Predicting cognitive and behavioral functions in patients with dementia: Practical prognostic models of logarithmic and linear regression

Abstract

Aims: This study provides data on predicting changes in cognitive functions, behavioral independences and disturbances in dementia patients by differential modeling with logarithmic and linear regression. Methods: This longitudinal study included two data analysis groups. Group one: 24 dementia patients for identification of cognitive and behavioral changes over time in group data; group two: 15 dementia patients to ensure correlation of the group data applied to prediction of each individual's degree of cognitive and behavioral changes. Group one mini-mental state examination, functional independence measure and dementia behavior disturbance scale scores were assessed initially and 3 and 6 months thereafter during hospitalization and were regressed on the logarithm and linear of time. In group two, calculations of the scores were made for the first two scorings after admission to tailor logarithmic and linear regression formulae to fit an individual's degree of changes at 9 and 12 months. Results: Changes in data over time resembled both logarithmic and linear functions. However, the scores sampled at two baseline points based on logarithmic regression modeling estimated prediction of cognitive and behavioral changes more accurately than did linear regression modeling. Conclusion: This simple-to-use logarithmic modeling accurately predicted changes in cognitive functions, behavioral independence and disturbances in patients with dementia.

Prediction of changes in functional ability of inpatients with schizophrenia using logarithmic and linear regression modelling

“…The present study also found that functional ability in inpatients with schizophrenia could be predicted by logarithmic modelling. We showed that logarithmic modelling can predict functioning of an individual with schizophrenia at discharge over various hospitalisation periods, whereas the predictive model in previous studies (Koyama et al, 2005;Suzuki et al, 2013;Watanabe et al, 2016) was used to assess applicability for all participants in a specific period. Furthermore, the present study showed that logarithmic modelling using the APS score based on brief observation assessments could predict recovery of functional ability for inpatients with schizophrenia during individual rehabilitation Figure 3.…”

“…For post hoc analyses, significant differences in APS score between the initial assessment and each time point assessment were analysed using the Shirley–Williams multiple comparison test. In addition, to identify the time course of recovery of functional ability in the group data and to construct predictive models, logarithmic (Koyama et al., 2005; Suzuki et al., 2013; Watanabe et al., 2016) and linear regression analyses (Suzuki et al., 2013; Watanabe et al., 2016) were performed with the group data using the following formulae: f(t) = a + b ln (t) and f (t) = a + b (t), where t is the number of days since the beginning of the occupational therapy programme, a is the APS score at the first assessment, and b is the slope of the changes in functional ability at three time points (first, second, and fourth assessments, or first, third, and fourth assessments). We used the APS scores at one week and at one month because functional recovery in schizophrenia requires substantial time (Robinson, Woerner, McMeniman, Mendelowitz, & Bilder, 2004).…”

“…For post hoc analyses, significant differences in APS score between the initial assessment and each time point assessment were analysed using the Shirley-Williams multiple comparison test. In addition, to identify the time course of recovery of functional ability in the group data and to construct predictive models, logarithmic (Koyama et al, 2005;Suzuki et al, 2013;Watanabe et al, 2016) and linear regression analyses (Suzuki et al, 2013;Watanabe et al, 2016) were performed with the group data using the following formulae:…”

“…In comparison with other assessment scales (Atsumi & Ohbuchi, 2002;Margolis, Harrison, Robinson, & Jayaram, 1996;Ohata et al, 2010), the APS was determined to be suitable for this study at the points that it can be used in the context of Japanese, it is easy to apply clinically as there are few items, and it is not affected by differences in recovery period between inpatients with schizophrenia. We hypothesised that (a) the time course of the initial phase of change in the recovery of functional ability could be approximated in a logarithmic model, as in previous studies (Koyama et al, 2005;Suzuki et al, 2013;Watanabe et al, 2016), and (b) recovery of functional ability could be accurately predicted by a logarithmic model. Therefore, we conducted a longitudinal study to investigate the possibility of predicting recovery of functional ability over time by logarithmic and linear regression modelling of changes during the treatment of schizophrenia.…”

“…However, few studies have addressed whether the prognosis for functional ability can be predicted by assessment during early stages of treatment or whether time course regression can predict changes in functional recovery in schizophrenia. If the time course of changes in the functional ability of patients with schizophrenia is revealed by logarithmic and linear regression modelling, as in other diseases (Koyama, Matsumoto, Okuno, & Domen, 2005; Suzuki et al., 2013; Watanabe et al., 2016), we can obtain useful and highly practical information regarding how patients with schizophrenia might recover in clinical situations. Comparison between logarithmic and linear regression models is necessary to determine which model is better suited for schizophrenia as previous studies have shown the applicability of both logarithmic and linear regression models (Suzuki et al., 2013; Watanabe et al., 2016).…”