53 research outputs found

    To test or not to test:Preliminary assessment of normality when comparing two independent samples

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    BACKGROUND: Student’s two-sample t test is generally used for comparing the means of two independent samples, for example, two treatment arms. Under the null hypothesis, the t test assumes that the two samples arise from the same normally distributed population with unknown variance. Adequate control of the Type I error requires that the normality assumption holds, which is often examined by means of a preliminary Shapiro-Wilk test. The following two-stage procedure is widely accepted: If the preliminary test for normality is not significant, the t test is used; if the preliminary test rejects the null hypothesis of normality, a nonparametric test is applied in the main analysis. METHODS: Equally sized samples were drawn from exponential, uniform, and normal distributions. The two-sample t test was conducted if either both samples (Strategy I) or the collapsed set of residuals from both samples (Strategy II) had passed the preliminary Shapiro-Wilk test for normality; otherwise, Mann-Whitney’s U test was conducted. By simulation, we separately estimated the conditional Type I error probabilities for the parametric and nonparametric part of the two-stage procedure. Finally, we assessed the overall Type I error rate and the power of the two-stage procedure as a whole. RESULTS: Preliminary testing for normality seriously altered the conditional Type I error rates of the subsequent main analysis for both parametric and nonparametric tests. We discuss possible explanations for the observed results, the most important one being the selection mechanism due to the preliminary test. Interestingly, the overall Type I error rate and power of the entire two-stage procedure remained within acceptable limits. CONCLUSION: The two-stage procedure might be considered incorrect from a formal perspective; nevertheless, in the investigated examples, this procedure seemed to satisfactorily maintain the nominal significance level and had acceptable power properties

    How to improve drug dosing for patients with renal impairment in primary care - a cluster-randomized controlled trial

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    Background: Patients with chronic kidney disease (CKD) are at increased risk for inappropriate or potentially harmful prescribing. The aim of this study was to examine whether a multifaceted intervention including the use of a software programme for the estimation of creatinine clearance and recommendation of individual dosage requirements may improve correct dosage adjustment of relevant medications for patients with CKD in primary care. Methods: A cluster-randomized controlled trial was conducted between January and December 2007 in small primary care practices in Germany. Practices were randomly allocated to intervention or control groups. In each practice, we included patients with known CKD and elderly patients (>=70 years) suffering from hypertension. The practices in the intervention group received interactive training and were provided a software programme to assist with individual dose adjustment. The control group performed usual care. Data were collected at baseline and at 6 months. The outcome measures, analyzed across individual patients, included prescriptions exceeding recommended maximum daily doses, with the primary outcome being prescriptions exceeding recommended standard daily doses by 30% or more. Results: Data from 44 general practitioners and 404 patients are included. The intervention was effective in reducing prescriptions exceeding the maximum daily dose per patients, with a trend in reducing prescriptions exceeding the standard daily dose by more than 30%. Conclusions: A multifaceted intervention including the use of a software program effectively reduced inappropriately high doses of renally excreted medications in patients with CKD in the setting of small primary care practices

    The use of external controls: To what extent can it currently be recommended?

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    With more and better clinical data being captured outside of clinical studies and greater data sharing of clinical studies, external controls may become a more attractive alternative to randomized clinical trials. Both industry and regulators recognize that in situations where a randomized study cannot be performed, external controls can provide the needed contextualization to allow a better interpretation of studies without a randomized control. It is also agreed that external controls will not fully replace randomized clinical trials as the gold standard for formal proof of efficacy in drug development and the yardstick of clinical research. However, it remains unclear in which situations conclusions about efficacy and a positive benefit/risk can reliably be based on the use of an external control. This paper will provide an overview on types of external control, their applications and the different sources of bias their use may incur, and discuss potential mitigation steps. It will also give recommendations on how the use of external controls can be justified

    Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: an observational study

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    BACKGROUND: Primary care-based care management (CM) could reduce hospital admissions in high-risk patients. Identification of patients most likely to benefit is needed as resources for CM are limited. This study aimed to compare hospitalization and mortality rates of patients identified for CM either by treating primary care physicians (PCPs) or predictive modelling software for hospitalization risk (PM). METHODS: In 2009, a cohort of 6,026 beneficiaries of a German statutory health insurance served as a sample for patient identification for CM by PCPs or commercial PM (CSSG 0.8, Verisk Health). The resulting samples were compared regarding hospitalization and mortality rates in 2010 and in the two year period before patient selection. No CM-intervention was delivered until the end of 2010 and PCPs were blinded for the assessment of hospitalization rates. RESULTS: In 2010, hospitalization rates of PM-identified patients were 80% higher compared to PCP-identified patients. Mortality rates were also 8% higher in PM-identified patients if compared to PCP-identified patients (10% vs. 2%). The hospitalization rate of patients independently identified by both PM and PCPs was numerically between PM- and PCP-identified patients. Time trend between 2007 and 2010 showed decreasing hospitalization rates in PM-identified patients (−15% per year) compared to increasing rates in PCP-identified patients (+34% per year). CONCLUSIONS: PM identified patients with higher hospitalization and mortality rates compared to PCP-referred patients. But the latter showed increasing hospitalization rates over time thereby suggesting that PCPs may be able to predict future deterioration in patients with relatively good current health status. These patients may most likely benefit from preventive services like CM

    Der Medication Appropriateness Index (MAI) als ZielgrĂ¶ĂŸe fĂŒr komplexe Interventionen: erste Erfahrungen aus der PRIMUM-Pilotstudie (BMBF-Förderkennzeichen: 01GK0702)

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    Meeting Abstract : 10. Deutscher Kongress fĂŒr Versorgungsforschung, 18. GAA-Jahrestagung. Deutsches Netzwerk Versorgungsforschung e. V. ; Gesellschaft fĂŒr Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e. V. 20.-22.10.2011, Köln Hintergrund: Multimedikation als Folge von MultimorbiditĂ€t ist ein zentrales Problem der Hausarztpraxis und erhöht das Risiko fĂŒr unangemessene Arzneimittel-Verordnungen (VO). Um die Medikation bei Ă€lteren, multimorbiden Patienten zu optimieren und zu priorisieren, wurde eine computergestĂŒtzte, durch Medizinische Fachangestellte (MFA) assistierte, komplexe Intervention (checklistengestĂŒtztes VorbereitungsgesprĂ€ch sowie ÜberprĂŒfung eingenommener Medikamente durch MFA, Einsatz des web-basierten ArzneimittelinformationsDienstes AiD, spezifisches Arzt-Patienten-GesprĂ€ch) entwickelt und in einer 12-monatigen Pilotstudie auf Machbarkeit getestet. Ein auf 9 Items reduzierter MAI [1] wurde eingesetzt, um dessen Eignung als potentielles primĂ€res Outcome der Hauptstudie zu prĂŒfen. Material und Methoden: In die Pilotstudie in 20 Hausarztpraxen mit Cluster-Randomisation auf Praxisebene in Kontrollgruppe (Regelversorgung b. empfohlenem Standard) vs. Interventionsgruppe (komplexe Intervention b. empfohlenem Standard) wurden 5 Pat./Praxis eingeschlossen (≄65 Jahre, ≄3 chron. Erkrankungen, ≄5 Dauermedikamente, MMSE ≄26, Lebenserwartung ≄6 Monate). Zur Bewertung des MAI wurden an Baseline (T0), 6 Wo. (T1) & 3 Mon. (T2) nach Intervention erhoben: VO, Diagnosen, Natrium, Kalium & Kreatinin i.S., GrĂ¶ĂŸe, Gewicht, Geschlecht, Cumulative Illness Rating Scale (CIRS) [2] durch die Hausarztpraxis; Symptome fĂŒr unerwĂŒnschte Arzneimittelwirkungen im Patienten-Telefoninterview. FĂŒr den MAI wurde die Angemessenheit jeder VO in den 9 Kategorien Indikation, EffektivitĂ€t, Dosierung, korrekter & praktikabler Applikationsweg, Arzneimittelwechselwirkung, Drug-disease-Interaktion, Doppelverordnung, Anwendungsdauer 3-stufig bewertet (1 = korrekt - 3 = unkorrekt) und fĂŒr die Auswertung auf Patientenebene summiert. Die Bewertung erfolgte ohne Kenntnis der Gruppenzugehörigkeit. Deskriptive Statistiken und ReliabilitĂ€tsanalysen, ungewichtete Auswertung und Gewichtung n. Bregnhoj [3]. Ergebnisse: Es wurden N=100 Patienten in die Studie eingeschlossen, im Mittel 76 Jahre (Standardabweichung, SD 6; Range, R: 64-93) , 52% Frauen, durchschnittlich 9 VO/Pat. (SD 2; R 4-16), mittlerer CIRS-Score 10 (SD 4; R 0-23). Basierend auf N=851 VO (100 Pat.) zu T0 betrug der ReliabilitĂ€tskoeffizient (RK, Cronbachs Alpha) der ungewichteten 9 Items 0,70. Items 1-5 wiesen akzeptable TrennschĂ€rfen auf (0,52-0,64), die der Items 6, 7 & 9 fielen mit 0,21-0,29 niedriger aus, die des Item 8 betrug 0,06. Auf der Basis der 9 gewichteten Items fiel die interne Konsistenz des MAI erwartet höher aus (0,75). Die ReliabilitĂ€tsanalysen auf VO-Ebene zeigten einen RK von 0,67 (ungewichtet) vs. 0,75 (gewichtet), die TrennschĂ€rfen waren vergleichbar. Zur Zwischenauswertung betrug der MAI (T1-T0) in der Interventionsgruppe (5 Praxen, 24 Pat.) -0,9 (SD 5,6), in der Kontrollgruppe (7 Praxen, 35 Pat.) -0,5 (SD 4,9); die Differenz zwischen beiden Gruppen Mi–Mk -0,4 [95% Konfidenzintervall: -3,4;2,6]. Schlussfolgerung: Der MAI ist als potentielles primĂ€res Outcome in der Hauptstudie geeignet: wenige fehlende Werte, Darstellung von Unterschieden prĂ€-post und zwischen den Gruppen, akzeptable interne Konsistenz. Der niedrige TrennschĂ€rfekoeffizient des Items 8 weist darauf hin, dass dieses Item nicht mit dem Gesamt-Skalenwert korreliert, auch die Items 6, 7 und 9 korrelieren wesentlich schwĂ€cher mit dem Gesamt-Skalenwert als die Items 1 bis 5. Eine Wichtung z.B. der Items 2, 5, 6 und 9 könnte erwogen werden, um den Fokus der Intervention in der HauptzielgrĂ¶ĂŸe angemessen abzubilden

