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A comparative study of variable selection methods in the context of developing psychiatric screening instruments.

ABSTRACT: The development of screening instruments for psychiatric disorders involves item selection from a pool of items in existing questionnaires assessing clinical and behavioral phenotypes. A screening instrument should consist of only a few items and have good accuracy in classifying cases and non-cases. Variable/item selection methods such as Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, Classification and Regression Tree, Random Forest, and the two-sample t-test can be used in such context. Unlike situations where variable selection methods are most commonly applied (e.g., ultra high-dimensional genetic or imaging data), psychiatric data usually have lower dimensions and are characterized by the following factors: correlations and possible interactions among predictors, unobservability of important variables (i.e., true variables not measured by available questionnaires), amount and pattern of missing values in the predictors, and prevalence of cases in the training data. We investigate how these factors affect the performance of several variable selection methods and compare them with respect to selection performance and prediction error rate via simulations. Our results demonstrated that: (1) for complete data, LASSO and Elastic Net outperformed other methods with respect to variable selection and future data prediction, and (2) for certain types of incomplete data, Random Forest induced bias in imputation, leading to incorrect ranking of variable importance. We propose the Imputed-LASSO combining Random Forest imputation and LASSO; this approach offsets the bias in Random Forest and offers a simple yet efficient item selection approach for missing data. As an illustration, we apply the methods to items from the standard Autism Diagnostic Interview-Revised version.

SUBMITTER: Lu F

PROVIDER: S-EPMC4026268 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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A comparative study of variable selection methods in the context of developing psychiatric screening instruments.

Lu Feihan F Petkova Eva E

Statistics in medicine 20130811 3

The development of screening instruments for psychiatric disorders involves item selection from a pool of items in existing questionnaires assessing clinical and behavioral phenotypes. A screening instrument should consist of only a few items and have good accuracy in classifying cases and non-cases. Variable/item selection methods such as Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, Classification and Regression Tree, Random Forest, and the two-sample t-test can be used ...[more]

PMID: 23934941

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Project description:BackgroundAs a hot method in machine learning field, the forests approach is an attractive alternative approach to Cox model. Random survival forests (RSF) methodology is the most popular survival forests method, whereas its drawbacks exist such as a selection bias towards covariates with many possible split points. Conditional inference forests (CIF) methodology is known to reduce the selection bias via a two-step split procedure implementing hypothesis tests as it separates the variable selection and splitting, but its computation costs too much time. Random forests with maximally selected rank statistics (MSR-RF) methodology proposed recently seems to be a great improvement on RSF and CIF.MethodsIn this paper we used simulation study and real data application to compare prediction performances and variable selection performances among three survival forests methods, including RSF, CIF and MSR-RF. To evaluate the performance of variable selection, we combined all simulations to calculate the frequency of ranking top of the variable importance measures of the correct variables, where higher frequency means better selection ability. We used Integrated Brier Score (IBS) and c-index to measure the prediction accuracy of all three methods. The smaller IBS value, the greater the prediction.ResultsSimulations show that three forests methods differ slightly in prediction performance. MSR-RF and RSF might perform better than CIF when there are only continuous or binary variables in the datasets. For variable selection performance, When there are multiple categorical variables in the datasets, the selection frequency of RSF seems to be lowest in most cases. MSR-RF and CIF have higher selection rates, and CIF perform well especially with the interaction term. The fact that correlation degree of the variables has little effect on the selection frequency indicates that three forest methods can handle data with correlation. When there are only continuous variables in the datasets, MSR-RF perform better. When there are only binary variables in the datasets, RSF and MSR-RF have more advantages than CIF. When the variable dimension increases, MSR-RF and RSF seem to be more robustthan CIF CONCLUSIONS: All three methods show advantages in prediction performances and variable selection performances under different situations. The recent proposed methodology MSR-RF possess practical value and is well worth popularizing. It is important to identify the appropriate method in real use according to the research aim and the nature of covariates.

