Physicians' communication skills with patients and legal liability in decided medical malpractice litigation cases in Japan

<p>Abstract</p> <p>Background</p> <p>In medical malpractice litigations in recent years in Japan, it is notable that the growing number of medical litigation cases includes the issue of a doctor's explanation to the patient as a pivotal point. The objective of this study was to identify factors of physicians' communication skills with patients, as related to their legal liability, and differences in doctors' communication skills with patients by the type of medical facility.</p> <p>Methods</p> <p>Decisions of medical malpractice litigation cases between 1988 and 2005 in Japan, the pivotal issue of which was a physician's explanation, were analyzed in the study. The content of each decision was summarized using the study variables (information about the patient, doctor, manner of the doctor's explanation, and subsequent litigation), and a database comprising the content of each decision (<it>N </it>= 100) was constructed. In order to evaluate an association between doctors' communication skills with patients and the outcome of the litigation, the analysis was performed based on the outcome of litigation or the type of medical facility.</p> <p>Results</p> <p>The ratio of acknowledged physician liability by court decision was lower in cases in which the doctor's explanation occurred before treatment or surgery (<it>p </it>= 0.013). The ratio of acknowledged physician liability by court decision was higher in cases of elective or non-urgent treatment (<it>p </it>= 0.046). The ratio of acknowledged physician liability by court decision was higher in clinics than in hospital groups (<it>p </it>= 0.036).</p> <p>Conclusion</p> <p>These findings are beneficial for the prevention of medical disputes and improvement of patient-physician communication.</p

A simple method for estimating relative risk using logistic regression

<p>Abstract</p> <p>Background</p> <p>Odds ratios (OR) significantly overestimate associations between risk factors and common outcomes. The estimation of relative risks (RR) or prevalence ratios (PR) has represented a statistical challenge in multivariate analysis and, furthermore, some researchers do not have access to the available methods. Objective: To propose and evaluate a new method for estimating RR and PR by logistic regression.</p> <p>Methods</p> <p>A provisional database was designed in which events were duplicated but identified as non-events. After, a logistic regression was performed and effect measures were calculated, which were considered RR estimations. This method was compared with binomial regression, Cox regression with robust variance and ordinary logistic regression in analyses with three outcomes of different frequencies.</p> <p>Results</p> <p>ORs estimated by ordinary logistic regression progressively overestimated RRs as the outcome frequency increased. RRs estimated by Cox regression and the method proposed in this article were similar to those estimated by binomial regression for every outcome. However, confidence intervals were wider with the proposed method.</p> <p>Conclusion</p> <p>This simple tool could be useful for calculating the effect of risk factors and the impact of health interventions in developing countries when other statistical strategies are not available.</p

Objective Estimates Improve Risk Stratification for Primary Graft Dysfunction after Lung Transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112244/1/ajt13262.pd

Ongoing monitoring of data clustering in multicenter studies

Background: Multicenter study designs have several advantages, but the possibility of non-random measurement error resulting from procedural differences between the centers is a special concern. While it is possible to address and correct for some measurement error through statistical analysis, proactive data monitoring is essential to ensure high-quality data collection. Methods: In this article, we describe quality assurance efforts aimed at reducing the effect of measurement error in a recent follow-up of a large cluster-randomized controlled trial through periodic evaluation of intraclass correlation coefficients (ICCs) for continuous measurements. An ICC of 0 indicates the variance in the data is not due to variation between the centers, and thus the data are not clustered by center. Results: Through our review of early data downloads, we identified several outcomes (including sitting height, waist circumference, and systolic blood pressure) with higher than expected ICC values. Further investigation revealed variations in the procedures used by pediatricians to measure these outcomes. We addressed these procedural inconsistencies through written clarification of the protocol and refresher training workshops with the pediatricians. Further data monitoring at subsequent downloads showed that these efforts had a beneficial effect on data quality (sitting height ICC decreased from 0.92 to 0.03, waist circumference from 0.10 to 0.07, and systolic blood pressure from 0.16 to 0.12). Conclusions: We describe a simple but formal mechanism for identifying ongoing problems during data collection. The calculation of the ICC can easily be programmed and the mechanism has wide applicability, not just to cluster randomized controlled trials but to any study with multiple centers or with multiple observers

Accounting for centre-effects in multicentre trials with a binary outcome - when, why, and how?

