Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine

Background: Although subgroup analyses in clinical trials may provide evidence for individualised medicine, their conduct and interpretation remain controversial. Methods: Subgroup effect can be defined as the difference in treatment effect across patient subgroups. Cumulative subgroup analysis refers to a series of repeated pooling of subgroup effects after adding data from each of related trials chronologically, to investigate the accumulating evidence for subgroup effects. We illustrated the clinical relevance of cumulative subgroup analysis in two case studies using data from published individual patient data (IPD) meta-analyses. Computer simulations were also conducted to examine the statistical properties of cumulative subgroup analysis. Results: In case study 1, an IPD meta-analysis of 10 randomised trials (RCTs) on beta blockers for heart failure reported significant interaction of treatment effects with baseline rhythm. Cumulative subgroup analysis could have detected the subgroup effect 15 years earlier, with five fewer trials and 71% less patients, than the IPD meta-analysis which first reported it. Case study 2 involved an IPD meta-analysis of 11 RCTs on treatments for pulmonary arterial hypertension that reported significant subgroup effect by aetiology. Cumulative subgroup analysis could have detected the subgroup effect 6 years earlier, with three fewer trials and 40% less patients than the IPD meta-analysis. Computer simulations have indicated that cumulative subgroup analysis increases the statistical power and is not associated with inflated false positives. Conclusions: To reduce waste of research data, subgroup analyses in clinical trials should be more widely conducted and adequately reported so that cumulative subgroup analyses could be timely performed to inform clinical practice and further research

The use of interim data and Data Monitoring Committee recommendations in randomized controlled trial reports: frequency, implications and potential sources of bias

Background: Interim analysis of accumulating trial data is important to protect participant safety during randomized controlled trials (RCTs). Data Monitoring Committees (DMCs) often undertake such analyses, but their widening role may lead to extended use of interim analysis or recommendations that could potentially bias trial results.Methods: Systematic search of eight major publications: Annals of Internal Medicine, BMJ, Circulation, CID, JAMA, JCO, Lancet and NEJM, including all randomised controlled trials ( RCTs) between June 2000 and May 2005 to identify RCTs that reported use of interim analysis, with or without DMC involvement. Recommendations made by the DMC or based on interim analysis were identified and potential sources of bias assessed. Independent double data extraction was performed on all included trials.Results: We identified 1772 RCTs, of which 470 (27%; 470/1772) reported the use of a DMC and a further 116 (7%; 116/1772) trials reported some form of interim analysis without explicit mention of a DMC. There were 28 trials ( 24 with a formal DMC), randomizing a total of 79396 participants, identified as recommending changes to the trial that may have lead to biased results. In most of these, some form of sample size re-estimation was recommended with four trials also reporting changes to trial endpoints. The review relied on information reported in the primary publications and methods papers relating to the trials, higher rates of use may have occurred but not been reported.Conclusion: The reported use of interim analysis and DMCs in clinical trials has been increasing in recent years. It is reassuring that in most cases recommendations were made in the interest of participant safety. However, in practice, recommendations that may lead to potentially biased trial results are being made

Controversies in the Use of Beta Blockers in Heart Failure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94510/1/j.1527-5299.2003.00294.x.pd

DESAIN GAYA HIP HOP DAN ELEGAN DALAM IDENTITY PARAMUDA

Kerja praktek merupakan suatu tugas yang harus di tempuh oleh setiap mahasiswa sebagai syarat perkuliahan, praktikum, dan Tugas Akhir/ Skripsi (S1) Dan Tugas Akhir (D3) dalam rangka pengembangan pengetahuan mahasiswa. Mata kuliah ini memiliki bobot 2 (dua) SKS. Selama masa kerja praktek mahasiswa mengikuti jadwal kerja yang telah ditetapkan perusahaan dan disesuaikan dengan jadwal kuliah. Jumlah jam kerja yang ditetapkan juga tergantung dari kebijaksanaan di perusahaan. Kerja Praktek ini di lakukan oleh setiap mahasiswa, dan mahasiswa tersebut melakukan prakteknya di sebuah Perusahaan/Instansi atau Lembaga. Selama melakukan Kerja Praktek mahasiswa di tuntut untuk mengerjakan suatu tugas sesuai dengan jurusannya dan biasanya Kerja Praktek tersebut di lakukan kurang lebih 2 bulan lamanya. Dengan melakukan Kerja Praktek ini, kita bisa mendapatkan pengalaman yang dalam perkuliahan tidak bisa didapatkan, contohnya dalam melakukan pekerjaannya kita bisa bertemu dengan kliennya langsung, kita bisa merencanakan pekerjaan yang kita dapat kemudian kita rapatkan pekerjaan itu bersama – sama agar bisa dilaksanakan sesuai dengan rencana kita

Modern goat farm management

10.00; notes of short course run in conjunction with Duchy Agric. Coll., Stoke Climsland (GB) 2-4 Apr 1986; tutors Mottram, T.; Helliwell, SAvailable from British Library Document Supply Centre- DSC:87/17634(Modern) / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

IMPLEMENTASI ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) DALAM PREDIKSI KEBUTUHAN LISTRIK JANGKA PANJANG DI JAWA TIMUR

In system electricity, strategy predictions will need electricity very needed for anticipate need energy electricity in Indonesia, especially in East Java. Because East Java is one of them province with economy and growth high population facing challenge big in fulfil need the electricity in a way sustainable. Therefore, it is necessary predictions accurate for determine need electricity in distribution electricity period long that is annual. Prediction burden electricity in the area East Java uses Adaptive Neuro Fuzzy Inference System (ANFIS) method. Method ANFIS research through stages that is studies literature, collection, data processing, data simulated in Matlab with input training and testing data, ANFIS training, ANFIS testing, and analysis results and conclusions. In this prediction, 2 scenarios were carried out. Research result shows: 1) Scenario 1 has 3 inputs, including quantity population(t), GRDP (t), and burden electricity (t), as well as 1 target output, namely burden electricity (t+1). Scenario 2 has 4 inputs, including amount population (t), GRDP (t), burden electricity (t-1), and load electricity (t), as well as 1 target output, namely (t+1). This research uses a Generalized Bell type membership function with 3 membership functions for each input data with the number of epochs is 100 times; 2) From 2 scenarios the produces the highest error in scenario 1 with The MAPE value is 5.6349444%, and if MAPE < 10% then prediction or forecasting very accurate. Scenario with lowest error value generated by scenario 2 with The MAPE value is 2.3001713%, and if MAPE < 10% then prediction or forecasting very accurate. So that predictions with scenario 2 more accurate from scenario 1 and if the more Lots number of inputs then predictions the more accurate. Keywords— Energy, Electricity, ANFIS, MAPE