# High Resolution studies

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High Resolution studies
Sampling and power analysis in the High Resolution studies Pamela Minicozzi Descriptive Studies and Health Planning Unit, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan

High Resolution studies
collected detailed data from patients’ clinical records, so that the influence of non-routinely collected factors (tumour molecular characteristics, diagnostic investigations, treatment, relapse) on survival and differences in standard care could be analysed

Problem Solution In each country, the population of incident cases
for a particular cancer consists of N subjects N is large (so, rare cancers are not considered here) Since N is large, not all cases can be investigated use a representative sample to derive valid conclusions that are applicable to the entire original population Solution

Two questions What kind of probability sampling should we use?
What sample size should we use?

Sampling

Previous High Resolution studies
Samples were representative of 1-year incidence a time interval (e.g. 6 months) within the study period, provided that incidence was complete an administratively defined area covered by cancer registration

Present High Resolution studies Main types of probability sampling
We want to eliminate variations in types of sampling between countries and within a single country This implies more sophisticated sampling Main types of probability sampling

Simple random sampling
assign a unique number to each element of the study population determine the sample size randomly select the population elements using a table of random numbers a list of numbers generated randomly by a computer Advantage: auxiliary information on subjects is not required Disadvantage: - if subgroups of the population are of particular interest, they may not be included in sufficient numbers in the sample

Stratified sampling identify stratification variable(s) and determine the number of strata to be used (e.g. day and month of birth, year of diagnosis, cancer registry, etc.) divide the population into strata and determine the sample size of each stratum randomly select the population elements in each stratum Advantage: a more representative sample is obtained Disadvantage: - requires information on the proportion of the total population belonging to each stratum

Systematic sampling determine the sample size (n); thus the sampling interval “i” is n/N randomly select a number “r” from 1 to “i” select all the other subjects in the following positions: r, r+ i, r+ 2*i, etc, until the sample is exhausted Advantage: eliminate the possibility of autocorrelation Disadvantage: - only the first element is selected on a probability basis  pseudo-random sampling

many subjects do we need?
How many subjects do we need?

Hypothesis test and significance level
The main elements Statistical power Hypothesis test and significance level Previous pilot study to determine the minimum sample size required to get a significant result (or to detect a meaningful effect) the probability that the difference will be detected (e.g. 80%, 90%) the probability that a positive finding is due to chance alone (e.g. 1%, 5%) Previous pilot studies they explored whether some variables can be measured with sufficient precision (or available) and checked the study vision

Previous High Resolution studies
Number of patients was defined based on: observed differences in survival and risk of death incidence of the cancer under study difficulties in collecting clinical information available economic resources Notwithstanding that ... we were able to identify statistically significant relative excess risks of death up to 1.60 among European countries up to 1.40 among Italian areas for breast cancer for which differences in survival are small.  Applicable to other cancers for which survival differences are larger

Example for breast cancer (diagnosis 95-99)
Plot power as a function of hazard ratio for a 5% two-sided log-rank test with 80% power over sample sizes ranging from 100 and 1000 Assume 75% survival as reference (the overall survival in Europe, range: 65-90%) 45%

Example for colorectal cancer (diagnosis 95-99)
Plot power as a function of hazard ratio for a 5% two-sided log-rank test with 80% power over sample sizes ranging from 100 and 1000 Assume 50% survival as reference (the overall survival in Europe, range: 30-70%) 32%

Example for lung cancer (diagnosis 95-99)
Plot power as a function of hazard ratio for a 5% two-sided log-rank test with 80% power over sample sizes ranging from 100 and 1000 Assume 10% survival as reference (the overall survival in Europe, range: 5-20%) 30%

Present High Resolution studies
We want to analyse both differences in survival and adherence to standard care Power analysis for both logistic regression analysis (to analyse the odds of receiving one type of care (typically standard care)) and relative survival analysis (to analyse differences in relative survival and relative excess risks of death)

Conclusions Taking into account
existing samplings and power methodology experience from previous studies different coverage of Cancer Registries available economic resources We want to standardize the selection of data include a minimum number of cases that satisfies statistical considerations related to all aims of our studies Prof. JS Long1 (Regression Models for Categorical and Limited Dependent,1997) suggests that sample sizes of less than 100 cases should be avoided and that 500 observations should be adequate for almost any situation. 1Professor of Sociology and Statistics at Indiana University