    Identifying factors associated with experiences of coronary heart disease patients receiving structured chronic care and counselling in European primary care

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    BACKGROUND: Primary care for chronic illness varies across European healthcare systems. In patients suffering from coronary heart disease (CHD), factors associated with patients’ experiences of receiving structured chronic care and counselling at the patient and practice level were investigated. METHODS: In an observational study comprising 140 general practices from five European countries (Austria, Germany, the Netherlands, Switzerland and the United Kingdom), 30 patients with Coronary Heart Disease (CHD) per practice were chosen at random to partake in this research. Patients were provided with a questionnaire and the Patient Assessment of Chronic Illness Care (PACIC-5A) - instrument. Practice characteristics were assessed through a practice questionnaire and face to face interviews. Data were aggregated to obtain two practice scores representing quality management and CHD care, respectively. A hierarchical multilevel analysis was performed to examine the impact of patient and practice characteristics on PACIC scores. RESULTS: The final sample included 1745 CHD-patients from 131 general practices with a mean age of 67.8 (SD 9.9) years. The overall PACIC score was 2.84 (95%CI: 2.79; 2.89) and the 5A score reflecting structured lifestyle counselling was 2.75 (95% CI: 2.69; 2.79). At the patient level, male gender, more frequent practice contact and fewer related or unrelated conditions were associated with higher PACIC scores. At the practice level, performance scores reflecting quality management (p = 0.013) and CHD care (p = 0.009) were associated with improved assessment of the structured chronic care and counselling received. CONCLUSIONS: Patients’ perceived quality of care varies. However, good practice management and organisation of care were positively reflected in patients’ assessments of receiving structured chronic illness care. This highlights the importance of integrating patient experiences into quality measurements to provide feedback to health care professionals

