are, however, prone to bias introduced by time-dependent confounding. Time-dependent confounding occurs when an exposure (e.g. being physically active at some time-point) potentially affects the future status of a confounder (such as depression sometime later), and both variables have an effect on latter outcome (i.e. disability). “Conventional” analysis with e.g. Cox-regression is the mainstay when analyzing longitudinal observational studies. Unfortunately, it does not provide unbiased estimates in the presence of time-dependent confounding. Marginal structural models (MSM) – a relatively new class of causal models – have the potential to adequately account for time-dependent confounding. Here we analyze the effect of older people being physically active on disability, in a large long-term observational study. We address time-dependent confounding by using marginal structural models and provide a non-technical practical demonstration of how to implement this type of modeling. Methods Data is from 639 elderly individuals ascertained in the European multi-center Leukoaraiosis and Disability study (LADIS), followed-up yearly over a period of three years. We estimated the effect of self-reported physical activity on the probability to transit to instrumental disability in the presence of a large set of potential confounders. We compare the results of “conventional” modeling approaches to those estimated using marginal structural models, highlighting discrepancies. Results A “conventional” Cox-regression-like adjustment for salient baseline confounders signals a significant risk reduction under physical activity for later instrumental disability (OR 0.62, 95% CI 0.44–0.90). However, given MSM estimation, the effect is attenuated towards null (OR 1.00, 95% CI 0.57–1.76). Conclusions Contrary to most reports, we did not find that physical activity in older people prevents future instrumental disability, when taking time-dependent confounding into account. This result may be due to the characteristics our particular study population. It is, however, also conceivable that previous evidence neglected the effect of this type of bias. We suggest that analysts of longitudinal observational studies consider marginal structural models as a further modeling approach.http://link.springer.com/article/10.1186/s12877-017-0657-3Physical activityMarginal structural modelsCausal inferenceAge-related white matter lesionsDisabilityLongitudinal observational studies
collection DOAJ
language English
format Article
sources DOAJ
author Stefan H. Kreisel
Christian Blahak
Hansjörg Bäzner
Michael G. Hennerici
spellingShingle Stefan H. Kreisel
Christian Blahak
Hansjörg Bäzner
Michael G. Hennerici
Does being physically active prevent future disability in older people? Attenuated effects when taking time-dependent confounders into account
BMC Geriatrics
Physical activity
Marginal structural models
Causal inference
Age-related white matter lesions
Disability
Longitudinal observational studies
author_facet Stefan H. Kreisel
Christian Blahak
Hansjörg Bäzner
Michael G. Hennerici
author_sort Stefan H. Kreisel
title Does being physically active prevent future disability in older people? Attenuated effects when taking time-dependent confounders into account
title_short Does being physically active prevent future disability in older people? Attenuated effects when taking time-dependent confounders into account
title_full Does being physically active prevent future disability in older people? Attenuated effects when taking time-dependent confounders into account
title_fullStr Does being physically active prevent future disability in older people? Attenuated effects when taking time-dependent confounders into account
title_full_unstemmed Does being physically active prevent future disability in older people? Attenuated effects when taking time-dependent confounders into account
title_sort does being physically active prevent future disability in older people? attenuated effects when taking time-dependent confounders into account
publisher BMC
series BMC Geriatrics
issn 1471-2318
publishDate 2017-12-01
description Abstract Background Causal experimental evidence that physical activity prevents disability in older people is sparse. Being physically active has nonetheless been shown to be associated with disability-free survival in observational studies. Observational studies are, however, prone to bias introduced by time-dependent confounding. Time-dependent confounding occurs when an exposure (e.g. being physically active at some time-point) potentially affects the future status of a confounder (such as depression sometime later), and both variables have an effect on latter outcome (i.e. disability). “Conventional” analysis with e.g. Cox-regression is the mainstay when analyzing longitudinal observational studies. Unfortunately, it does not provide unbiased estimates in the presence of time-dependent confounding. Marginal structural models (MSM) – a relatively new class of causal models – have the potential to adequately account for time-dependent confounding. Here we analyze the effect of older people being physically active on disability, in a large long-term observational study. We address time-dependent confounding by using marginal structural models and provide a non-technical practical demonstration of how to implement this type of modeling. Methods Data is from 639 elderly individuals ascertained in the European multi-center Leukoaraiosis and Disability study (LADIS), followed-up yearly over a period of three years. We estimated the effect of self-reported physical activity on the probability to transit to instrumental disability in the presence of a large set of potential confounders. We compare the results of “conventional” modeling approaches to those estimated using marginal structural models, highlighting discrepancies. Results A “conventional” Cox-regression-like adjustment for salient baseline confounders signals a significant risk reduction under physical activity for later instrumental disability (OR 0.62, 95% CI 0.44–0.90). However, given MSM estimation, the effect is attenuated towards null (OR 1.00, 95% CI 0.57–1.76). Conclusions Contrary to most reports, we did not find that physical activity in older people prevents future instrumental disability, when taking time-dependent confounding into account. This result may be due to the characteristics our particular study population. It is, however, also conceivable that previous evidence neglected the effect of this type of bias. We suggest that analysts of longitudinal observational studies consider marginal structural models as a further modeling approach.
topic Physical activity
Marginal structural models
Causal inference
Age-related white matter lesions
Disability
Longitudinal observational studies
url http://link.springer.com/article/10.1186/s12877-017-0657-3
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