Literature DB >> 7640241

Review of survival analyses published in cancer journals.

D G Altman1, B L De Stavola, S B Love, K A Stepniewska.   

Abstract

Survival analysis has found widespread applications in medicine in the last 10-15 years. However, there has been no published review of the use and presentation of survival analyses. We have carried out a systematic review of the research papers published between October and December 1991 in five clinical oncology journals. A total of 132 papers were reviewed. We looked at several aspects of study design, data handling, analysis and presentation of the results. We found that almost half of the papers did not give any summary of length of follow-up; that in 62% of papers at least one end point was not clearly defined; and that both logrank and multivariate analyses were frequently reported at most only as P-values [63/84 (75%) and 22/47 (47%) respectively]. Furthermore, although many studies were small, uncertainty of the estimates was rarely indicated [in 13/84 (15%) logrank and 16/47 (34%) multivariate results]. The procedure for categorisation of continuous variables in logrank analyses was explained in only 8/49 (16%) papers. The quality of graphs was felt to be poor in 43/117 (37%) papers which included at least one survival curve. To address some of the presentational inadequacies found in this review we include new suggested guidelines for the presentation of survival analyses in medical journals. These would complement the statistical guidelines recommended by several clinical oncology journals.

Entities:  

Mesh:

Year:  1995        PMID: 7640241      PMCID: PMC2033978          DOI: 10.1038/bjc.1995.364

Source DB:  PubMed          Journal:  Br J Cancer        ISSN: 0007-0920            Impact factor:   7.640


  59 in total

1.  Varied differentiation responses of human leukemias to bryostatin 1.

Authors:  A S Kraft; F William; G R Pettit; M B Lilly
Journal:  Cancer Res       Date:  1989-03-01       Impact factor: 12.701

2.  Effects of activators of protein kinase C, including bryostatins 1 and 2, on the growth of A549 human lung carcinoma cells.

Authors:  I L Dale; A Gescher
Journal:  Int J Cancer       Date:  1989-01-15       Impact factor: 7.396

3.  Multivariate survival analysis using Cox's regression model.

Authors:  E Christensen
Journal:  Hepatology       Date:  1987 Nov-Dec       Impact factor: 17.425

4.  Differentiation of HL-60 cells is associated with an increase in the 35-kDa protein lipocortin I.

Authors:  F William; B Mroczkowski; S Cohen; A S Kraft
Journal:  J Cell Physiol       Date:  1988-12       Impact factor: 6.384

5.  Bryostatin induces changes in protein kinase C location and activity without altering c-myc gene expression in human promyelocytic leukemia cells (HL-60).

Authors:  A S Kraft; V V Baker; W S May
Journal:  Oncogene       Date:  1987-05       Impact factor: 9.867

6.  Regulation of hematopoiesis-IV: The role of interleukin-3 and bryostatin 1 in the growth of erythropoietic progenitors from normal and anemic W/Wv mice.

Authors:  J P Leonard; W S May; J N Ihle; G R Pettit; S J Sharkis
Journal:  Blood       Date:  1988-11       Impact factor: 22.113

7.  Inhibition by bryostatin 1 of the phorbol ester-induced blockage of differentiation in hexamethylene bisacetamide-treated Friend erythroleukemia cells.

Authors:  M L Dell'Aquila; H T Nguyen; C L Herald; G R Pettit; P M Blumberg
Journal:  Cancer Res       Date:  1987-11-15       Impact factor: 12.701

8.  Mimicry of bryostatin 1 induced phosphorylation patterns in HL-60 cells by high-phorbol ester concentrations.

Authors:  B S Warren; Y Kamano; G R Pettit; P M Blumberg
Journal:  Cancer Res       Date:  1988-11-01       Impact factor: 12.701

9.  Phosphorylation of lamin B at the nuclear membrane by activated protein kinase C.

Authors:  A P Fields; G R Pettit; W S May
Journal:  J Biol Chem       Date:  1988-06-15       Impact factor: 5.157

10.  Bryostatin 1 activates protein kinase C and induces monocytic differentiation of HL-60 cells.

Authors:  R M Stone; E Sariban; G R Pettit; D W Kufe
Journal:  Blood       Date:  1988-07       Impact factor: 22.113

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  100 in total

Review 1.  Calculating the number needed to treat for trials where the outcome is time to an event.

Authors:  D G Altman; P K Andersen
Journal:  BMJ       Date:  1999-12-04

2.  Numbers needed to treat derived from meta-analysis. Are an absurdity.

Authors:  B G Charlton
Journal:  BMJ       Date:  1999-10-30

3.  Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration.

Authors:  Douglas G Altman; Lisa M McShane; Willi Sauerbrei; Sheila E Taube
Journal:  BMC Med       Date:  2012-05-29       Impact factor: 8.775

4.  Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): explanation and elaboration.

Authors:  Douglas G Altman; Lisa M McShane; Willi Sauerbrei; Sheila E Taube
Journal:  PLoS Med       Date:  2012-05-29       Impact factor: 11.069

5.  Laparoscopic-assisted abdominoperineal resection for low rectal cancer provides a shorter length of hospital stay while not affecting the recurrence or survival: a propensity score-matched analysis.

Authors:  Manfred Odermatt; Karen Flashman; Jim Khan; Amjad Parvaiz
Journal:  Surg Today       Date:  2015-09-05       Impact factor: 2.549

6.  CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials.

Authors:  David Moher; Sally Hopewell; Kenneth F Schulz; Victor Montori; Peter C Gøtzsche; P J Devereaux; Diana Elbourne; Matthias Egger; Douglas G Altman
Journal:  BMJ       Date:  2010-03-23

7.  Efficacy of prophylactic irradiation to the contralateral testis for patients with advanced-stage primary testicular lymphoma: an analysis of outcomes at a single institution.

Authors:  Ryouji Tokiya; Eisaku Yoden; Kei Konishi; Nobuhiko Kamitani; Junichi Hiratsuka; Risa Koresawa; Tadashi Hirose; Fuminori Sano; Hirotoshi Tokunaga; Toshinori Kondo; Hideho Wada; Takashi Sugihara
Journal:  Int J Hematol       Date:  2017-06-13       Impact factor: 2.490

8.  WebDISCO: a web service for distributed cox model learning without patient-level data sharing.

Authors:  Chia-Lun Lu; Shuang Wang; Zhanglong Ji; Yuan Wu; Li Xiong; Xiaoqian Jiang; Lucila Ohno-Machado
Journal:  J Am Med Inform Assoc       Date:  2015-07-09       Impact factor: 4.497

9.  Immortal time bias in pharmacoepidemiological studies on cancer patient survival: empirical illustration for beta-blocker use in four cancers with different prognosis.

Authors:  Janick Weberpals; Lina Jansen; Myrthe P P van Herk-Sukel; Josephina G Kuiper; Mieke J Aarts; Pauline A J Vissers; Hermann Brenner
Journal:  Eur J Epidemiol       Date:  2017-09-01       Impact factor: 8.082

10.  Temporal trends of postinjury multiple-organ failure: still resource intensive, morbid, and lethal.

Authors:  Angela Sauaia; Ernest E Moore; Jeffrey L Johnson; Theresa L Chin; Anirban Banerjee; Jason L Sperry; Ronald V Maier; C Cothren Burlew
Journal:  J Trauma Acute Care Surg       Date:  2014-03       Impact factor: 3.313

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