    Primary care practice-based care management for chronically ill patients (PraCMan): study protocol for a cluster randomized controlled trial [ISRCTN56104508]

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    Background: Care management programmes are an effective approach to care for high risk patients with complex care needs resulting from multiple co-occurring medical and non-medical conditions. These patients are likely to be hospitalized for a potentially "avoidable" cause. Nurse-led care management programmes for high risk elderly patients showed promising results. Care management programmes based on health care assistants (HCAs) targeting adult patients with a high risk of hospitalisation may be an innovative approach to deliver cost-efficient intensified care to patients most in need. Methods: PraCMan is a cluster randomized controlled trial with primary care practices as unit of randomisation. The study evaluates a complex primary care practice-based care management of patients at high risk for future hospitalizations. Eligible patients either suffer from type 2 diabetes mellitus, chronic obstructive pulmonary disease, chronic heart failure or any combination. Patients with a high likelihood of hospitalization within the following 12 months (based on insurance data) will be included in the trial. During 12 months of intervention patients of the care management group receive comprehensive assessment of medical and non-medical needs and resources as well as regular structured monitoring of symptoms. Assessment and monitoring will be performed by trained HCAs from the participating practices. Additionally, patients will receive written information, symptom diaries, action plans and a medication plan to improve self-management capabilities. This intervention is addition to usual care. Patients from the control group receive usual care. Primary outcome is the number of all-cause hospitalizations at 12 months follow-up, assessed by insurance claims data. Secondary outcomes are health-related quality of life (SF12, EQ5D), quality of chronic illness care (PACIC), health care utilisation and costs, medication adherence (MARS), depression status and severity (PHQ-9), self-management capabilities and clinical parameters. Data collection will be performed at baseline, 12 and 24 months (12 months post-intervention). Discussion: Practice-based care management for high risk individuals involving trained HCAs appears to be a promising approach to face the needs of an aging population with increasing care demands. Trial registration: Current Controlled Trials ISRCTN5610450

    The effects of midazolam on intraocular pressure in children during examination under sedation

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    Background: To obtain reliable and accurate measurements of the intraocular pressure (IOP) in children often requires sedation or anaesthesia. Therefore, we investigated the effects of oral midazolam on IOP in children. Methods: In a prospective study, IOP was measured in 72 eyes of 36 cooperative children without glaucoma requiring general anaesthesia (mean age 3.5±1.3 years, body weight ≀20 kg) by using a Perkins hand-held tonometer. Measurements of IOP were performed before, and 15 and 30 min after sedation with orally administered midazolam (1 mg/kg) given as preoperative medication, and 5 and 15 min after induction of general anaesthesia. The individual IOP courses were analysed. Results: In all of the cooperative children, IOP measurement was possible after sedation with midazolam. Mean IOP was 11.2±0.3 mmHg before sedation, 10.9±0.2 mmHg at 15 min, and 10.7±0.3 mmHg 30 min after administration of midazolam. This small decrease was not statistically significant, whilst the IOP decline at 5 and 15 min after induction of general anaesthesia was statistically significant (p<0.0001). Conclusion: Sedation with midazolam can be assumed to be an applicable, well-tolerated, safe method for IOP measurements in children

    How can clinical research improve European health outcomes in cancer?

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    We review the mechanisms by which clinical cancer research can improve health outcomes and argue that this should be central to the development of policy. Recent series of major international studies have analysed large, often nationwide, datasets for cancer patient outcomes and participation in clinical research. They have evaluated and quantified the impact of new evidence generated by randomised controlled trials on cancer survival. They show a strong and probably causal relationship between the participation in clinical research in hospitals and the outcomes for patients with the disease under study in those hospitals. Also, institutions that are active in clinical trials appear to take up well evidenced innovations more rapidly than those which are not so engaged. Further work is necessary to confirm and examine the generalisability of these findings but we argue that all of these mechanisms are likely to lead to improved outcomes for patients as a consequence of the conduct of clinical research. The size of the benefit appears to be substantial and an active programme to promote clinical research across cancer care systems should be a part of National Cancer Plans and Cancer Control Strategies
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