[Modeling incomplete observations].

M Chavance; R Manfredi

Home/Archive/PubMed

PubMed · 11011305

[Modeling incomplete observations].

M Chavance·R Manfredi

View original source ↗Export metadata ↓

Abstract

Incomplete observations, common in epidemiology as in many other fields, lead to problems of bias, precision and power. Using a simple example with 3 binary variables, we discuss situations where the observed odds ratio is biased. We present and compare the main strategies of analysis: complete observations modeling, missing data indicator, weighted analysis, simple imputation, multiple imputation, selection models, shared variable models.

Explore related subjects

Bias Data Interpretation, Statistical Epidemiologic Measurements Epidemiologic Research Design Humans Longitudinal Studies Models, Statistical Odds Ratio Comparative Study Journal Article Validation Study

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

M Chavance, R Manfredi. 2000. [Modeling incomplete observations].. https://pubmed.ncbi.nlm.nih.gov/11011305/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Assessment of blinding in pharmacotherapy and noninvasive neuromodulation randomized controlled trials for neuropathic pain in adults.

In randomized controlled trials (RCTs), study participants and research personnel are often blinded to minimize biases related to knowing treatment allocation. To determine if blinding was effective, participants may be asked which treatment they believe they received ("treatment guess"). This descriptive review characterized blinding assessment (BA) reporting in pharmacotherapy and neuromodulation neuropathic pain RCTs. Of 288 papers, 36 (12.5%) reported a BA. One paper reported the results of 2 studies, so in total 37 studies with a BA were assessed. Of these, 19 were crossover, 17 parallel, and 1 partial crossover in design. All 37 studies assessed participant blinding, and 10 also assessed investigator blinding. Approximately 27% included an "unsure" answer option for treatment guess, and 38% asked the reason for the guess. There were no clear patterns in BA reporting across time nor based on treatment type. Seventeen trials provided sufficient data to calculate Bang Blinding Index (BI) to determine blinding success. Participants remained blinded (BI = 0 ± 0.2) in 10/17 placebo and 10/17 treatment arms, 6 placebo and 5 treatment arms had a BI > 0.2 suggesting possible unblinding, whereas 1 placebo and 2 treatment arms had a BI < -0.2 suggesting misinformed guessing. Overall, we found that BAs are done in a minority of published neuropathic pain trials and with variable methodology. Given the importance of minimizing risk of bias because of treatment unblinding, future studies should consider including BAs, and further consensus building is necessary to determine if and how BAs should be conducted and interpreted in analgesic clinical trials.

Bias↗

PubMed2001

Estimating hospital deaths due to medical errors: preventability is in the eye of the reviewer.

CONTEXT: Studies using physician implicit review have suggested that the number of deaths due to medical errors in US hospitals is extremely high. However, some have questioned the validity of these estimates. OBJECTIVE: To examine the reliability of reviewer ratings of medical error and the implications of a death described as "preventable by better care" in terms of the probability of immediate and short-term survival if care had been optimal. DESIGN: Retrospective implicit review of medical records from 1995-1996. SETTING AND PARTICIPANTS: Fourteen board-certified, trained internists used a previously tested structured implicit review instrument to conduct 383 reviews of 111 hospital deaths at 7 Department of Veterans Affairs medical centers, oversampling for markers previously found to be associated with high rates of preventable deaths. Patients considered terminally ill who received comfort care only were excluded. MAIN OUTCOME MEASURES: Reviewer estimates of whether deaths could have been prevented by optimal care (rated on a 5-point scale) and of the probability that patients would have lived to discharge or for 3 months or more if care had been optimal (rated from 0%-100%). RESULTS: Similar to previous studies, almost a quarter (22.7%) of active-care patient deaths were rated as at least possibly preventable by optimal care, with 6.0% rated as probably or definitely preventable. Interrater reliability for these ratings was also similar to previous studies (0.34 for 2 reviewers). The reviewers' estimates of the percentage of patients who would have left the hospital alive had optimal care been provided was 6.0% (95% confidence interval [CI], 3.4%-8.6%). However, after considering 3-month prognosis and adjusting for the variability and skewness of reviewers' ratings, clinicians estimated that only 0.5% (95% CI, 0.3%-0.7%) of patients who died would have lived 3 months or more in good cognitive health if care had been optimal, representing roughly 1 patient per 10 000 admissions to the study hospitals. CONCLUSIONS: Medical errors are a major concern regardless of patients' life expectancies, but our study suggests that previous interpretations of medical error statistics are probably misleading. Our data place the estimates of preventable deaths in context, pointing out the limitations of this means of identifying medical errors and assessing their potential implications for patient outcomes.

Bias↗

PubMed2001

Adjustments for center in multicenter studies: an overview.

Increasingly, investigators rely on multicenter or multigroup studies to demonstrate effectiveness and generalizability. Authors too often overlook the analytic challenges in these study designs: the correlation of outcomes and exposures among patients within centers, confounding of associations by center, and effect modification of treatment or exposure across center. Correlation or clustering, resulting from the similarity of outcomes among patients within a center, requires an adjustment to confidence intervals and P values, especially in observational studies and in randomized multicenter studies in which treatment is allocated by center rather than by individual patient. Multicenter designs also warrant testing and adjustment for the potential bias of confounding by center, and for the presence of effect modification or interaction by center. This paper uses examples from the recent biomedical literature to highlight the issues and analytic options.

Bias↗