Sparse kernel machine regression for ordinal outcomes

Assessing prognosis and prediction of treatment response in early rheumatoid arthritis: systematic reviews

BACKGROUND

Rheumatoid arthritis (RA) is a chronic, debilitating disease associated with reduced quality of life and substantial costs. It is unclear which tests and assessment tools allow the best assessment of prognosis in people with early RA and whether or not variables predict the response of patients to different drug treatments.

OBJECTIVE

To systematically review evidence on the use of selected tests and assessment tools in patients with early RA (1) in the evaluation of a prognosis (review 1) and (2) as predictive markers of treatment response (review 2).

DATA SOURCES

Electronic databases (e.g. MEDLINE, EMBASE, The Cochrane Library, Web of Science Conference Proceedings; searched to September 2016), registers, key websites, hand-searching of reference lists of included studies and key systematic reviews and contact with experts.

STUDY SELECTION

Review 1 - primary studies on the development, external validation and impact of clinical prediction models for selected outcomes in adult early RA patients. Review 2 - primary studies on the interaction between selected baseline covariates and treatment (conventional and biological disease-modifying antirheumatic drugs) on salient outcomes in adult early RA patients.

RESULTS

Review 1 - 22 model development studies and one combined model development/external validation study reporting 39 clinical prediction models were included. Five external validation studies evaluating eight clinical prediction models for radiographic joint damage were also included. c-statistics from internal validation ranged from 0.63 to 0.87 for radiographic progression (different definitions, six studies) and 0.78 to 0.82 for the Health Assessment Questionnaire (HAQ). Predictive performance in external validations varied considerably. Three models [(1) Active controlled Study of Patients receiving Infliximab for the treatment of Rheumatoid arthritis of Early onset (ASPIRE) C-reactive protein (ASPIRE CRP), (2) ASPIRE erythrocyte sedimentation rate (ASPIRE ESR) and (3) Behandelings Strategie (BeSt)] were externally validated using the same outcome definition in more than one population. Results of the random-effects meta-analysis suggested substantial uncertainty in the expected predictive performance of models in a new sample of patients. Review 2 - 12 studies were identified. Covariates examined included anti-citrullinated protein/peptide anti-body (ACPA) status, smoking status, erosions, rheumatoid factor status, C-reactive protein level, erythrocyte sedimentation rate, swollen joint count (SJC), body mass index and vascularity of synovium on power Doppler ultrasound (PDUS). Outcomes examined included erosions/radiographic progression, disease activity, physical function and Disease Activity Score-28 remission. There was statistical evidence to suggest that ACPA status, SJC and PDUS status at baseline may be treatment effect modifiers, but not necessarily that they are prognostic of response for all treatments. Most of the results were subject to considerable uncertainty and were not statistically significant.

LIMITATIONS

The meta-analysis in review 1 was limited by the availability of only a small number of external validation studies. Studies rarely investigated the interaction between predictors and treatment.

SUGGESTED RESEARCH PRIORITIES

Collaborative research (including the use of individual participant data) is needed to further develop and externally validate the clinical prediction models. The clinical prediction models should be validated with respect to individual treatments. Future assessments of treatment by covariate interactions should follow good statistical practice.

CONCLUSIONS

Review 1 - uncertainty remains over the optimal prediction model(s) for use in clinical practice. Review 2 - in general, there was insufficient evidence that the effect of treatment depended on baseline characteristics.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42016042402.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

A weighted kernel machine regression approach to environmental pollutants and infertility

In epidemiological studies of environmental pollutants in relation to human infertility, it is common that concentrations of a large number of exposures are collected in both male and female partners. Such a couple-based study poses some new challenges in statistical analysis, especially when the effect of the totality of these chemical mixtures is of interest, because these exposures may have complex nonlinear and nonadditive relationships with the infertility outcome. Kernel machine regression, as a nonparametric regression method, can be applied to model such effects, while accounting for the highly correlated structure within and across exposures. However, it does not consider the partner-specific structure in these study data, which may lead to suboptimal estimation for the effects of environmental exposures. To overcome this limitation, we developed a weighted kernel machine regression method (wKRM) to model the joint effect of partner-specific exposures, in which a linear weight procedure is used to combine the female and male partners' exposure concentrations. The proposed wKRM is not only able to reduce the number of analyzed exposures but also provide an overall importance index of female and male partners' exposures in the risk of infertility. Simulation studies demonstrate good performance of the wKRM in both estimating the joint effects of exposures and fitting the infertility outcome. Application of the proposed method to a prospective infertility study suggests that the male partner's exposure to polychlorinated biphenyls might contribute more toward infertility than the female partner's.

Identification of subjects with polycystic ovary syndrome using electronic health records

BACKGROUND

Polycystic ovary syndrome (PCOS) is a heterogeneous disorder because of the variable criteria used for diagnosis. Therefore, International Classification of Diseases 9 (ICD-9) codes may not accurately capture the diagnostic criteria necessary for large scale PCOS identification. We hypothesized that use of electronic medical records text and data would more specifically capture PCOS subjects.

METHODS

Subjects with PCOS were identified in the Partners Healthcare Research Patients Data Registry by searching for the term "polycystic ovary syndrome" using natural language processing (n = 24,930). A training subset of 199 identified charts was reviewed and categorized based on likelihood of a true Rotterdam PCOS diagnosis, i.e. two out of three of the following: irregular menstrual cycles, hyperandrogenism and/or polycystic ovary morphology. Data from the history, physical exam, laboratory and radiology results were codified and extracted from notes of definite PCOS subjects. Thirty-two terms were used to build an algorithm for identifying definite PCOS cases and applied to the rest of the dataset. The positive predictive value cutoff was set at 76.8 % to maximize the number of subjects available for study. A true positive predictive value for the algorithm was calculated after review of 100 charts from subjects identified as definite PCOS cases with at least two documented Rotterdam criteria. The positive predictive value was compared to that calculated using 200 charts identified using the ICD-9 code for PCOS (256.4; n = 13,670). In addition, a cohort of previously recruited PCOS subjects was submitted for algorithm validation.

RESULTS

Chart review demonstrated that 64 % were confirmed as definitely PCOS using the algorithm, with a 9 % false positive rate. 66 % of subjects identified by ICD-9 code for PCOS could be confirmed as definitely PCOS, with an 8.5 % false positive rate. There was no significant difference in the positive predictive values using the two methods (p = 0.2). However, the number of charts that had insufficient confirmatory data was lower using the algorithm (5 % vs 11 %; p < 0.04). Of 477 subjects with PCOS recruited and examined individually and present in the database as patients, 451 were found within the algorithm dataset.

CONCLUSIONS

Extraction of text parameters along with codified data improves the confidence in PCOS patient cohorts identified using the electronic medical record. However, the positive predictive value was not significantly different when using ICD-9 codes or the specific algorithm. Further studies are needed to determine the positive predictive value of the two methods in additional electronic medical record datasets.