BACKGROUND: It is often desirable to account for centre-effects in the analysis of multicentre randomised trials, however it is unclear which analysis methods are best in trials with a binary outcome. METHODS: We compared the performance of four methods of analysis (fixed-effects models, random-effects models, generalised estimating equations (GEE), and Mantel-Haenszel) using a re-analysis of a previously reported randomised trial (MIST2) and a large simulation study. RESULTS: The re-analysis of MIST2 found that fixed-effects and Mantel-Haenszel led to many patients being dropped from the analysis due to over-stratification (up to 69% dropped for Mantel-Haenszel, and up to 33% dropped for fixed-effects). Conversely, random-effects and GEE included all patients in the analysis, however GEE did not reach convergence. Estimated treatment effects and p-values were highly variable across different analysis methods. The simulation study found that most methods of analysis performed well with a small number of centres. With a large number of centres, fixed-effects led to biased estimates and inflated type I error rates in many situations, and Mantel-Haenszel lost power compared to other analysis methods in some situations. Conversely, both random-effects and GEE gave nominal type I error rates and good power across all scenarios, and were usually as good as or better than either fixed-effects or Mantel-Haenszel. However, this was only true for GEEs with non-robust standard errors (SEs); using a robust ‘sandwich’ estimator led to inflated type I error rates across most scenarios. CONCLUSIONS: With a small number of centres, we recommend the use of fixed-effects, random-effects, or GEE with non-robust SEs. Random-effects and GEE with non-robust SEs should be used with a moderate or large number of centres

Protocol for implementation of family health history collection and decision support into primary care using a computerized family health history system

<p>Abstract</p> <p>Background</p> <p>The CDC's Family History Public Health Initiative encourages adoption and increase awareness of family health history. To meet these goals and develop a personalized medicine implementation science research agenda, the Genomedical Connection is using an implementation research (T3 research) framework to develop and integrate a self-administered computerized family history system with built-in decision support into 2 primary care clinics in North Carolina.</p> <p>Methods/Design</p> <p>The family health history system collects a three generation family history on 48 conditions and provides decision support (pedigree and tabular family history, provider recommendation report and patient summary report) for 4 pilot conditions: breast cancer, ovarian cancer, colon cancer, and thrombosis. All adult English-speaking, non-adopted, patients scheduled for well-visits are invited to complete the family health system prior to their appointment. Decision support documents are entered into the medical record and available to provider's prior to the appointment. In order to optimize integration, components were piloted by stakeholders prior to and during implementation. Primary outcomes are change in appropriate testing for hereditary thrombophilia and screening for breast cancer, colon cancer, and ovarian cancer one year after study enrollment. Secondary outcomes include implementation measures related to the benefits and burdens of the family health system and its impact on clinic workflow, patients' risk perception, and intention to change health related behaviors. Outcomes are assessed through chart review, patient surveys at baseline and follow-up, and provider surveys. Clinical validity of the decision support is calculated by comparing its recommendations to those made by a genetic counselor reviewing the same pedigree; and clinical utility is demonstrated through reclassification rates and changes in appropriate screening (the primary outcome).</p> <p>Discussion</p> <p>This study integrates a computerized family health history system within the context of a routine well-visit appointment to overcome many of the existing barriers to collection and use of family history information by primary care providers. Results of the implementation process, its acceptability to patients and providers, modifications necessary to optimize the system, and impact on clinical care can serve to guide future implementation projects for both family history and other tools of personalized medicine, such as health risk assessments.</p

Screening for data clustering in multicenter studies: the residual intraclass correlation

status: publishe

Design effect in multicenter studies: gain or loss of power?

<p>Abstract</p> <p>Background</p> <p>In a multicenter trial, responses for subjects belonging to a common center are correlated. Such a clustering is usually assessed through the design effect, defined as a ratio of two variances. The aim of this work was to describe and understand situations where the design effect involves a gain or a loss of power.</p> <p>Methods</p> <p>We developed a design effect formula for a multicenter study aimed at testing the effect of a binary factor (which thus defines two groups) on a continuous outcome, and explored this design effect for several designs (from individually stratified randomized trials to cluster randomized trials, and for other designs such as matched pair designs or observational multicenter studies).</p> <p>Results</p> <p>The design effect depends on the intraclass correlation coefficient (ICC) (which assesses the correlation between data for two subjects from the same center) but also on a statistic <it>S</it>, which quantifies the heterogeneity of the group distributions among centers (thus the level of association between the binary factor and the center) and on the degree of global imbalance (the number of subjects are then different) between the two groups. This design effect may induce either a loss or a gain in power, depending on whether the <it>S </it>statistic is respectively higher or lower than 1.</p> <p>Conclusion</p> <p>We provided a global design effect formula applying for any multicenter study and allowing identifying factors – the ICC and the distribution of the group proportions among centers – that are associated with a gain or a loss of power in such studies.</p

