FOUNDED IN 1859 BY QUEEN EMMA AND KING KAMEHAMEHA IV

Office of Research and Development

Office of Research and Development
The Queen’s Medical Center
1301 Punchbowl Street, UHT 508
Honolulu, Hawaii 96813

Biostatistical Services

Introduction

The John A. Burns School of Medicine (JABSOM), University of Hawaii, and The Office of Research and Development (ORD), and The Queen’s Medical Center (QMC), see the need for coordinated and streamlined biostatistical/bioinformatical support for biomedical researchers (includes clinical, translational, basic). ORD has established a limited budget for QMC staff and affiliates to request biostatistical support through the JABSOM Office of Biostatistics & Quantitative Health Sciences (BQHS). To manage the available funds, QMC staff in need of biostatistical support can access ORD funds under the following criteria:

  1. Investigator completes ORD Form 7 (this form) except sections marked in gray.
  2. Submits ORD Form 7 to the Pre-Award Coordinator (Lori Tsue, ltsue@queens.org). The coordinator will submit Form 7 to Biostatistics Core Coordinator (Meliza Roman, biostat@hawaii.edu, 808-692-1466) with cc to investigator.
  3. The investigator initiates 1 hour consultation with BCF by contacting the Biostatistics Core Coordinator (Meliza Roman), which will be supported by UH institutional infrastructural grants (NIH U54MD007601 OLA HAWAII). This meeting allows BCF to determine estimated costs. The researcher agrees that OLA HAWAII will be acknowledged in any publication and presentation resulted from this biostatistical collaboration. Additional grant(s) may also need to be acknowledged.
  4. BCF returns ORD Form 7 to Pre-Award Coordinator with rough outline of level of support and cost estimate (cc investigator).
  5. The Director of ORD must approve the completed Form 7 for BCF support. The Post-Award Coordinator (Lori Tsue, ltsue@queens.org) will post the approved Form 7 in the ORD database (RPTS).
  6. BQHS will invoice ORD through the Post-Award Coordinator. Together with the invoice, BCF will provide the Post-Award Coordinator with a progress report. This will be used to determine whether the project is on budget. If not, the project has to be re-assessed for a change order or for an adjustment in the sampling analysis plan.
  7. Project costs may be capped depending on budget available.

Publication and Presentation Preparation

Statistical writing requires specific terminology to describe the study outcome(s) accurately, and is complementary to the clinical aspects of the study. To request biostatistical support through the ORD/BQHS collaboration, submit ORD Form 7 (download here) to:

Lori Tsue
Coordinator, Sponsored Programs
1301 Punchbowl Street, UH Tower 508
Honolulu, Hawaii 96813

Phone: 808.691.4121
Fax: 808.691.4055
Email: ltsue@queens.org

It is imperative that a research study be designed properly so that the conclusions are sound and valid. Basic study design (e.g. case-control, randomized), study objectives, and the appropriate subject population must be considered at an early stage of protocol development. The statistician depends on the researcher to provide information such as the minimum benefit that would be clinically relevant.

Sample size and power calculations to ensure that the study will have adequate power to detect both the efficacy and safety outcomes of interest. The researcher needs to provide the minimum benefit that would be clinically relevant for the respective study population and research objective.

For most investigator initiated studies, a statistical analysis plan (SAP) is a brief summarization of the statistical approaches to be used when analyzing the data. For complex studies, an additional, more detailed statistical plan can be developed. Analyses must be planned prior to the start of the a study as changes to the SAP may affect the integrity, and therefore the validity of the data.

Note that in human research any changes to the SAP may also entail accountability to funding agencies, in particular the FDA. Any changes to the SAP have to be IRB re-reviewed to ensure that the rights, safety and welfare of the participants continue to be protected.

Depending on the study design, we can provide you with the following:

  • Randomizing treatment groups – the randomization process is done by using a random number-producing algorithm (for randomized studies).
  • Selecting a random subject sample – the same algorithm can be applied to chart review type studies for the purpose of identifying subjects.
  • Perform the matching process in a matched case-control study.

Data listings, statistical tables, and figures can be developed along with analysis results. They support the written analysis results and clinical findings of the study. Data listings are usually needed for formal submissions to regulatory agencies such as the FDA, and show subject data in an organized way such as patient demographics, vital signs, medical history, adverse events, and efficacy data. Statistical tables show summary data comparisons and include descriptive statistics such as n, mean, median, std and probabilities (p-values).

It is imperative that a research study be designed properly so that the conclusions are sound and valid. Basic study design (e.g. case-control, randomized), study objectives, and the appropriate subject population must be considered at an early stage of protocol development. The statistician depends on the researcher to provide information such as the minimum benefit that would be clinically relevant.

Sample size and power calculations to ensure that the study will have adequate power to detect both the efficacy and safety outcomes of interest. The researcher needs to provide the minimum benefit that would be clinically relevant for the respective study population and research objective.

For most investigator initiated studies, a statistical analysis plan (SAP) is a brief summarization of the statistical approaches to be used when analyzing the data. For complex studies, an additional, more detailed statistical plan can be developed. Analyses must be planned prior to the start of the a study as changes to the SAP may affect the integrity, and therefore the validity of the data.

Note that in human research any changes to the SAP may also entail accountability to funding agencies, in particular the FDA. Any changes to the SAP have to be IRB re-reviewed to ensure that the rights, safety and welfare of the participants continue to be protected.

Depending on the study design, we can provide you with the following:

  • Randomizing treatment groups – the randomization process is done by using a random number-producing algorithm (for randomized studies).
  • Selecting a random subject sample – the same algorithm can be applied to chart review type studies for the purpose of identifying subjects.
  • Perform the matching process in a matched case-control study.

Data listings, statistical tables, and figures can be developed along with analysis results. They support the written analysis results and clinical findings of the study. Data listings are usually needed for formal submissions to regulatory agencies such as the FDA, and show subject data in an organized way such as patient demographics, vital signs, medical history, adverse events, and efficacy data. Statistical tables show summary data comparisons and include descriptive statistics such as n, mean, median, std and probabilities (p-values).