Ardo van den Hout

Active Cognitive Lifestyle Associates with Cognitive Recovery and a Reduced Risk of Cognitive Decline

Education and lifestyle factors linked with complex mental activity are thought to affect the progression of cognitive decline. Collectively, these factors can be combined to create a cognitive reserve or cognitive lifestyle score. This study tested the association between cognitive lifestyle score and cognitive change in a population-based cohort of older persons from five sites across England and Wales. Data came from 13,004 participants of the Medical Research Council Cognitive Function and Ageing Study who were aged 65 years and over. Cognition was assessed at multiple waves over 16 years using the Mini-Mental State Examination. Subjects were grouped into four cognitive states (no impairment, slight impairment, moderate impairment, severe impairment) and cognitive lifestyle score was assessed as a composite measure of education, mid-life occupation, and current social engagement. A multi-state model was used to test the effect of cognitive lifestyle score on cognitive transitions. Hazard ratios for cognitive lifestyle score showed significant differences between those in the upper compared to the lower tertile with a more active cognitive lifestyle associating with: a decreased risk of moving from no to slight impairment (0.58, 95% CI (0.45, 0.74)); recovery from a slightly impaired state back to a non-impaired state (2.93 (1.35, 6.38)); but an increased mortality risk from a severely impaired state (1.28 (1.12, 1.45)). An active cognitive lifestyle is associated with a more favorable cognitive trajectory in older persons. Future studies would ideally incorporate neuroradiological and neuropathological data to determine if there is causal evidence for these associations.

The logistic regression model with response variables subject to randomized response

The univariate and multivariate logistic regression model is discussed where response variables are subject to randomized response (RR). RR is an interview technique that can be used when sensitive questions have to be asked and respondents are reluctant to answer directly. RR variables may be described as misclassified categorical variables where conditional misclassification probabilities are known. The univariate model is revisited and is presented as a generalized linear model. Standard software can be easily adjusted to take into account the RR design. The multivariate model does not appear to have been considered elsewhere in an RR setting; it is shown how a Fisher scoring algorithm can be used to take the RR aspect into account. The approach is illustrated by analyzing RR data taken from a study in regulatory non-compliance regarding unemployment benefit.

Active Cognitive Lifestyle Is Associated with Positive Cognitive Health Transitions and Compression of Morbidity from Age Sixty-Five

Background Three factors commonly used as measures of cognitive lifestyle are education, occupation, and social engagement. This study determined the relative importance of each variable to long term cognitive health in those with and without severe cognitive impairment. Methods Data came from 12,470 participants from a multi-centre population-based cohort (Medical Research Council Cognitive Function and Ageing Study). Respondents were aged 65 years and over and were followed-up over 16 years. Cognitive states of no impairment, slight impairment, and moderate/severe impairment were defined, based on scores from the Mini-Mental State Examination. Multi-state modelling was used to investigate links between component cognitive lifestyle variables, cognitive state transitions over time, and death. Results Higher educational attainment and a more complex mid-life occupation were associated with a lower risk of moving from a non-impaired to a slightly impaired state (hazard ratios 0.5 and 0.8), but with increased mortality from a severely impaired state (1.3 and 1.1). More socially engaged individuals had a decreased risk of moving from a slightly impaired state to a moderately/severely impaired state (0.7). All three cognitive lifestyle variables were linked to an increased chance of cognitive recovery back to the non-impaired state. Conclusions In those without severe cognitive impairment, different aspects of cognitive lifestyle predict positive cognitive transitions over time, and in those with severe cognitive impairment, a reduced life-expectancy. An active cognitive lifestyle is therefore linked to compression of cognitive morbidity in late life.

Log-linear randomized-response models taking self-protective response behavior into account

Randomized response (RR) is an interview technique designed to eliminate response bias when sensitive questions are asked. In RR the answer depends partly on the true status of the respondent and partly on the outcome of a randomizing device. Although RR elicits more honest answers than direct questions do, it is susceptible to self-protective response behavior; that is, the respondent gives an evasive answer irrespective of the outcome of the randomizing device. The authors present a log-linear RR model that accounts for this kind of self-protection (SP). The main results of this SP model are estimates of (1) the probability of SP, (2) the log-linear parameters describing the associations between the sensitive characteristics, and (3) the prevalence of the sensitive characteristics that are corrected for SP. The model is illustrated with two examples from a Dutch survey measuring noncompliance with social welfare rules.