Project description:Background:Building prognostic models of clinical outcomes is an increasingly important research task and will remain a vital area in genomic medicine. Prognostic models of clinical outcomes are usually built and validated utilizing variable selection methods and machine learning tools. The challenges, however, in ultra-high dimensional space are not only to reduce the dimensionality of the data, but also to retain the important variables which predict the outcome. Screening approaches, such as the sure independence screening (SIS), iterative SIS (ISIS) and principled SIS (PSIS) have been developed to overcome the challenge of high dimensionality. We are interested in identifying important single-nucleotide polymorphisms (SNPs) and integrating them into a validated prognostic model of overall survival in patients with metastatic prostate cancer. While the abovementioned variable selection approaches have theoretical justification in selecting SNPs, the comparison and the performance of these combined methods in predicting time-to-event outcomes have not been previously studied in ultra-high dimensional space with hundreds of thousands of variables. Methods:We conducted a series of simulations to compare the performance of different combinations of variable selection approaches and classification trees, such as the least absolute shrinkage and selection operator (LASSO), adaptive least absolute shrinkage and selection operator (ALASSO) and random survival forest (RSF), in ultra-high dimensional setting data for the purpose of developing prognostic models for a time-to-event outcome that is subject to censoring. The variable selection methods were evaluated for discrimination (Harrell's concordance statistic), calibration and overall performance. In addition, we applied these approaches to 498,081 SNPs from 623 Caucasian patients with prostate cancer. Results:When n=300, ISIS-LASSO and ISIS-ALASSO chose all the informative variables which resulted in the highest Harrell's c-index (>0.80). On the other hand, with a small sample size (n=150), ALASSO performed better than any other combinations as demonstrated by the highest c-index and/or overall performance, although there was evidence of overfitting. In analyzing the prostate cancer data, ISIS-ALASSO, SIS-LASSO, and SIS-ALASSO combinations achieved the highest discrimination with c-index of 0.67. Conclusions:Choosing the appropriate variable selection method for training a model is a critical step in developing a robust prognostic model. Based on the simulation studies, the effective use of ALASSO or a combination of methods, such as ISIS-LASSO and ISIS-ALASSO, allows both for the development of prognostic models with high predictive accuracy and a low risk of overfitting assuming moderate sample sizes.

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Project description:BACKGROUND:Frailty exposes older people to an elevated risk of a range of negative outcomes. Emerging evidence that frailty can be effectively treated within community settings has stimulated calls for more proactive screening within primary care. Assessing feasibility is a critical preliminary step in assessing the efficacy of interventions such as screening. However, few studies have explored the feasibility and acceptability of administering frailty screening instruments within general practice, and even fewer have incorporated patient perspectives. Our study had three objectives: To 1) assess overall feasibility of the instruments (completion time and rate); 2) assess patient acceptability towards the instruments; and 3) assess the feasibility and acceptability of the instruments to administering nurses. METHODS:The feasibility and acceptability of several frailty screening instruments (PRISMA-7, Edmonton Frail Scale, FRAIL Scale Questionnaire, Gait Speed, Groningen Frailty Indicator, Reported Edmonton Frail Scale and Kihon Checklist) was explored within the context of a larger diagnostic test accuracy (DTA) study. Completion time and rate was collected for all participants (N =?243). A sub-sample of patients (n =?30) rated each instrument for ease of completion and provided comment on perceived acceptability. Lastly, five of six administering nurses involved in the DTA study participated in semi-structured face-to-face interviews, rating the instruments against several feasibility and acceptability criteria (time, space, equipment, skill required to implement, acceptability to patients and nurses, ease of scoring) and providing comment on their responses. RESULTS:The PRISMA-7 returned the highest overall feasibility and acceptability, requiring minimal space, equipment, skills and time to implement, and returning the fastest completion rate and highest patient and nurse acceptability rating. All screening instruments were faster to implement than the two reference standards (Fried's Frailty Phenotype and Frailty Index). Self-administered instruments were subject to lower rates of completion than nurse-administered instruments. CONCLUSIONS:This study has demonstrated that a number of commonly used frailty screening instruments are potentially feasible for implementation within general practice. Ultimately, more research is needed to determine how contextual factors, such as differences in individual patient and clinician preferences, setting and system factors, impact on the feasibility of screening in practice.

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A comparative study of variable selection methods in the context of developing psychiatric screening instruments.

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A comparative study of variable selection methods in the context of developing psychiatric screening instruments.

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