Adjusting for multiple prognostic factors in the analysis of randomised trials

Background: When multiple prognostic factors are adjusted for in the analysis of a randomised trial, it is unclear (1) whether it is necessary to account for each of the strata, formed by all combinations of the prognostic factors (stratified analysis), when randomisation has been balanced within each stratum (stratified randomisation), or whether adjusting for the main effects alone will suffice, and (2) the best method of adjustment in terms of type I error rate and power, irrespective of the randomisation method. Methods: We used simulation to (1) determine if a stratified analysis is necessary after stratified randomisation, and (2) to compare different methods of adjustment in terms of power and type I error rate. We considered the following methods of analysis: adjusting for covariates in a regression model, adjusting for each stratum using either fixed or random effects, and Mantel-Haenszel or a stratified Cox model depending on outcome. Results: Stratified analysis is required after stratified randomisation to maintain correct type I error rates when (a) there are strong interactions between prognostic factors, and (b) there are approximately equal number of patients in each stratum. However, simulations based on real trial data found that type I error rates were unaffected by the method of analysis (stratified vs unstratified), indicating these conditions were not met in real datasets. Comparison of different analysis methods found that with small sample sizes and a binary or time-to-event outcome, most analysis methods lead to either inflated type I error rates or a reduction in power; the lone exception was a stratified analysis using random effects for strata, which gave nominal type I error rates and adequate power. Conclusions: It is unlikely that a stratified analysis is necessary after stratified randomisation except in extreme scenarios. Therefore, the method of analysis (accounting for the strata, or adjusting only for the covariates) will not generally need to depend on the method of randomisation used. Most methods of analysis work well with large sample sizes, however treating strata as random effects should be the analysis method of choice with binary or time-to-event outcomes and a small sample size

Determinants of access to experimental antiretroviral drugs in an Italian cohort of patients with HIV: a multilevel analysis

<p>Abstract</p> <p>Background</p> <p>Identification of the determinants of access to investigational drugs is important to promote equity and scientific validity in clinical research. We aimed to analyze factors associated with the use of experimental antiretrovirals in Italy.</p> <p>Methods</p> <p>We studied participants in the Italian Cohort of Antiretroviral-Naive Patients (ICoNA). All patients 18 years or older who had started cART (≥ 3 drugs including at least two NRTI) after their enrolment and during 1997-2007 were included in this analysis. We performed a random effect logistic regression analysis to take into account clustering observations within clinical units. The outcome variable was the use of an experimental antiretroviral, defined as an antiretroviral started before commercial availability, in any episode of therapy initiation/change. Use of an experimental antiretroviral obtained through a clinical trial or an expanded access program (EAP) was also analyzed separately.</p> <p>Results</p> <p>A total of 9,441 episodes of therapy initiation/change were analyzed in 3,752 patients. 392 episodes (360 patients) involved an experimental antiretroviral. In multivariable analysis, factors associated with the overall use of experimental antiretrovirals were: number of experienced drugs (≥ 8 drugs versus "naive": adjusted odds ratio [AOR] = 3.71) or failed antiretrovirals(3-4 drugs and ≥ 5 drugs versus 0-2 drugs: AOR = 1.42 and 2.38 respectively); calendar year (AOR = 0.80 per year) and plasma HIV-RNA copies/ml at therapy change (≥ 4 log versus < 2 log: AOR = 1.55). The probability of taking an experimental antiretroviral through a trial was significantly lower for patients suffering from liver co-morbidity(AOR = 0.65) and for those who experienced 3-4 drugs (vs naive) (AOR = 0.55), while it increased for multi-treated patients(AOR = 2.60). The probability to start an experimental antiretroviral trough an EAP progressively increased with the increasing number of experienced and of failed drugs and also increased for patients with liver co-morbidity (AOR = 1.44; p = 0.053). and for male homosexuals (vs heterosexuals: AOR = 1.67). Variability of the random effect associated to clinical units was statistically significant (p < 0.001) although no association was found with specific characteristics of clinical unit examined.</p> <p>Conclusions</p> <p>Among patients with HIV infection in Italy, access to experimental antiretrovirals seems to be influenced mainly by exhaustion of treatment options and not by socio-demographic factors.</p