The Analysis of multivariate misclassified data with special attention to randomized response data

This article discusses log-linear analysis of misclassified categorical data when conditional misclassification probabilities are known. This kind of misclassification occurs when data are collected using a randomized response design. The authors describe the misclassification by a latent class model. Since a latent class model is a log-linear model with one or more categorical latent variables, it is possible to investigate relations between misclassified variables. Methods to fit log-linear models for the latent table are discussed, including an EM algorithm. Attention is given to problems with boundary solutions. The results can also be used in statistical disclosure control when the post-randomization method is applied to protect the privacy of respondents, in epidemiology when specificity and sensitivity are known, and in data mining when privacy is protected by intentional statistical perturbation. Examples are given using randomized response data from a research into social benefit fraud.This article discusses log-linear analysis of misclassified categorical data when conditional misclassification probabilities are known. This kind of misclassification occurs when data are collected using a randomized response design. The authors describe the misclassification by a latent class model. Since a latent class model is a log-linear model with one or more categorical latent variables, it is possible to investigate relations between misclassified variables. Methods to fit log-linear models for the latent table are discussed, including an EM algorithm. Attention is given to problems with boundary solutions. The results can also be used in statistical disclosure control when the post-randomization method is applied to protect the privacy of respondents, in epidemiology when specificity and sensitivity are known, and in data mining when privacy is protected by intentional statistical perturbation. Examples are given using randomized response data from a research into social benefit fraud.

Multi‐state analysis of cognitive ability data: A piecewise‐constant model and a Weibull model

Longitudinal data can be used to estimate transition intensities between healthy and unhealthy states prior to death. When health is defined with respect to cognitive ability during old age, the trajectory of performance is either static or downward. An illness-death model is presented where the intensity of a transition is allowed to change over time by using a piecewise-constant model or by using a Weibull model. Observed improvement of cognitive ability is modelled as misclassification. The methodology is extended to estimate life expectancy with and without cognitive impairment. The measurement of cognitive ability is important as it is an important predictor of future need for care. The models can help to understand how cognitive decline develops over time and which factors play a role. The methods are illustrated using data from the Medical Research Council Cognitive Function and Ageing Study in the U.K. (1991-2005).

Investigating terminal decline: results from a UK population-based study of aging.

The terminal decline hypothesis states that in the proximity of death, an individuals decline in cognitive abilities accelerates. We aimed at estimating the onset of faster rate of decline in global cognition using Mini Mental State Examination (MMSE) scores from participants of the Cambridge City over 75 Cohort Study (CC75C), a U.K. population-based longitudinal study of aging where almost all participants have died. The random change point model fitted to MMSE scores structured as a function of distance to death allowed us to identify a potentially different onset of change in rate of decline before death for each individual in the sample. Differences in rate of change before and after the onset of change in rate of decline by sociodemographic variables were investigated. On average, the onset of a faster rate of change occurred about 7.7 years before death and varied across individuals. Our results show that most individuals experience a period of slight decline followed by a much sharper decline. Education, age at death, and cognitive impairment at study entry were identified as modifiers of rate of change before and after change in rate of decline. Gender differences were found in rate of decline in the final stages of life. Our study suggests that terminal decline is a heterogeneous process, with its onset varying between individuals.

Estimating the prevalence of sensitive behaviour and cheating with a dual design for direct questioning and randomized response

Randomized response is a misclassification design to estimate the prevalence of sensitive behaviour. Respondents who do not follow the instructions of the design are considered to be cheating. A mixture model is proposed to estimate the prevalence of sensitive behaviour and cheating in the case of a dual sampling scheme with direct questioning and randomized response. The mixing weight is the probability of cheating, where cheating is modelled separately for direct questioning and randomized response. For Bayesian inference, Markov chain Monte Carlo sampling is applied to sample parameter values from the posterior. The model makes it possible to analyse dual sample scheme data in a unified way and to assess cheating for direct questions as well as for randomized response questions. The research is illustrated with randomized response data concerning violations of regulations for social benefit.

Estimating dementia-free life expectancy for Parkinson's patients using Bayesian inference and microsimulation.

Interval-censored longitudinal data taken from a Norwegian study of individuals with Parkinsons disease are investigated with respect to the onset of dementia. Of interest are risk factors for dementia and the subdivision of total life expectancy (LE) into LE with and without dementia. To estimate LEs using extrapolation, a parametric continuous-time 3-state illness–death Markov model is presented in a Bayesian framework. The framework is well suited to allow for heterogeneity via random effects and to investigate additional computation using model parameters. In the estimation of LEs, microsimulation is used to take into account random effects. Intensities of moving between the states are allowed to change in a piecewise-constant fashion by linking them to age as a time-dependent covariate. Possible right censoring at the end of the follow-up can be incorporated. The model is applicable in many situations where individuals are followed over a long time period. In describing how a disease develops over time, the model can help to predict future need for health care.

Smooth random change point models.

Change point models are used to describe processes over time that show a change in direction. An example of such a process is cognitive ability, where a decline a few years before death is sometimes observed. A broken-stick model consists of two linear parts and a breakpoint where the two lines intersect. Alternatively, models can be formulated that imply a smooth change between the two linear parts. Change point models can be extended by adding random effects to account for variability between subjects. A new smooth change point model is introduced and examples are presented that show how change point models can be estimated using functions in R for mixed-effects models. The Bayesian inference using WinBUGS is also discussed. The methods are illustrated using data from a population-based longitudinal study of ageing, the Cambridge City over 75 Cohort Study. The aim is to identify how many years before death individuals experience a change in the rate of decline of their cognitive ability.